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2025-05-22
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south-forecast.2025-05-22.0.log

south-forecast.2025-05-22.0.log 169 KB
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
  1. 2025-05-22 11:00:08,282 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    2. 

  2.  - _tfmw_add_deprecation_warning
    3. 

  3. 2025-05-22 11:00:08,613 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    4. 

  4. 2025-05-22 11:00:11,212 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    5. 

  5.  - _tfmw_add_deprecation_warning
    6. 

  6. 2025-05-22 11:00:14,107 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    7. 

  7.  - _tfmw_add_deprecation_warning
    8. 

  8. 2025-05-22 11:00:14,110 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    9. 

  9.  - _tfmw_add_deprecation_warning
    10. 

  10. 2025-05-22 11:00:14,392 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources
    11. 

  11. 2025-05-22 11:00:14,401 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
    12. 

  12. 2025-05-22 11:00:14,415 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    13. 

  13. 2025-05-22 11:00:14,419 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    14. 

  14. 2025-05-22 11:00:14,423 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
    15. 

  15. 2025-05-22 11:00:14,427 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
    16. 

  16. 2025-05-22 11:00:14,463 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    17. 

  17. 2025-05-22 11:00:14,463 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    18. 

  18. 2025-05-22 11:00:14,463 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    19. 

  19. 2025-05-22 11:00:14,463 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    20. 

  20. 2025-05-22 11:00:14,464 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    21. 

  21. 2025-05-22 11:00:14,464 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    22. 

  22. 2025-05-22 11:00:15,484 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    23. 

  23. 2025-05-22 11:00:15,484 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    24. 

  24. 2025-05-22 11:00:15,485 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    25. 

  25. 2025-05-22 11:00:15,485 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    26. 

  26. 2025-05-22 11:00:28,458 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    27. 

  27. 2025-05-22 11:00:28,458 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    28. 

  28. 2025-05-22 11:00:28,458 - tf_lstm.py - INFO - 训练集损失函数为：[9.0069e-01 3.1528e-01 9.7720e-02 2.6780e-02 6.4900e-03 1.4300e-03
    29. 

  29.  3.2000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05
    30. 

  30.  6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05
    31. 

  31.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    32. 

  32.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    33. 

  33.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    34. 

  34.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    35. 

  35.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    36. 

  36.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    37. 

  37.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    38. 

  38.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    39. 

  39.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    40. 

  40.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    41. 

  41.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    42. 

  42.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    43. 

  43.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    44. 

  44.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training
    45. 

  45. 2025-05-22 11:00:28,458 - tf_lstm.py - INFO - 训练集损失函数为：[9.0124e-01 3.1763e-01 9.9520e-02 2.7860e-02 7.1800e-03 1.9800e-03
    46. 

  46.  8.2000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04
    47. 

  47.  5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    48. 

  48.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    49. 

  49.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    50. 

  50.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    51. 

  51.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    52. 

  52.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    53. 

  53.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    54. 

  54.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    55. 

  55.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    56. 

  56.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    57. 

  57.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    58. 

  58.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04
    59. 

  59.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    60. 

  60.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    61. 

  61.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training
    62. 

  62. 2025-05-22 11:00:28,459 - tf_lstm.py - INFO - 验证集损失函数为：[5.0957e-01 1.6535e-01 4.7580e-02 1.2060e-02 2.7500e-03 6.1000e-04
    63. 

  63.  1.7000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    64. 

  64.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    65. 

  65.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    66. 

  66.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    67. 

  67.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    68. 

  68.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    69. 

  69.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    70. 

  70.  8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    71. 

  71.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    72. 

  72.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    73. 

  73.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    74. 

  74.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    75. 

  75.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    76. 

  76.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    77. 

  77.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    78. 

  78.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
    79. 

  79. 2025-05-22 11:00:28,459 - tf_lstm.py - INFO - 验证集损失函数为：[0.51216 0.16791 0.0493  0.01325 0.00369 0.00147 0.00101 0.00092 0.0009
    80. 

  80.  0.00089 0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087
    81. 

  81.  0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085
    82. 

  82.  0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084
    83. 

  83.  0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084
    84. 

  84.  0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    85. 

  85.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    86. 

  86.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082
    87. 

  87.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    88. 

  88.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    89. 

  89.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    90. 

  90.  0.00082] - training
    91. 

  91. 2025-05-22 11:00:28,518 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682e934cf784a6b4a7b99c0b - insert_trained_model_into_mongo
    92. 

  92. 2025-05-22 11:00:28,528 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682e934c6e3c6f67919b8a61 - insert_trained_model_into_mongo
    93. 

  93. 2025-05-22 11:00:28,553 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682e934c6e3c6f67919b8a63 - insert_scaler_model_into_mongo
    94. 

  94. 2025-05-22 11:00:28,559 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682e934cf784a6b4a7b99c0d - insert_scaler_model_into_mongo
    95. 

  95. 2025-05-22 11:00:29,840 - task_worker.py - ERROR - Area -99 failed: 'NoneType' object has no attribute 'area_id' - region_task
    96. 

  96. 2025-05-22 11:07:40,679 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    97. 

  97.  - _tfmw_add_deprecation_warning
    98. 

  98. 2025-05-22 11:07:44,165 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    99. 

  99. 2025-05-22 11:08:09,067 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    100. 

  100.  - _tfmw_add_deprecation_warning
    101. 

  101. 2025-05-22 11:13:00,030 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    102. 

  102.  - _tfmw_add_deprecation_warning
    103. 

  103. 2025-05-22 11:13:00,144 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    104. 

  104. 2025-05-22 11:13:02,718 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    105. 

  105.  - _tfmw_add_deprecation_warning
    106. 

  106. 2025-05-22 11:13:05,631 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    107. 

  107.  - _tfmw_add_deprecation_warning
    108. 

  108. 2025-05-22 11:13:05,632 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    109. 

  109.  - _tfmw_add_deprecation_warning
    110. 

  110. 2025-05-22 11:13:05,855 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
    111. 

  111. 2025-05-22 11:13:05,855 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources
    112. 

  112. 2025-05-22 11:13:05,873 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    113. 

  113. 2025-05-22 11:13:05,873 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    114. 

  114. 2025-05-22 11:13:05,881 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
    115. 

  115. 2025-05-22 11:13:05,881 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
    116. 

  116. 2025-05-22 11:13:05,909 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    117. 

  117. 2025-05-22 11:13:05,909 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    118. 

  118. 2025-05-22 11:13:05,909 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    119. 

  119. 2025-05-22 11:13:05,909 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    120. 

  120. 2025-05-22 11:13:05,909 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    121. 

  121. 2025-05-22 11:13:05,910 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    122. 

  122. 2025-05-22 11:13:06,849 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    123. 

  123. 2025-05-22 11:13:06,850 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    124. 

  124. 2025-05-22 11:13:06,872 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    125. 

  125. 2025-05-22 11:13:06,872 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    126. 

  126. 2025-05-22 11:13:19,649 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    127. 

  127. 2025-05-22 11:13:19,649 - tf_lstm.py - INFO - 训练集损失函数为：[9.0405e-01 3.1641e-01 9.7950e-02 2.6820e-02 6.5000e-03 1.4300e-03
    128. 

  128.  3.2000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05
    129. 

  129.  6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05
    130. 

  130.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    131. 

  131.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    132. 

  132.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    133. 

  133.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    134. 

  134.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    135. 

  135.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    136. 

  136.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    137. 

  137.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    138. 

  138.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    139. 

  139.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    140. 

  140.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    141. 

  141.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    142. 

  142.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    143. 

  143.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training
    144. 

  144. 2025-05-22 11:13:19,649 - tf_lstm.py - INFO - 验证集损失函数为：[5.1157e-01 1.6582e-01 4.7650e-02 1.2070e-02 2.7500e-03 6.1000e-04
    145. 

  145.  1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    146. 

  146.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    147. 

  147.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    148. 

  148.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    149. 

  149.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    150. 

  150.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    151. 

  151.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    152. 

  152.  8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    153. 

  153.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    154. 

  154.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    155. 

  155.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    156. 

  156.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    157. 

  157.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    158. 

  158.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    159. 

  159.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    160. 

  160.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
    161. 

  161. 2025-05-22 11:13:19,659 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    162. 

  162. 2025-05-22 11:13:19,660 - tf_lstm.py - INFO - 训练集损失函数为：[9.0396e-01 3.1831e-01 9.9550e-02 2.7800e-02 7.1400e-03 1.9600e-03
    163. 

  163.  8.2000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04
    164. 

  164.  5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    165. 

  165.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    166. 

  166.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    167. 

  167.  5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    168. 

  168.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    169. 

  169.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    170. 

  170.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    171. 

  171.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    172. 

  172.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    173. 

  173.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    174. 

  174.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    175. 

  175.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04
    176. 

  176.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    177. 

  177.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    178. 

  178.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training
    179. 

  179. 2025-05-22 11:13:19,660 - tf_lstm.py - INFO - 验证集损失函数为：[0.51355 0.16811 0.04924 0.01319 0.00367 0.00146 0.00101 0.00092 0.0009
    180. 

  180.  0.00089 0.00089 0.00088 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087
    181. 

  181.  0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085
    182. 

  182.  0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084
    183. 

  183.  0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084
    184. 

  184.  0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    185. 

  185.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    186. 

  186.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082
    187. 

  187.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    188. 

  188.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    189. 

  189.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    190. 

  190.  0.00082] - training
    191. 

  191. 2025-05-22 11:13:19,705 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682e964ff0d6786d32a727ec - insert_trained_model_into_mongo
    192. 

  192. 2025-05-22 11:13:19,709 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682e964f3e4b222ab549997c - insert_trained_model_into_mongo
    193. 

  193. 2025-05-22 11:13:19,728 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682e964ff0d6786d32a727ee - insert_scaler_model_into_mongo
    194. 

  194. 2025-05-22 11:13:19,733 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682e964f3e4b222ab549997e - insert_scaler_model_into_mongo
    195. 

  195. 2025-05-22 11:13:21,022 - task_worker.py - ERROR - Area -99 failed: 'NoneType' object has no attribute 'area_id' - region_task
    196. 

  196. 2025-05-22 13:06:09,682 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    197. 

  197.  - _tfmw_add_deprecation_warning
    198. 

  198. 2025-05-22 13:06:09,939 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    199. 

  199. 2025-05-22 13:06:12,715 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    200. 

  200.  - _tfmw_add_deprecation_warning
    201. 

  201. 2025-05-22 13:06:15,661 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    202. 

  202.  - _tfmw_add_deprecation_warning
    203. 

  203. 2025-05-22 13:06:15,662 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    204. 

  204.  - _tfmw_add_deprecation_warning
    205. 

  205. 2025-05-22 13:06:15,932 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
    206. 

  206. 2025-05-22 13:06:15,932 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources
    207. 

  207. 2025-05-22 13:06:15,959 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    208. 

  208. 2025-05-22 13:06:15,959 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    209. 

  209. 2025-05-22 13:06:15,967 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
    210. 

  210. 2025-05-22 13:06:15,968 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
    211. 

  211. 2025-05-22 13:06:16,004 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    212. 

  212. 2025-05-22 13:06:16,004 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    213. 

  213. 2025-05-22 13:06:16,005 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    214. 

  214. 2025-05-22 13:06:16,005 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    215. 

  215. 2025-05-22 13:06:16,005 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    216. 

  216. 2025-05-22 13:06:16,005 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    217. 

  217. 2025-05-22 13:06:17,007 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    218. 

  218. 2025-05-22 13:06:17,007 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    219. 

  219. 2025-05-22 13:06:17,007 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    220. 

  220. 2025-05-22 13:06:17,007 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    221. 

  221. 2025-05-22 13:06:29,989 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    222. 

  222. 2025-05-22 13:06:29,990 - tf_lstm.py - INFO - 训练集损失函数为：[9.0225e-01 3.1749e-01 9.9030e-02 2.7310e-02 6.6500e-03 1.4700e-03
    223. 

  223.  3.3000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05
    224. 

  224.  6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    225. 

  225.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    226. 

  226.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    227. 

  227.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    228. 

  228.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    229. 

  229.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    230. 

  230.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    231. 

  231.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    232. 

  232.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    233. 

  233.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    234. 

  234.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    235. 

  235.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    236. 

  236.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    237. 

  237.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    238. 

  238.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training
    239. 

  239. 2025-05-22 13:06:29,990 - tf_lstm.py - INFO - 验证集损失函数为：[0.50924 0.16612 0.04859 0.013   0.00362 0.00146 0.00101 0.00092 0.0009
    240. 

  240.  0.00089 0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087
    241. 

  241.  0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085
    242. 

  242.  0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084
    243. 

  243.  0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084
    244. 

  244.  0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    245. 

  245.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    246. 

  246.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082
    247. 

  247.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    248. 

  248.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    249. 

  249.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    250. 

  250.  0.00082] - training
    251. 

  251. 2025-05-22 13:06:29,990 - tf_lstm.py - INFO - 验证集损失函数为：[5.1195e-01 1.6715e-01 4.8400e-02 1.2340e-02 2.8200e-03 6.2000e-04
    252. 

  252.  1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    253. 

  253.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    254. 

  254.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    255. 

  255.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    256. 

  256.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    257. 

  257.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    258. 

  258.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    259. 

  259.  8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    260. 

  260.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    261. 

  261.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    262. 

  262.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    263. 

  263.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    264. 

  264.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    265. 

  265.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    266. 

  266.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    267. 

  267.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
    268. 

  268. 2025-05-22 13:06:30,043 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eb0d6741733b69380e30e - insert_trained_model_into_mongo
    269. 

  269. 2025-05-22 13:06:30,044 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eb0d63e5e1c931e47042e - insert_trained_model_into_mongo
    270. 

  270. 2025-05-22 13:06:30,052 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eb0d6741733b69380e310 - insert_scaler_model_into_mongo
    271. 

  271. 2025-05-22 13:06:30,052 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eb0d63e5e1c931e470430 - insert_scaler_model_into_mongo
    272. 

  272. 2025-05-22 13:06:31,384 - task_worker.py - ERROR - Area 1002 failed: 'Datetime' - region_task
    273. 

  273. 2025-05-22 13:21:28,302 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    274. 

  274.  - _tfmw_add_deprecation_warning
    275. 

  275. 2025-05-22 13:21:32,961 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    276. 

  276. 2025-05-22 13:21:55,981 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    277. 

  277.  - _tfmw_add_deprecation_warning
    278. 

  278. 2025-05-22 13:22:19,846 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    279. 

  279.  - _tfmw_add_deprecation_warning
    280. 

  280. 2025-05-22 13:22:19,965 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    281. 

  281.  - _tfmw_add_deprecation_warning
    282. 

  282. 2025-05-22 13:22:32,298 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
    283. 

  283. 2025-05-22 13:22:32,299 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources
    284. 

  284. 2025-05-22 13:22:49,121 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    285. 

  285. 2025-05-22 13:22:49,123 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    286. 

  286. 2025-05-22 13:22:59,511 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
    287. 

  287. 2025-05-22 13:22:59,519 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
    288. 

  288. 2025-05-22 13:22:59,558 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    289. 

  289. 2025-05-22 13:22:59,558 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    290. 

  290. 2025-05-22 13:22:59,560 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    291. 

  291. 2025-05-22 13:22:59,565 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    292. 

  292. 2025-05-22 13:22:59,565 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    293. 

  293. 2025-05-22 13:22:59,567 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    294. 

  294. 2025-05-22 13:23:14,175 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    295. 

  295. 2025-05-22 13:23:14,175 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    296. 

  296. 2025-05-22 13:23:14,176 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    297. 

  297. 2025-05-22 13:23:14,177 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    298. 

  298. 2025-05-22 13:23:52,897 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    299. 

  299. 2025-05-22 13:23:52,897 - tf_lstm.py - INFO - 训练集损失函数为：[9.0598e-01 3.1883e-01 9.9640e-02 2.7810e-02 7.1500e-03 1.9700e-03
    300. 

  300.  8.2000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04
    301. 

  301.  5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    302. 

  302.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    303. 

  303.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    304. 

  304.  5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    305. 

  305.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    306. 

  306.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    307. 

  307.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    308. 

  308.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    309. 

  309.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    310. 

  310.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    311. 

  311.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    312. 

  312.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04
    313. 

  313.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    314. 

  314.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    315. 

  315.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training
    316. 

  316. 2025-05-22 13:23:52,898 - tf_lstm.py - INFO - 验证集损失函数为：[0.51461 0.16825 0.04928 0.0132  0.00367 0.00147 0.00101 0.00092 0.0009
    317. 

  317.  0.00089 0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087
    318. 

  318.  0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085
    319. 

  319.  0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084
    320. 

  320.  0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084
    321. 

  321.  0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    322. 

  322.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    323. 

  323.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082
    324. 

  324.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    325. 

  325.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    326. 

  326.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    327. 

  327.  0.00082] - training
    328. 

  328. 2025-05-22 13:23:52,944 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    329. 

  329. 2025-05-22 13:23:52,945 - tf_lstm.py - INFO - 训练集损失函数为：[9.0190e-01 3.1699e-01 9.8690e-02 2.7170e-02 6.6100e-03 1.4600e-03
    330. 

  330.  3.3000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05
    331. 

  331.  6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05
    332. 

  332.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    333. 

  333.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    334. 

  334.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    335. 

  335.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    336. 

  336.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    337. 

  337.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    338. 

  338.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    339. 

  339.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    340. 

  340.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    341. 

  341.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    342. 

  342.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    343. 

  343.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    344. 

  344.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    345. 

  345.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training
    346. 

  346. 2025-05-22 13:23:52,945 - tf_lstm.py - INFO - 验证集损失函数为：[5.1143e-01 1.6671e-01 4.8180e-02 1.2270e-02 2.8100e-03 6.3000e-04
    347. 

  347.  1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    348. 

  348.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    349. 

  349.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    350. 

  350.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    351. 

  351.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    352. 

  352.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    353. 

  353.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    354. 

  354.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05
    355. 

  355.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    356. 

  356.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    357. 

  357.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    358. 

  358.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    359. 

  359.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    360. 

  360.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    361. 

  361.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    362. 

  362.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
    363. 

  363. 2025-05-22 13:23:58,957 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eb4ee215b380c4e277f3d - insert_trained_model_into_mongo
    364. 

  364. 2025-05-22 13:23:58,962 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eb4eef0381e0011af450d - insert_trained_model_into_mongo
    365. 

  365. 2025-05-22 13:23:58,970 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eb4ee215b380c4e277f3f - insert_scaler_model_into_mongo
    366. 

  366. 2025-05-22 13:23:58,987 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eb4eef0381e0011af450f - insert_scaler_model_into_mongo
    367. 

  367. 2025-05-22 13:25:06,867 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    368. 

  368.  - _tfmw_add_deprecation_warning
    369. 

  369. 2025-05-22 13:25:10,017 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    370. 

  370. 2025-05-22 13:25:33,151 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    371. 

  371.  - _tfmw_add_deprecation_warning
    372. 

  372. 2025-05-22 13:25:56,908 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    373. 

  373.  - _tfmw_add_deprecation_warning
    374. 

  374. 2025-05-22 13:25:56,915 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    375. 

  375.  - _tfmw_add_deprecation_warning
    376. 

  376. 2025-05-22 13:31:51,198 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    377. 

  377.  - _tfmw_add_deprecation_warning
    378. 

  378. 2025-05-22 13:31:51,314 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    379. 

  379. 2025-05-22 13:31:53,876 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    380. 

  380.  - _tfmw_add_deprecation_warning
    381. 

  381. 2025-05-22 13:31:56,769 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    382. 

  382.  - _tfmw_add_deprecation_warning
    383. 

  383. 2025-05-22 13:31:56,769 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    384. 

  384.  - _tfmw_add_deprecation_warning
    385. 

  385. 2025-05-22 13:31:56,973 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources
    386. 

  386. 2025-05-22 13:31:56,974 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
    387. 

  387. 2025-05-22 13:31:56,992 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    388. 

  388. 2025-05-22 13:31:56,993 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    389. 

  389. 2025-05-22 13:31:57,000 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
    390. 

  390. 2025-05-22 13:31:57,001 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
    391. 

  391. 2025-05-22 13:31:57,027 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    392. 

  392. 2025-05-22 13:31:57,027 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    393. 

  393. 2025-05-22 13:31:57,028 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    394. 

  394. 2025-05-22 13:31:57,028 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    395. 

  395. 2025-05-22 13:31:57,029 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    396. 

  396. 2025-05-22 13:31:57,029 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    397. 

  397. 2025-05-22 13:31:57,948 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    398. 

  398. 2025-05-22 13:31:57,949 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    399. 

  399. 2025-05-22 13:31:57,957 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    400. 

  400. 2025-05-22 13:31:57,958 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    401. 

  401. 2025-05-22 13:32:10,777 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    402. 

  402. 2025-05-22 13:32:10,778 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    403. 

  403. 2025-05-22 13:32:10,778 - tf_lstm.py - INFO - 训练集损失函数为：[8.9570e-01 3.1407e-01 9.7760e-02 2.7200e-02 6.9900e-03 1.9300e-03
    404. 

  404.  8.1000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04
    405. 

  405.  5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    406. 

  406.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    407. 

  407.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    408. 

  408.  5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    409. 

  409.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    410. 

  410.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    411. 

  411.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    412. 

  412.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    413. 

  413.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    414. 

  414.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    415. 

  415.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    416. 

  416.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    417. 

  417.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    418. 

  418.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    419. 

  419.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training
    420. 

  420. 2025-05-22 13:32:10,778 - tf_lstm.py - INFO - 训练集损失函数为：[9.0424e-01 3.1647e-01 9.8080e-02 2.6880e-02 6.5100e-03 1.4300e-03
    421. 

  421.  3.2000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05
    422. 

  422.  6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05
    423. 

  423.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    424. 

  424.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    425. 

  425.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    426. 

  426.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    427. 

  427.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    428. 

  428.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    429. 

  429.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    430. 

  430.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    431. 

  431.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    432. 

  432.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    433. 

  433.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    434. 

  434.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    435. 

  435.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    436. 

  436.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training
    437. 

  437. 2025-05-22 13:32:10,778 - tf_lstm.py - INFO - 验证集损失函数为：[0.5076  0.16537 0.04823 0.01291 0.0036  0.00145 0.00101 0.00092 0.0009
    438. 

  438.  0.00089 0.00089 0.00088 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087
    439. 

  439.  0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085
    440. 

  440.  0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084
    441. 

  441.  0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084
    442. 

  442.  0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083
    443. 

  443.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    444. 

  444.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    445. 

  445.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    446. 

  446.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    447. 

  447.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    448. 

  448.  0.00082] - training
    449. 

  449. 2025-05-22 13:32:10,778 - tf_lstm.py - INFO - 验证集损失函数为：[5.1157e-01 1.6595e-01 4.7750e-02 1.2100e-02 2.7500e-03 6.1000e-04
    450. 

  450.  1.7000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    451. 

  451.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    452. 

  452.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    453. 

  453.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    454. 

  454.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    455. 

  455.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    456. 

  456.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    457. 

  457.  8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    458. 

  458.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    459. 

  459.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    460. 

  460.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    461. 

  461.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    462. 

  462.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    463. 

  463.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    464. 

  464.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    465. 

  465.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
    466. 

  466. 2025-05-22 13:32:10,816 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eb6daed827ab3f0037eb2 - insert_trained_model_into_mongo
    467. 

  467. 2025-05-22 13:32:10,816 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eb6dad7c91141fbffba57 - insert_trained_model_into_mongo
    468. 

  468. 2025-05-22 13:32:10,848 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eb6dad7c91141fbffba59 - insert_scaler_model_into_mongo
    469. 

  469. 2025-05-22 13:32:10,863 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eb6daed827ab3f0037eb4 - insert_scaler_model_into_mongo
    470. 

  470. 2025-05-22 13:32:12,164 - task_worker.py - ERROR - Area 1002 failed: 'Datetime' - region_task
    471. 

  471. 2025-05-22 13:36:17,718 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    472. 

  472.  - _tfmw_add_deprecation_warning
    473. 

  473. 2025-05-22 13:36:17,835 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    474. 

  474. 2025-05-22 13:36:20,536 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    475. 

  475.  - _tfmw_add_deprecation_warning
    476. 

  476. 2025-05-22 13:36:23,520 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    477. 

  477.  - _tfmw_add_deprecation_warning
    478. 

  478. 2025-05-22 13:36:23,524 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    479. 

  479.  - _tfmw_add_deprecation_warning
    480. 

  480. 2025-05-22 13:36:23,730 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources
    481. 

  481. 2025-05-22 13:36:23,730 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
    482. 

  482. 2025-05-22 13:36:23,748 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    483. 

  483. 2025-05-22 13:36:23,748 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    484. 

  484. 2025-05-22 13:36:23,757 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
    485. 

  485. 2025-05-22 13:36:23,757 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
    486. 

  486. 2025-05-22 13:36:23,784 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    487. 

  487. 2025-05-22 13:36:23,784 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    488. 

  488. 2025-05-22 13:36:23,785 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    489. 

  489. 2025-05-22 13:36:23,786 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    490. 

  490. 2025-05-22 13:36:23,786 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    491. 

  491. 2025-05-22 13:36:23,787 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    492. 

  492. 2025-05-22 13:36:24,750 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    493. 

  493. 2025-05-22 13:36:24,751 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    494. 

  494. 2025-05-22 13:36:24,755 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    495. 

  495. 2025-05-22 13:36:24,755 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    496. 

  496. 2025-05-22 13:36:37,674 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    497. 

  497. 2025-05-22 13:36:37,674 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    498. 

  498. 2025-05-22 13:36:37,675 - tf_lstm.py - INFO - 训练集损失函数为：[9.0215e-01 3.1682e-01 9.8480e-02 2.7050e-02 6.5600e-03 1.4400e-03
    499. 

  499.  3.2000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05
    500. 

  500.  6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    501. 

  501.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    502. 

  502.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    503. 

  503.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    504. 

  504.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    505. 

  505.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    506. 

  506.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    507. 

  507.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    508. 

  508.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    509. 

  509.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    510. 

  510.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    511. 

  511.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    512. 

  512.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    513. 

  513.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    514. 

  514.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training
    515. 

  515. 2025-05-22 13:36:37,675 - tf_lstm.py - INFO - 验证集损失函数为：[0.51142 0.16709 0.04888 0.01311 0.00366 0.00147 0.00102 0.00093 0.00091
    516. 

  516.  0.0009  0.0009  0.00089 0.00089 0.00088 0.00088 0.00088 0.00088 0.00087
    517. 

  517.  0.00087 0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086
    518. 

  518.  0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085
    519. 

  519.  0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084
    520. 

  520.  0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    521. 

  521.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    522. 

  522.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082 0.00082
    523. 

  523.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    524. 

  524.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    525. 

  525.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    526. 

  526.  0.00082] - training
    527. 

  527. 2025-05-22 13:36:37,675 - tf_lstm.py - INFO - 验证集损失函数为：[5.1143e-01 1.6646e-01 4.8020e-02 1.2190e-02 2.7800e-03 6.2000e-04
    528. 

  528.  1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    529. 

  529.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    530. 

  530.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    531. 

  531.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    532. 

  532.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    533. 

  533.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    534. 

  534.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    535. 

  535.  8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    536. 

  536.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    537. 

  537.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    538. 

  538.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    539. 

  539.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    540. 

  540.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    541. 

  541.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    542. 

  542.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    543. 

  543.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
    544. 

  544. 2025-05-22 13:36:37,713 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eb7e5627b101c684d84af - insert_trained_model_into_mongo
    545. 

  545. 2025-05-22 13:36:37,720 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eb7e5627b101c684d84b1 - insert_scaler_model_into_mongo
    546. 

  546. 2025-05-22 13:36:37,736 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eb7e5c78eb332b44d57b5 - insert_trained_model_into_mongo
    547. 

  547. 2025-05-22 13:36:37,757 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eb7e5c78eb332b44d57b7 - insert_scaler_model_into_mongo
    548. 

  548. 2025-05-22 13:36:39,035 - task_worker.py - ERROR - Area 1002 failed: 'Datetime' - region_task
    549. 

  549. 2025-05-22 13:43:10,995 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    550. 

  550.  - _tfmw_add_deprecation_warning
    551. 

  551. 2025-05-22 13:43:14,416 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    552. 

  552. 2025-05-22 13:43:37,750 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    553. 

  553.  - _tfmw_add_deprecation_warning
    554. 

  554. 2025-05-22 13:44:04,231 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    555. 

  555.  - _tfmw_add_deprecation_warning
    556. 

  556. 2025-05-22 13:44:04,235 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    557. 

  557.  - _tfmw_add_deprecation_warning
    558. 

  558. 2025-05-22 13:45:28,979 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
    559. 

  559. 2025-05-22 13:45:28,979 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources
    560. 

  560. 2025-05-22 13:45:29,000 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    561. 

  561. 2025-05-22 13:45:29,001 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    562. 

  562. 2025-05-22 13:45:29,028 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
    563. 

  563. 2025-05-22 13:45:29,029 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
    564. 

  564. 2025-05-22 13:45:29,071 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    565. 

  565. 2025-05-22 13:45:29,072 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    566. 

  566. 2025-05-22 13:45:29,073 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    567. 

  567. 2025-05-22 13:45:29,073 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    568. 

  568. 2025-05-22 13:45:29,074 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    569. 

  569. 2025-05-22 13:45:29,075 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    570. 

  570. 2025-05-22 13:45:31,139 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    571. 

  571. 2025-05-22 13:45:31,140 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    572. 

  572. 2025-05-22 13:45:31,142 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    573. 

  573. 2025-05-22 13:45:31,143 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    574. 

  574. 2025-05-22 13:46:07,442 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    575. 

  575. 2025-05-22 13:46:07,442 - tf_lstm.py - INFO - 训练集损失函数为：[8.9715e-01 3.1446e-01 9.7490e-02 2.6710e-02 6.4700e-03 1.4300e-03
    576. 

  576.  3.2000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05
    577. 

  577.  6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05
    578. 

  578.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    579. 

  579.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    580. 

  580.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    581. 

  581.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    582. 

  582.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    583. 

  583.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    584. 

  584.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    585. 

  585.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    586. 

  586.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    587. 

  587.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    588. 

  588.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    589. 

  589.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    590. 

  590.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    591. 

  591.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training
    592. 

  592. 2025-05-22 13:46:07,443 - tf_lstm.py - INFO - 验证集损失函数为：[5.0805e-01 1.6497e-01 4.7450e-02 1.2030e-02 2.7400e-03 6.1000e-04
    593. 

  593.  1.7000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    594. 

  594.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    595. 

  595.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    596. 

  596.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    597. 

  597.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    598. 

  598.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    599. 

  599.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    600. 

  600.  8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    601. 

  601.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    602. 

  602.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    603. 

  603.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    604. 

  604.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    605. 

  605.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    606. 

  606.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    607. 

  607.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    608. 

  608.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
    609. 

  609. 2025-05-22 13:46:07,584 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    610. 

  610. 2025-05-22 13:46:07,584 - tf_lstm.py - INFO - 训练集损失函数为：[8.9563e-01 3.1477e-01 9.8380e-02 2.7490e-02 7.0800e-03 1.9500e-03
    611. 

  611.  8.2000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04
    612. 

  612.  5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    613. 

  613.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    614. 

  614.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    615. 

  615.  5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    616. 

  616.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    617. 

  617.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    618. 

  618.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    619. 

  619.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    620. 

  620.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    621. 

  621.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    622. 

  622.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    623. 

  623.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    624. 

  624.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    625. 

  625.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    626. 

  626.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training
    627. 

  627. 2025-05-22 13:46:07,584 - tf_lstm.py - INFO - 验证集损失函数为：[0.50812 0.16611 0.04868 0.01307 0.00365 0.00146 0.00101 0.00092 0.0009
    628. 

  628.  0.00089 0.00089 0.00088 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087
    629. 

  629.  0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085
    630. 

  630.  0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084
    631. 

  631.  0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084
    632. 

  632.  0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083
    633. 

  633.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    634. 

  634.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    635. 

  635.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    636. 

  636.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    637. 

  637.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    638. 

  638.  0.00082] - training
    639. 

  639. 2025-05-22 13:46:14,116 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eba26770d6ffe7e22a033 - insert_trained_model_into_mongo
    640. 

  640. 2025-05-22 13:46:14,120 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eba26d439ed5d688f8e53 - insert_trained_model_into_mongo
    641. 

  641. 2025-05-22 13:46:14,129 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eba26770d6ffe7e22a035 - insert_scaler_model_into_mongo
    642. 

  642. 2025-05-22 13:46:14,166 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eba26d439ed5d688f8e55 - insert_scaler_model_into_mongo
    643. 

  643. 2025-05-22 14:22:14,267 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    644. 

  644.  - _tfmw_add_deprecation_warning
    645. 

  645. 2025-05-22 14:22:14,388 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    646. 

  646. 2025-05-22 14:22:17,074 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    647. 

  647.  - _tfmw_add_deprecation_warning
    648. 

  648. 2025-05-22 14:22:19,916 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    649. 

  649.  - _tfmw_add_deprecation_warning
    650. 

  650. 2025-05-22 14:22:22,871 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    651. 

  651.  - _tfmw_add_deprecation_warning
    652. 

  652. 2025-05-22 14:22:22,872 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    653. 

  653.  - _tfmw_add_deprecation_warning
    654. 

  654. 2025-05-22 14:22:23,608 - task_worker.py - ERROR - Area -99 failed: 'NoneType' object has no attribute 'area_id' - region_task
    655. 

  655. 2025-05-22 14:26:54,980 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    656. 

  656.  - _tfmw_add_deprecation_warning
    657. 

  657. 2025-05-22 14:26:55,106 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    658. 

  658. 2025-05-22 14:26:57,683 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    659. 

  659.  - _tfmw_add_deprecation_warning
    660. 

  660. 2025-05-22 14:27:00,530 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    661. 

  661.  - _tfmw_add_deprecation_warning
    662. 

  662. 2025-05-22 14:27:03,427 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    663. 

  663.  - _tfmw_add_deprecation_warning
    664. 

  664. 2025-05-22 14:27:03,432 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    665. 

  665.  - _tfmw_add_deprecation_warning
    666. 

  666. 2025-05-22 14:27:04,177 - task_worker.py - ERROR - Area -99 failed: 'NoneType' object has no attribute 'area_id' - region_task
    667. 

  667. 2025-05-22 14:27:41,884 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    668. 

  668.  - _tfmw_add_deprecation_warning
    669. 

  669. 2025-05-22 14:27:42,000 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    670. 

  670. 2025-05-22 14:27:44,598 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    671. 

  671.  - _tfmw_add_deprecation_warning
    672. 

  672. 2025-05-22 14:27:47,308 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    673. 

  673.  - _tfmw_add_deprecation_warning
    674. 

  674. 2025-05-22 14:27:50,203 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    675. 

  675.  - _tfmw_add_deprecation_warning
    676. 

  676. 2025-05-22 14:27:50,204 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    677. 

  677.  - _tfmw_add_deprecation_warning
    678. 

  678. 2025-05-22 14:27:50,954 - task_worker.py - ERROR - Area 1002 failed: Can only merge Series or DataFrame objects, a <class 'multiprocessing.managers.DictProxy'> was passed - region_task
    679. 

  679. 2025-05-22 15:56:08,387 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    680. 

  680.  - _tfmw_add_deprecation_warning
    681. 

  681. 2025-05-22 15:56:08,656 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    682. 

  682. 2025-05-22 15:56:11,294 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    683. 

  683.  - _tfmw_add_deprecation_warning
    684. 

  684. 2025-05-22 15:56:14,276 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    685. 

  685.  - _tfmw_add_deprecation_warning
    686. 

  686. 2025-05-22 15:56:14,282 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    687. 

  687.  - _tfmw_add_deprecation_warning
    688. 

  688. 2025-05-22 15:56:14,515 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
    689. 

  689. 2025-05-22 15:56:14,534 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources
    690. 

  690. 2025-05-22 15:56:14,537 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    691. 

  691. 2025-05-22 15:56:14,546 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
    692. 

  692. 2025-05-22 15:56:14,553 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    693. 

  693. 2025-05-22 15:56:14,561 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
    694. 

  694. 2025-05-22 15:56:14,583 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    695. 

  695. 2025-05-22 15:56:14,583 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    696. 

  696. 2025-05-22 15:56:14,584 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    697. 

  697. 2025-05-22 15:56:14,588 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    698. 

  698. 2025-05-22 15:56:14,588 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    699. 

  699. 2025-05-22 15:56:14,589 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    700. 

  700. 2025-05-22 15:56:15,565 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    701. 

  701. 2025-05-22 15:56:15,566 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    702. 

  702. 2025-05-22 15:56:15,566 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    703. 

  703. 2025-05-22 15:56:15,566 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    704. 

  704. 2025-05-22 15:56:28,464 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    705. 

  705. 2025-05-22 15:56:28,464 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    706. 

  706. 2025-05-22 15:56:28,465 - tf_lstm.py - INFO - 训练集损失函数为：[8.9989e-01 3.1527e-01 9.8050e-02 2.7280e-02 7.0200e-03 1.9400e-03
    707. 

  707.  8.2000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04
    708. 

  708.  5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04
    709. 

  709.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    710. 

  710.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    711. 

  711.  5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    712. 

  712.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    713. 

  713.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    714. 

  714.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    715. 

  715.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    716. 

  716.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    717. 

  717.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    718. 

  718.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    719. 

  719.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    720. 

  720.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    721. 

  721.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    722. 

  722.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training
    723. 

  723. 2025-05-22 15:56:28,465 - tf_lstm.py - INFO - 训练集损失函数为：[9.0568e-01 3.1746e-01 9.8530e-02 2.7070e-02 6.5900e-03 1.4600e-03
    724. 

  724.  3.2000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05
    725. 

  725.  6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05
    726. 

  726.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    727. 

  727.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    728. 

  728.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    729. 

  729.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    730. 

  730.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    731. 

  731.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    732. 

  732.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    733. 

  733.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    734. 

  734.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    735. 

  735.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    736. 

  736.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    737. 

  737.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    738. 

  738.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    739. 

  739.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training
    740. 

  740. 2025-05-22 15:56:28,465 - tf_lstm.py - INFO - 验证集损失函数为：[0.50977 0.16589 0.04836 0.01296 0.00362 0.00146 0.00101 0.00092 0.0009
    741. 

  741.  0.00089 0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087
    742. 

  742.  0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085
    743. 

  743.  0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084
    744. 

  744.  0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084
    745. 

  745.  0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083
    746. 

  746.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    747. 

  747.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    748. 

  748.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    749. 

  749.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    750. 

  750.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    751. 

  751.  0.00082] - training
    752. 

  752. 2025-05-22 15:56:28,465 - tf_lstm.py - INFO - 验证集损失函数为：[5.1289e-01 1.6660e-01 4.8030e-02 1.2220e-02 2.8000e-03 6.2000e-04
    753. 

  753.  1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    754. 

  754.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    755. 

  755.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    756. 

  756.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    757. 

  757.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    758. 

  758.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    759. 

  759.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    760. 

  760.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05
    761. 

  761.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    762. 

  762.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    763. 

  763.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    764. 

  764.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    765. 

  765.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    766. 

  766.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    767. 

  767.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    768. 

  768.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
    769. 

  769. 2025-05-22 15:56:28,507 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ed8acd5726df85922d205 - insert_trained_model_into_mongo
    770. 

  770. 2025-05-22 15:56:28,522 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ed8ac2e1adc5f49fe0f09 - insert_trained_model_into_mongo
    771. 

  771. 2025-05-22 15:56:28,529 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ed8acd5726df85922d207 - insert_scaler_model_into_mongo
    772. 

  772. 2025-05-22 15:56:28,543 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ed8ac2e1adc5f49fe0f0b - insert_scaler_model_into_mongo
    773. 

  773. 2025-05-22 15:56:29,862 - task_worker.py - ERROR - Area 1002 failed: 'Datetime' - region_task
    774. 

  774. 2025-05-22 15:57:07,940 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    775. 

  775.  - _tfmw_add_deprecation_warning
    776. 

  776. 2025-05-22 15:57:08,055 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    777. 

  777. 2025-05-22 15:57:10,632 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    778. 

  778.  - _tfmw_add_deprecation_warning
    779. 

  779. 2025-05-22 15:57:13,508 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    780. 

  780.  - _tfmw_add_deprecation_warning
    781. 

  781. 2025-05-22 15:57:13,518 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    782. 

  782.  - _tfmw_add_deprecation_warning
    783. 

  783. 2025-05-22 15:57:13,711 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources
    784. 

  784. 2025-05-22 15:57:13,718 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
    785. 

  785. 2025-05-22 15:57:13,729 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    786. 

  786. 2025-05-22 15:57:13,736 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    787. 

  787. 2025-05-22 15:57:13,737 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
    788. 

  788. 2025-05-22 15:57:13,744 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
    789. 

  789. 2025-05-22 15:57:13,770 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    790. 

  790. 2025-05-22 15:57:13,770 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    791. 

  791. 2025-05-22 15:57:13,770 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    792. 

  792. 2025-05-22 15:57:13,776 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    793. 

  793. 2025-05-22 15:57:13,776 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    794. 

  794. 2025-05-22 15:57:13,777 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    795. 

  795. 2025-05-22 15:57:14,694 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    796. 

  796. 2025-05-22 15:57:14,694 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    797. 

  797. 2025-05-22 15:57:14,712 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    798. 

  798. 2025-05-22 15:57:14,713 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    799. 

  799. 2025-05-22 15:57:27,628 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    800. 

  800. 2025-05-22 15:57:27,628 - tf_lstm.py - INFO - 训练集损失函数为：[9.1627e-01 3.2273e-01 1.0049e-01 2.7670e-02 6.7300e-03 1.4900e-03
    801. 

  801.  3.3000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05
    802. 

  802.  6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05
    803. 

  803.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    804. 

  804.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    805. 

  805.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    806. 

  806.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    807. 

  807.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    808. 

  808.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    809. 

  809.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    810. 

  810.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    811. 

  811.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    812. 

  812.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    813. 

  813.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    814. 

  814.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    815. 

  815.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    816. 

  816.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training
    817. 

  817. 2025-05-22 15:57:27,628 - tf_lstm.py - INFO - 验证集损失函数为：[5.2048e-01 1.6975e-01 4.9060e-02 1.2500e-02 2.8600e-03 6.3000e-04
    818. 

  818.  1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    819. 

  819.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    820. 

  820.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    821. 

  821.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    822. 

  822.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    823. 

  823.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    824. 

  824.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    825. 

  825.  8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    826. 

  826.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    827. 

  827.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    828. 

  828.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    829. 

  829.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    830. 

  830.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    831. 

  831.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    832. 

  832.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    833. 

  833.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
    834. 

  834. 2025-05-22 15:57:27,654 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    835. 

  835. 2025-05-22 15:57:27,655 - tf_lstm.py - INFO - 训练集损失函数为：[9.1329e-01 3.2208e-01 1.0066e-01 2.8050e-02 7.1900e-03 1.9700e-03
    836. 

  836.  8.2000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04
    837. 

  837.  5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04
    838. 

  838.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    839. 

  839.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    840. 

  840.  5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    841. 

  841.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    842. 

  842.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    843. 

  843.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    844. 

  844.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    845. 

  845.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    846. 

  846.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    847. 

  847.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    848. 

  848.  5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    849. 

  849.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    850. 

  850.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    851. 

  851.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training
    852. 

  852. 2025-05-22 15:57:27,655 - tf_lstm.py - INFO - 验证集损失函数为：[0.51954 0.17006 0.04973 0.01329 0.00368 0.00147 0.00101 0.00092 0.0009
    853. 

  853.  0.00089 0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087
    854. 

  854.  0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085
    855. 

  855.  0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084
    856. 

  856.  0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084
    857. 

  857.  0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    858. 

  858.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    859. 

  859.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082 0.00082
    860. 

  860.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    861. 

  861.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    862. 

  862.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    863. 

  863.  0.00082] - training
    864. 

  864. 2025-05-22 15:57:27,670 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ed8e7d0ad45b5ef1ff99f - insert_trained_model_into_mongo
    865. 

  865. 2025-05-22 15:57:27,693 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ed8e7952fc3d26b39b369 - insert_trained_model_into_mongo
    866. 

  866. 2025-05-22 15:57:27,698 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ed8e7d0ad45b5ef1ff9a1 - insert_scaler_model_into_mongo
    867. 

  867. 2025-05-22 15:57:27,700 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ed8e7952fc3d26b39b36b - insert_scaler_model_into_mongo
    868. 

  868. 2025-05-22 15:57:28,989 - task_worker.py - ERROR - Area 1002 failed: 'Datetime' - region_task
    869. 

  869. 2025-05-22 16:00:26,751 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    870. 

  870.  - _tfmw_add_deprecation_warning
    871. 

  871. 2025-05-22 16:02:27,969 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    872. 

  872. 2025-05-22 16:02:51,521 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    873. 

  873.  - _tfmw_add_deprecation_warning
    874. 

  874. 2025-05-22 16:03:20,174 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    875. 

  875.  - _tfmw_add_deprecation_warning
    876. 

  876. 2025-05-22 16:03:20,244 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    877. 

  877.  - _tfmw_add_deprecation_warning
    878. 

  878. 2025-05-22 16:03:43,812 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
    879. 

  879. 2025-05-22 16:03:43,818 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources
    880. 

  880. 2025-05-22 16:03:43,837 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    881. 

  881. 2025-05-22 16:03:43,843 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    882. 

  882. 2025-05-22 16:03:43,869 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
    883. 

  883. 2025-05-22 16:03:43,874 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
    884. 

  884. 2025-05-22 16:03:43,915 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    885. 

  885. 2025-05-22 16:03:43,916 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    886. 

  886. 2025-05-22 16:03:43,918 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    887. 

  887. 2025-05-22 16:03:43,920 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    888. 

  888. 2025-05-22 16:03:43,920 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    889. 

  889. 2025-05-22 16:03:43,922 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    890. 

  890. 2025-05-22 16:03:45,818 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    891. 

  891. 2025-05-22 16:03:45,820 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    892. 

  892. 2025-05-22 16:03:45,842 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    893. 

  893. 2025-05-22 16:03:45,842 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    894. 

  894. 2025-05-22 16:04:23,192 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    895. 

  895. 2025-05-22 16:04:23,193 - tf_lstm.py - INFO - 训练集损失函数为：[9.0817e-01 3.2013e-01 1.0016e-01 2.7980e-02 7.1900e-03 1.9700e-03
    896. 

  896.  8.2000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04
    897. 

  897.  5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    898. 

  898.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    899. 

  899.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    900. 

  900.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    901. 

  901.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    902. 

  902.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    903. 

  903.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    904. 

  904.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    905. 

  905.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    906. 

  906.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    907. 

  907.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    908. 

  908.  5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    909. 

  909.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    910. 

  910.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    911. 

  911.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training
    912. 

  912. 2025-05-22 16:04:23,192 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    913. 

  913. 2025-05-22 16:04:23,193 - tf_lstm.py - INFO - 验证集损失函数为：[0.51636 0.16911 0.04957 0.01328 0.00369 0.00147 0.00101 0.00092 0.0009
    914. 

  914.  0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087
    915. 

  915.  0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085
    916. 

  916.  0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084
    917. 

  917.  0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084
    918. 

  918.  0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    919. 

  919.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    920. 

  920.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082
    921. 

  921.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    922. 

  922.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    923. 

  923.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    924. 

  924.  0.00082] - training
    925. 

  925. 2025-05-22 16:04:23,193 - tf_lstm.py - INFO - 训练集损失函数为：[9.0438e-01 3.1823e-01 9.9190e-02 2.7320e-02 6.6400e-03 1.4700e-03
    926. 

  926.  3.3000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05
    927. 

  927.  6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    928. 

  928.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    929. 

  929.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    930. 

  930.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    931. 

  931.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    932. 

  932.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    933. 

  933.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    934. 

  934.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    935. 

  935.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    936. 

  936.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    937. 

  937.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    938. 

  938.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    939. 

  939.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    940. 

  940.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    941. 

  941.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training
    942. 

  942. 2025-05-22 16:04:23,193 - tf_lstm.py - INFO - 验证集损失函数为：[5.1324e-01 1.6746e-01 4.8450e-02 1.2330e-02 2.8200e-03 6.2000e-04
    943. 

  943.  1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    944. 

  944.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    945. 

  945.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    946. 

  946.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    947. 

  947.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    948. 

  948.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    949. 

  949.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    950. 

  950.  8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    951. 

  951.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    952. 

  952.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    953. 

  953.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    954. 

  954.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    955. 

  955.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    956. 

  956.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    957. 

  957.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    958. 

  958.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
    959. 

  959. 2025-05-22 16:04:28,183 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eda8ce1472c691263abcd - insert_trained_model_into_mongo
    960. 

  960. 2025-05-22 16:04:28,184 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eda8ca57967ec2470f538 - insert_trained_model_into_mongo
    961. 

  961. 2025-05-22 16:04:28,234 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eda8ca57967ec2470f53a - insert_scaler_model_into_mongo
    962. 

  962. 2025-05-22 16:04:28,235 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eda8ce1472c691263abcf - insert_scaler_model_into_mongo
    963. 

  963. 2025-05-22 16:15:20,362 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    964. 

  964.  - _tfmw_add_deprecation_warning
    965. 

  965. 2025-05-22 16:15:20,482 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    966. 

  966. 2025-05-22 16:15:23,124 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    967. 

  967.  - _tfmw_add_deprecation_warning
    968. 

  968. 2025-05-22 16:15:26,082 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    969. 

  969.  - _tfmw_add_deprecation_warning
    970. 

  970. 2025-05-22 16:15:26,083 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    971. 

  971.  - _tfmw_add_deprecation_warning
    972. 

  972. 2025-05-22 16:15:26,299 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources
    973. 

  973. 2025-05-22 16:15:26,299 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
    974. 

  974. 2025-05-22 16:15:26,318 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    975. 

  975. 2025-05-22 16:15:26,318 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    976. 

  976. 2025-05-22 16:15:26,326 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
    977. 

  977. 2025-05-22 16:15:26,326 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
    978. 

  978. 2025-05-22 16:15:26,353 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    979. 

  979. 2025-05-22 16:15:26,353 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    980. 

  980. 2025-05-22 16:15:26,353 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    981. 

  981. 2025-05-22 16:15:26,353 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    982. 

  982. 2025-05-22 16:15:26,353 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    983. 

  983. 2025-05-22 16:15:26,353 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    984. 

  984. 2025-05-22 16:15:27,303 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    985. 

  985. 2025-05-22 16:15:27,303 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    986. 

  986. 2025-05-22 16:15:27,303 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    987. 

  987. 2025-05-22 16:15:27,303 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    988. 

  988. 2025-05-22 16:15:40,212 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    989. 

  989. 2025-05-22 16:15:40,212 - tf_lstm.py - INFO - 训练集损失函数为：[9.0386e-01 3.1856e-01 9.9750e-02 2.7890e-02 7.1800e-03 1.9800e-03
    990. 

  990.  8.3000e-04 6.0000e-04 5.5000e-04 5.5000e-04 5.4000e-04 5.4000e-04
    991. 

  991.  5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04
    992. 

  992.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    993. 

  993.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    994. 

  994.  5.3000e-04 5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    995. 

  995.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    996. 

  996.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    997. 

  997.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    998. 

  998.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    999. 

  999.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1000. 

  1000.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1001. 

  1001.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1002. 

  1002.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1003. 

  1003.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    1004. 

  1004.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    1005. 

  1005.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training
    1006. 

  1006. 2025-05-22 16:15:40,212 - tf_lstm.py - INFO - 训练集损失函数为：[8.9587e-01 3.1399e-01 9.7500e-02 2.6790e-02 6.5000e-03 1.4400e-03
    1007. 

  1007.  3.2000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05
    1008. 

  1008.  6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05
    1009. 

  1009.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1010. 

  1010.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1011. 

  1011.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1012. 

  1012.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1013. 

  1013.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1014. 

  1014.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1015. 

  1015.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1016. 

  1016.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1017. 

  1017.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1018. 

  1018.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1019. 

  1019.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1020. 

  1020.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1021. 

  1021.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1022. 

  1022.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training
    1023. 

  1023. 2025-05-22 16:15:40,212 - tf_lstm.py - INFO - 验证集损失函数为：[0.51364 0.16831 0.04936 0.01325 0.0037  0.00148 0.00101 0.00092 0.0009
    1024. 

  1024.  0.0009  0.00089 0.00089 0.00088 0.00088 0.00088 0.00088 0.00087 0.00087
    1025. 

  1025.  0.00087 0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086
    1026. 

  1026.  0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085
    1027. 

  1027.  0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084
    1028. 

  1028.  0.00084 0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083
    1029. 

  1029.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    1030. 

  1030.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    1031. 

  1031.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1032. 

  1032.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1033. 

  1033.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1034. 

  1034.  0.00082] - training
    1035. 

  1035. 2025-05-22 16:15:40,213 - tf_lstm.py - INFO - 验证集损失函数为：[5.0722e-01 1.6484e-01 4.7540e-02 1.2080e-02 2.7600e-03 6.1000e-04
    1036. 

  1036.  1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1037. 

  1037.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1038. 

  1038.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1039. 

  1039.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1040. 

  1040.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1041. 

  1041.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1042. 

  1042.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1043. 

  1043.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05
    1044. 

  1044.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1045. 

  1045.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1046. 

  1046.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1047. 

  1047.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1048. 

  1048.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1049. 

  1049.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1050. 

  1050.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1051. 

  1051.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
    1052. 

  1052. 2025-05-22 16:15:40,256 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682edd2c58422a3c9f4e08aa - insert_trained_model_into_mongo
    1053. 

  1053. 2025-05-22 16:15:40,266 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682edd2c1c34fc32963f2881 - insert_trained_model_into_mongo
    1054. 

  1054. 2025-05-22 16:15:40,287 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682edd2c58422a3c9f4e08ac - insert_scaler_model_into_mongo
    1055. 

  1055. 2025-05-22 16:15:40,289 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682edd2c1c34fc32963f2883 - insert_scaler_model_into_mongo
    1056. 

  1056. 2025-05-22 16:15:41,578 - task_worker.py - ERROR - Area 1002 failed: 'Datetime' - region_task
    1057. 

  1057. 2025-05-22 16:18:18,375 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1058. 

  1058.  - _tfmw_add_deprecation_warning
    1059. 

  1059. 2025-05-22 16:18:18,503 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    1060. 

  1060. 2025-05-22 16:18:21,225 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1061. 

  1061.  - _tfmw_add_deprecation_warning
    1062. 

  1062. 2025-05-22 16:18:24,210 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1063. 

  1063.  - _tfmw_add_deprecation_warning
    1064. 

  1064. 2025-05-22 16:18:24,216 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1065. 

  1065.  - _tfmw_add_deprecation_warning
    1066. 

  1066. 2025-05-22 16:18:24,419 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources
    1067. 

  1067. 2025-05-22 16:18:24,420 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
    1068. 

  1068. 2025-05-22 16:18:24,439 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    1069. 

  1069. 2025-05-22 16:18:24,440 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    1070. 

  1070. 2025-05-22 16:18:24,447 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
    1071. 

  1071. 2025-05-22 16:18:24,448 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
    1072. 

  1072. 2025-05-22 16:18:24,475 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    1073. 

  1073. 2025-05-22 16:18:24,475 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    1074. 

  1074. 2025-05-22 16:18:24,476 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    1075. 

  1075. 2025-05-22 16:18:24,476 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    1076. 

  1076. 2025-05-22 16:18:24,476 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    1077. 

  1077. 2025-05-22 16:18:24,477 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    1078. 

  1078. 2025-05-22 16:18:25,429 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    1079. 

  1079. 2025-05-22 16:18:25,429 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    1080. 

  1080. 2025-05-22 16:18:25,431 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    1081. 

  1081. 2025-05-22 16:18:25,431 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    1082. 

  1082. 2025-05-22 16:18:38,854 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    1083. 

  1083. 2025-05-22 16:18:38,854 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    1084. 

  1084. 2025-05-22 16:18:38,854 - tf_lstm.py - INFO - 训练集损失函数为：[8.9569e-01 3.1485e-01 9.8280e-02 2.7410e-02 7.0500e-03 1.9500e-03
    1085. 

  1085.  8.2000e-04 6.0000e-04 5.6000e-04 5.5000e-04 5.5000e-04 5.4000e-04
    1086. 

  1086.  5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04
    1087. 

  1087.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    1088. 

  1088.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    1089. 

  1089.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04
    1090. 

  1090.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1091. 

  1091.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1092. 

  1092.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1093. 

  1093.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1094. 

  1094.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1095. 

  1095.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1096. 

  1096.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1097. 

  1097.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04
    1098. 

  1098.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    1099. 

  1099.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    1100. 

  1100.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training
    1101. 

  1101. 2025-05-22 16:18:38,855 - tf_lstm.py - INFO - 训练集损失函数为：[9.1019e-01 3.2047e-01 9.9880e-02 2.7530e-02 6.7100e-03 1.4800e-03
    1102. 

  1102.  3.3000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05
    1103. 

  1103.  6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05
    1104. 

  1104.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1105. 

  1105.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1106. 

  1106.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1107. 

  1107.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1108. 

  1108.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1109. 

  1109.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1110. 

  1110.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1111. 

  1111.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1112. 

  1112.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1113. 

  1113.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1114. 

  1114.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1115. 

  1115.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1116. 

  1116.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1117. 

  1117.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training
    1118. 

  1118. 2025-05-22 16:18:38,855 - tf_lstm.py - INFO - 验证集损失函数为：[0.50823 0.16598 0.0485  0.01302 0.00365 0.00147 0.00102 0.00093 0.00091
    1119. 

  1119.  0.0009  0.0009  0.00089 0.00089 0.00089 0.00088 0.00088 0.00088 0.00087
    1120. 

  1120.  0.00087 0.00087 0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086
    1121. 

  1121.  0.00086 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085
    1122. 

  1122.  0.00085 0.00085 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084
    1123. 

  1123.  0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083
    1124. 

  1124.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    1125. 

  1125.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    1126. 

  1126.  0.00083 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1127. 

  1127.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1128. 

  1128.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1129. 

  1129.  0.00082] - training
    1130. 

  1130. 2025-05-22 16:18:38,855 - tf_lstm.py - INFO - 验证集损失函数为：[5.1680e-01 1.6865e-01 4.8800e-02 1.2440e-02 2.8500e-03 6.3000e-04
    1131. 

  1131.  1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1132. 

  1132.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1133. 

  1133.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1134. 

  1134.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1135. 

  1135.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1136. 

  1136.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1137. 

  1137.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1138. 

  1138.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05
    1139. 

  1139.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1140. 

  1140.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1141. 

  1141.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1142. 

  1142.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1143. 

  1143.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1144. 

  1144.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1145. 

  1145.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1146. 

  1146.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
    1147. 

  1147. 2025-05-22 16:18:38,892 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eddde166c956a98e4cdb6 - insert_trained_model_into_mongo
    1148. 

  1148. 2025-05-22 16:18:38,903 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eddde9239166b91e13257 - insert_trained_model_into_mongo
    1149. 

  1149. 2025-05-22 16:18:38,910 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eddde9239166b91e13259 - insert_scaler_model_into_mongo
    1150. 

  1150. 2025-05-22 16:18:38,924 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eddde166c956a98e4cdb8 - insert_scaler_model_into_mongo
    1151. 

  1151. 2025-05-22 16:19:36,327 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1152. 

  1152.  - _tfmw_add_deprecation_warning
    1153. 

  1153. 2025-05-22 16:19:36,452 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    1154. 

  1154. 2025-05-22 16:19:39,140 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1155. 

  1155.  - _tfmw_add_deprecation_warning
    1156. 

  1156. 2025-05-22 16:19:42,056 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1157. 

  1157.  - _tfmw_add_deprecation_warning
    1158. 

  1158. 2025-05-22 16:19:42,056 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1159. 

  1159.  - _tfmw_add_deprecation_warning
    1160. 

  1160. 2025-05-22 16:19:42,261 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources
    1161. 

  1161. 2025-05-22 16:19:42,261 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
    1162. 

  1162. 2025-05-22 16:19:42,279 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    1163. 

  1163. 2025-05-22 16:19:42,279 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    1164. 

  1164. 2025-05-22 16:19:42,287 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
    1165. 

  1165. 2025-05-22 16:19:42,287 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
    1166. 

  1166. 2025-05-22 16:19:42,316 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    1167. 

  1167. 2025-05-22 16:19:42,316 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    1168. 

  1168. 2025-05-22 16:19:42,317 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    1169. 

  1169. 2025-05-22 16:19:42,317 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    1170. 

  1170. 2025-05-22 16:19:42,317 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    1171. 

  1171. 2025-05-22 16:19:42,317 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    1172. 

  1172. 2025-05-22 16:19:43,248 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    1173. 

  1173. 2025-05-22 16:19:43,249 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    1174. 

  1174. 2025-05-22 16:19:43,261 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    1175. 

  1175. 2025-05-22 16:19:43,261 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    1176. 

  1176. 2025-05-22 16:19:56,132 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    1177. 

  1177. 2025-05-22 16:19:56,132 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    1178. 

  1178. 2025-05-22 16:19:56,133 - tf_lstm.py - INFO - 训练集损失函数为：[9.0371e-01 3.1890e-01 9.9980e-02 2.7980e-02 7.2000e-03 1.9800e-03
    1179. 

  1179.  8.2000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04
    1180. 

  1180.  5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    1181. 

  1181.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    1182. 

  1182.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    1183. 

  1183.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1184. 

  1184.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1185. 

  1185.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1186. 

  1186.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1187. 

  1187.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1188. 

  1188.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1189. 

  1189.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1190. 

  1190.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1191. 

  1191.  5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    1192. 

  1192.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    1193. 

  1193.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    1194. 

  1194.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training
    1195. 

  1195. 2025-05-22 16:19:56,133 - tf_lstm.py - INFO - 验证集损失函数为：[0.51401 0.16865 0.04951 0.01329 0.0037  0.00147 0.00101 0.00092 0.0009
    1196. 

  1196.  0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087
    1197. 

  1197.  0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085 0.00085
    1198. 

  1198.  0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084 0.00084
    1199. 

  1199.  0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084
    1200. 

  1200.  0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    1201. 

  1201.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    1202. 

  1202.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082 0.00082
    1203. 

  1203.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1204. 

  1204.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1205. 

  1205.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1206. 

  1206.  0.00082] - training
    1207. 

  1207. 2025-05-22 16:19:56,133 - tf_lstm.py - INFO - 验证集损失函数为：[5.1199e-01 1.6678e-01 4.8120e-02 1.2220e-02 2.7800e-03 6.2000e-04
    1208. 

  1208.  1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1209. 

  1209.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1210. 

  1210.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1211. 

  1211.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1212. 

  1212.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1213. 

  1213.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1214. 

  1214.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1215. 

  1215.  8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1216. 

  1216.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1217. 

  1217.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1218. 

  1218.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1219. 

  1219.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1220. 

  1220.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1221. 

  1221.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1222. 

  1222.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1223. 

  1223.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
    1224. 

  1224. 2025-05-22 16:19:56,191 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ede2c6f86cd09523a290d - insert_trained_model_into_mongo
    1225. 

  1225. 2025-05-22 16:19:56,191 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ede2c82774bd11b16d043 - insert_trained_model_into_mongo
    1226. 

  1226. 2025-05-22 16:19:56,209 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ede2c6f86cd09523a290f - insert_scaler_model_into_mongo
    1227. 

  1227. 2025-05-22 16:19:56,223 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ede2c82774bd11b16d045 - insert_scaler_model_into_mongo
    1228. 

  1228. 2025-05-22 16:19:57,513 - task_worker.py - ERROR - Area 1002 failed: 'Datetime' - region_task
    1229. 

  1229. 2025-05-22 16:23:41,614 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1230. 

  1230.  - _tfmw_add_deprecation_warning
    1231. 

  1231. 2025-05-22 16:23:47,120 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    1232. 

  1232. 2025-05-22 16:24:10,592 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1233. 

  1233.  - _tfmw_add_deprecation_warning
    1234. 

  1234. 2025-05-22 16:24:39,240 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1235. 

  1235.  - _tfmw_add_deprecation_warning
    1236. 

  1236. 2025-05-22 16:24:39,242 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1237. 

  1237.  - _tfmw_add_deprecation_warning
    1238. 

  1238. 2025-05-22 16:24:39,724 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
    1239. 

  1239. 2025-05-22 16:24:39,726 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources
    1240. 

  1240. 2025-05-22 16:24:39,745 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    1241. 

  1241. 2025-05-22 16:24:39,748 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    1242. 

  1242. 2025-05-22 16:24:39,774 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
    1243. 

  1243. 2025-05-22 16:24:39,776 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
    1244. 

  1244. 2025-05-22 16:24:39,816 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    1245. 

  1245. 2025-05-22 16:24:39,816 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    1246. 

  1246. 2025-05-22 16:24:39,817 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    1247. 

  1247. 2025-05-22 16:24:39,817 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    1248. 

  1248. 2025-05-22 16:24:39,819 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    1249. 

  1249. 2025-05-22 16:24:39,819 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    1250. 

  1250. 2025-05-22 16:24:41,717 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    1251. 

  1251. 2025-05-22 16:24:41,717 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    1252. 

  1252. 2025-05-22 16:24:41,748 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    1253. 

  1253. 2025-05-22 16:24:41,748 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    1254. 

  1254. 2025-05-22 16:25:18,445 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    1255. 

  1255. 2025-05-22 16:25:18,445 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    1256. 

  1256. 2025-05-22 16:25:18,445 - tf_lstm.py - INFO - 训练集损失函数为：[9.0181e-01 3.1616e-01 9.7980e-02 2.6830e-02 6.4900e-03 1.4300e-03
    1257. 

  1257.  3.2000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05
    1258. 

  1258.  6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05
    1259. 

  1259.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1260. 

  1260.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1261. 

  1261.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1262. 

  1262.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1263. 

  1263.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1264. 

  1264.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1265. 

  1265.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1266. 

  1266.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1267. 

  1267.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1268. 

  1268.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1269. 

  1269.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1270. 

  1270.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1271. 

  1271.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1272. 

  1272.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training
    1273. 

  1273. 2025-05-22 16:25:18,445 - tf_lstm.py - INFO - 训练集损失函数为：[8.9533e-01 3.1324e-01 9.7340e-02 2.7070e-02 6.9500e-03 1.9200e-03
    1274. 

  1274.  8.1000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04
    1275. 

  1275.  5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    1276. 

  1276.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    1277. 

  1277.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    1278. 

  1278.  5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1279. 

  1279.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1280. 

  1280.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1281. 

  1281.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1282. 

  1282.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1283. 

  1283.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1284. 

  1284.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1285. 

  1285.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1286. 

  1286.  5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    1287. 

  1287.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    1288. 

  1288.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    1289. 

  1289.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training
    1290. 

  1290. 2025-05-22 16:25:18,446 - tf_lstm.py - INFO - 验证集损失函数为：[5.1082e-01 1.6583e-01 4.7670e-02 1.2070e-02 2.7500e-03 6.1000e-04
    1291. 

  1291.  1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1292. 

  1292.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1293. 

  1293.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1294. 

  1294.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1295. 

  1295.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1296. 

  1296.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1297. 

  1297.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1298. 

  1298.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1299. 

  1299.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1300. 

  1300.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1301. 

  1301.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1302. 

  1302.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1303. 

  1303.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1304. 

  1304.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1305. 

  1305.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1306. 

  1306.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
    1307. 

  1307. 2025-05-22 16:25:18,446 - tf_lstm.py - INFO - 验证集损失函数为：[0.50668 0.16475 0.04801 0.01285 0.00359 0.00145 0.00101 0.00092 0.0009
    1308. 

  1308.  0.00089 0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087
    1309. 

  1309.  0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085
    1310. 

  1310.  0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084
    1311. 

  1311.  0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084
    1312. 

  1312.  0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083
    1313. 

  1313.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    1314. 

  1314.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082
    1315. 

  1315.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1316. 

  1316.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1317. 

  1317.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1318. 

  1318.  0.00082] - training
    1319. 

  1319. 2025-05-22 16:25:18,550 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682edf6e4f4d66c1538080fc - insert_trained_model_into_mongo
    1320. 

  1320. 2025-05-22 16:25:18,562 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682edf6e4f4d66c1538080fe - insert_scaler_model_into_mongo
    1321. 

  1321. 2025-05-22 16:25:18,564 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682edf6ee396be238e929ca2 - insert_trained_model_into_mongo
    1322. 

  1322. 2025-05-22 16:25:18,576 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682edf6ee396be238e929ca4 - insert_scaler_model_into_mongo
    1323. 

  1323. 2025-05-22 16:36:42,098 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1324. 

  1324.  - _tfmw_add_deprecation_warning
    1325. 

  1325. 2025-05-22 16:37:06,300 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    1326. 

  1326. 2025-05-22 16:37:29,931 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1327. 

  1327.  - _tfmw_add_deprecation_warning
    1328. 

  1328. 2025-05-22 16:37:56,059 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1329. 

  1329.  - _tfmw_add_deprecation_warning
    1330. 

  1330. 2025-05-22 16:37:56,060 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1331. 

  1331.  - _tfmw_add_deprecation_warning
    1332. 

  1332. 2025-05-22 16:37:56,556 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources
    1333. 

  1333. 2025-05-22 16:37:56,556 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
    1334. 

  1334. 2025-05-22 16:37:56,578 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    1335. 

  1335. 2025-05-22 16:37:56,578 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    1336. 

  1336. 2025-05-22 16:37:56,606 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
    1337. 

  1337. 2025-05-22 16:37:56,606 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
    1338. 

  1338. 2025-05-22 16:37:56,647 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    1339. 

  1339. 2025-05-22 16:37:56,647 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    1340. 

  1340. 2025-05-22 16:37:56,647 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    1341. 

  1341. 2025-05-22 16:37:56,649 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    1342. 

  1342. 2025-05-22 16:37:56,649 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    1343. 

  1343. 2025-05-22 16:37:58,554 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    1344. 

  1344. 2025-05-22 16:37:58,554 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    1345. 

  1345. 2025-05-22 16:37:58,580 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    1346. 

  1346. 2025-05-22 16:37:58,580 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    1347. 

  1347. 2025-05-22 16:38:35,605 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    1348. 

  1348. 2025-05-22 16:38:35,605 - tf_lstm.py - INFO - 训练集损失函数为：[9.0578e-01 3.1937e-01 1.0006e-01 2.7970e-02 7.1800e-03 1.9700e-03
    1349. 

  1349.  8.2000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04
    1350. 

  1350.  5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    1351. 

  1351.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    1352. 

  1352.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    1353. 

  1353.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1354. 

  1354.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1355. 

  1355.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1356. 

  1356.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1357. 

  1357.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1358. 

  1358.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1359. 

  1359.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1360. 

  1360.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1361. 

  1361.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04
    1362. 

  1362.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    1363. 

  1363.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    1364. 

  1364.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training
    1365. 

  1365. 2025-05-22 16:38:35,606 - tf_lstm.py - INFO - 验证集损失函数为：[0.51488 0.16886 0.04954 0.01328 0.00368 0.00146 0.001   0.00092 0.0009
    1366. 

  1366.  0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087
    1367. 

  1367.  0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085
    1368. 

  1368.  0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084
    1369. 

  1369.  0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084
    1370. 

  1370.  0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    1371. 

  1371.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    1372. 

  1372.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082
    1373. 

  1373.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1374. 

  1374.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1375. 

  1375.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1376. 

  1376.  0.00082] - training
    1377. 

  1377. 2025-05-22 16:38:35,635 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    1378. 

  1378. 2025-05-22 16:38:35,635 - tf_lstm.py - INFO - 训练集损失函数为：[8.9900e-01 3.1653e-01 9.8870e-02 2.7350e-02 6.6900e-03 1.4900e-03
    1379. 

  1379.  3.3000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05
    1380. 

  1380.  6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05
    1381. 

  1381.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1382. 

  1382.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1383. 

  1383.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1384. 

  1384.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1385. 

  1385.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1386. 

  1386.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1387. 

  1387.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1388. 

  1388.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1389. 

  1389.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1390. 

  1390.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1391. 

  1391.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1392. 

  1392.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1393. 

  1393.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1394. 

  1394.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training
    1395. 

  1395. 2025-05-22 16:38:35,636 - tf_lstm.py - INFO - 验证集损失函数为：[5.1022e-01 1.6676e-01 4.8400e-02 1.2390e-02 2.8500e-03 6.4000e-04
    1396. 

  1396.  1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1397. 

  1397.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1398. 

  1398.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1399. 

  1399.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1400. 

  1400.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1401. 

  1401.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1402. 

  1402.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1403. 

  1403.  8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1404. 

  1404.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1405. 

  1405.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1406. 

  1406.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1407. 

  1407.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1408. 

  1408.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1409. 

  1409.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1410. 

  1410.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1411. 

  1411.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
    1412. 

  1412. 2025-05-22 16:38:35,708 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ee28b27b5da75067d35a8 - insert_trained_model_into_mongo
    1413. 

  1413. 2025-05-22 16:38:35,719 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ee28b7a38bab92a0a5a3a - insert_trained_model_into_mongo
    1414. 

  1414. 2025-05-22 16:38:35,731 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ee28b7a38bab92a0a5a3c - insert_scaler_model_into_mongo
    1415. 

  1415. 2025-05-22 16:38:35,741 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ee28b27b5da75067d35aa - insert_scaler_model_into_mongo
    1416. 

  1416. 2025-05-22 16:41:06,720 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1417. 

  1417.  - _tfmw_add_deprecation_warning
    1418. 

  1418. 2025-05-22 16:41:06,848 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    1419. 

  1419. 2025-05-22 16:41:09,615 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1420. 

  1420.  - _tfmw_add_deprecation_warning
    1421. 

  1421. 2025-05-22 16:41:36,827 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1422. 

  1422.  - _tfmw_add_deprecation_warning
    1423. 

  1423. 2025-05-22 16:41:52,435 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    1424. 

  1424. 2025-05-22 16:42:15,970 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1425. 

  1425.  - _tfmw_add_deprecation_warning
    1426. 

  1426. 2025-05-22 16:42:47,697 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1427. 

  1427.  - _tfmw_add_deprecation_warning
    1428. 

  1428. 2025-05-22 16:42:47,703 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1429. 

  1429.  - _tfmw_add_deprecation_warning
    1430. 

  1430. 2025-05-22 16:42:48,195 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
    1431. 

  1431. 2025-05-22 16:42:48,200 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources
    1432. 

  1432. 2025-05-22 16:42:48,218 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    1433. 

  1433. 2025-05-22 16:42:48,221 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    1434. 

  1434. 2025-05-22 16:42:48,246 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
    1435. 

  1435. 2025-05-22 16:42:48,249 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
    1436. 

  1436. 2025-05-22 16:42:48,289 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    1437. 

  1437. 2025-05-22 16:42:48,289 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    1438. 

  1438. 2025-05-22 16:42:48,291 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    1439. 

  1439. 2025-05-22 16:42:48,291 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    1440. 

  1440. 2025-05-22 16:42:48,291 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    1441. 

  1441. 2025-05-22 16:42:48,293 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    1442. 

  1442. 2025-05-22 16:42:50,221 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    1443. 

  1443. 2025-05-22 16:42:50,222 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    1444. 

  1444. 2025-05-22 16:42:50,251 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    1445. 

  1445. 2025-05-22 16:42:50,251 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    1446. 

  1446. 2025-05-22 16:43:27,237 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    1447. 

  1447. 2025-05-22 16:43:27,238 - tf_lstm.py - INFO - 训练集损失函数为：[8.9134e-01 3.1183e-01 9.6960e-02 2.7010e-02 6.9500e-03 1.9300e-03
    1448. 

  1448.  8.1000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04
    1449. 

  1449.  5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04
    1450. 

  1450.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    1451. 

  1451.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    1452. 

  1452.  5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1453. 

  1453.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1454. 

  1454.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1455. 

  1455.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1456. 

  1456.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1457. 

  1457.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1458. 

  1458.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1459. 

  1459.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1460. 

  1460.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1461. 

  1461.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    1462. 

  1462.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    1463. 

  1463.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training
    1464. 

  1464. 2025-05-22 16:43:27,238 - tf_lstm.py - INFO - 验证集损失函数为：[0.50446 0.16402 0.04786 0.01284 0.0036  0.00145 0.00101 0.00092 0.0009
    1465. 

  1465.  0.00089 0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087
    1466. 

  1466.  0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085
    1467. 

  1467.  0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084
    1468. 

  1468.  0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084
    1469. 

  1469.  0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083
    1470. 

  1470.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    1471. 

  1471.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    1472. 

  1472.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1473. 

  1473.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1474. 

  1474.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1475. 

  1475.  0.00082] - training
    1476. 

  1476. 2025-05-22 16:43:27,314 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    1477. 

  1477. 2025-05-22 16:43:27,314 - tf_lstm.py - INFO - 训练集损失函数为：[8.9997e-01 3.1582e-01 9.7960e-02 2.6850e-02 6.5100e-03 1.4400e-03
    1478. 

  1478.  3.2000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05
    1479. 

  1479.  6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05
    1480. 

  1480.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1481. 

  1481.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1482. 

  1482.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1483. 

  1483.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1484. 

  1484.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1485. 

  1485.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1486. 

  1486.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1487. 

  1487.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1488. 

  1488.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1489. 

  1489.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1490. 

  1490.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1491. 

  1491.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1492. 

  1492.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1493. 

  1493.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training
    1494. 

  1494. 2025-05-22 16:43:27,314 - tf_lstm.py - INFO - 验证集损失函数为：[5.1007e-01 1.6575e-01 4.7690e-02 1.2090e-02 2.7600e-03 6.1000e-04
    1495. 

  1495.  1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1496. 

  1496.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1497. 

  1497.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1498. 

  1498.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1499. 

  1499.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1500. 

  1500.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1501. 

  1501.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1502. 

  1502.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05
    1503. 

  1503.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1504. 

  1504.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1505. 

  1505.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1506. 

  1506.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1507. 

  1507.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1508. 

  1508.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1509. 

  1509.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1510. 

  1510.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
    1511. 

  1511. 2025-05-22 16:43:27,336 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ee3afa37ce5e300dffc58 - insert_trained_model_into_mongo
    1512. 

  1512. 2025-05-22 16:43:27,385 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ee3afa37ce5e300dffc5a - insert_scaler_model_into_mongo
    1513. 

  1513. 2025-05-22 16:43:27,429 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ee3afded77197474391d7 - insert_trained_model_into_mongo
    1514. 

  1514. 2025-05-22 16:43:27,441 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ee3afded77197474391d9 - insert_scaler_model_into_mongo
    1515. 

  1515. 2025-05-22 16:45:43,854 - task_worker.py - ERROR - Area 1002 failed: 'Datetime' - region_task
    1516. 

  1516. 2025-05-22 16:46:18,805 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1517. 

  1517.  - _tfmw_add_deprecation_warning
    1518. 

  1518. 2025-05-22 16:46:21,969 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    1519. 

  1519. 2025-05-22 16:46:46,365 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1520. 

  1520.  - _tfmw_add_deprecation_warning
    1521. 

  1521. 2025-05-22 16:47:14,361 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1522. 

  1522.  - _tfmw_add_deprecation_warning
    1523. 

  1523. 2025-05-22 16:47:14,373 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1524. 

  1524.  - _tfmw_add_deprecation_warning
    1525. 

  1525. 2025-05-22 16:47:15,097 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources
    1526. 

  1526. 2025-05-22 16:47:15,100 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
    1527. 

  1527. 2025-05-22 16:47:15,135 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    1528. 

  1528. 2025-05-22 16:47:15,136 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    1529. 

  1529. 2025-05-22 16:47:15,169 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
    1530. 

  1530. 2025-05-22 16:47:15,173 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
    1531. 

  1531. 2025-05-22 16:47:15,235 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    1532. 

  1532. 2025-05-22 16:47:15,235 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    1533. 

  1533. 2025-05-22 16:47:15,239 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    1534. 

  1534. 2025-05-22 16:47:15,241 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    1535. 

  1535. 2025-05-22 16:47:15,241 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    1536. 

  1536. 2025-05-22 16:47:15,244 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    1537. 

  1537. 2025-05-22 16:47:17,702 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    1538. 

  1538. 2025-05-22 16:47:17,703 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    1539. 

  1539. 2025-05-22 16:47:17,719 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    1540. 

  1540. 2025-05-22 16:47:17,720 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    1541. 

  1541. 2025-05-22 16:47:59,223 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    1542. 

  1542. 2025-05-22 16:47:59,223 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    1543. 

  1543. 2025-05-22 16:47:59,223 - tf_lstm.py - INFO - 训练集损失函数为：[9.0682e-01 3.1992e-01 1.0042e-01 2.8180e-02 7.2700e-03 2.0000e-03
    1544. 

  1544.  8.3000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04
    1545. 

  1545.  5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    1546. 

  1546.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    1547. 

  1547.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.2000e-04
    1548. 

  1548.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1549. 

  1549.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1550. 

  1550.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1551. 

  1551.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1552. 

  1552.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1553. 

  1553.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1554. 

  1554.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1555. 

  1555.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1556. 

  1556.  5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    1557. 

  1557.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    1558. 

  1558.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    1559. 

  1559.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training
    1560. 

  1560. 2025-05-22 16:47:59,223 - tf_lstm.py - INFO - 训练集损失函数为：[9.0271e-01 3.1676e-01 9.8510e-02 2.7120e-02 6.6000e-03 1.4600e-03
    1561. 

  1561.  3.3000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05
    1562. 

  1562.  6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05
    1563. 

  1563.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1564. 

  1564.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1565. 

  1565.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1566. 

  1566.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1567. 

  1567.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1568. 

  1568.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1569. 

  1569.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1570. 

  1570.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1571. 

  1571.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1572. 

  1572.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1573. 

  1573.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1574. 

  1574.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1575. 

  1575.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1576. 

  1576.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training
    1577. 

  1577. 2025-05-22 16:47:59,224 - tf_lstm.py - INFO - 验证集损失函数为：[0.51566 0.1692  0.04981 0.0134  0.00373 0.00148 0.00101 0.00092 0.0009
    1578. 

  1578.  0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087
    1579. 

  1579.  0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085 0.00085
    1580. 

  1580.  0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084 0.00084
    1581. 

  1581.  0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084
    1582. 

  1582.  0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    1583. 

  1583.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    1584. 

  1584.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082 0.00082 0.00082
    1585. 

  1585.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1586. 

  1586.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1587. 

  1587.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1588. 

  1588.  0.00082] - training
    1589. 

  1589. 2025-05-22 16:47:59,224 - tf_lstm.py - INFO - 验证集损失函数为：[5.1142e-01 1.6643e-01 4.8090e-02 1.2250e-02 2.8000e-03 6.2000e-04
    1590. 

  1590.  1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1591. 

  1591.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1592. 

  1592.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1593. 

  1593.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1594. 

  1594.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1595. 

  1595.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1596. 

  1596.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1597. 

  1597.  8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1598. 

  1598.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1599. 

  1599.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1600. 

  1600.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1601. 

  1601.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1602. 

  1602.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1603. 

  1603.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1604. 

  1604.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1605. 

  1605.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
    1606. 

  1606. 2025-05-22 16:47:59,336 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ee4bf35eeabaeac48dbca - insert_trained_model_into_mongo
    1607. 

  1607. 2025-05-22 16:47:59,349 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ee4bf35eeabaeac48dbcc - insert_scaler_model_into_mongo
    1608. 

  1608. 2025-05-22 16:47:59,372 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ee4bf9324837aabe55d44 - insert_scaler_model_into_mongo
    1609. 

  1609. 2025-05-22 16:51:53,965 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1610. 

  1610.  - _tfmw_add_deprecation_warning
    1611. 

  1611. 2025-05-22 16:51:54,086 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    1612. 

  1612. 2025-05-22 16:51:56,677 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1613. 

  1613.  - _tfmw_add_deprecation_warning
    1614. 

  1614. 2025-05-22 16:51:59,595 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1615. 

  1615.  - _tfmw_add_deprecation_warning
    1616. 

  1616. 2025-05-22 16:51:59,599 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1617. 

  1617.  - _tfmw_add_deprecation_warning
    1618. 

  1618. 2025-05-22 16:51:59,803 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources
    1619. 

  1619. 2025-05-22 16:51:59,821 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    1620. 

  1620. 2025-05-22 16:51:59,829 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
    1621. 

  1621. 2025-05-22 16:51:59,829 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
    1622. 

  1622. 2025-05-22 16:51:59,857 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    1623. 

  1623. 2025-05-22 16:51:59,857 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    1624. 

  1624. 2025-05-22 16:51:59,858 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    1625. 

  1625. 2025-05-22 16:51:59,858 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    1626. 

  1626. 2025-05-22 16:51:59,858 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    1627. 

  1627. 2025-05-22 16:51:59,859 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    1628. 

  1628. 2025-05-22 16:52:00,807 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    1629. 

  1629. 2025-05-22 16:52:00,807 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    1630. 

  1630. 2025-05-22 16:52:00,816 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    1631. 

  1631. 2025-05-22 16:52:00,816 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    1632. 

  1632. 2025-05-22 16:52:13,882 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    1633. 

  1633. 2025-05-22 16:52:13,883 - tf_lstm.py - INFO - 训练集损失函数为：[9.0366e-01 3.1743e-01 9.8920e-02 2.7540e-02 7.0700e-03 1.9500e-03
    1634. 

  1634.  8.2000e-04 6.0000e-04 5.5000e-04 5.5000e-04 5.4000e-04 5.4000e-04
    1635. 

  1635.  5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04
    1636. 

  1636.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    1637. 

  1637.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    1638. 

  1638.  5.3000e-04 5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1639. 

  1639.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1640. 

  1640.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1641. 

  1641.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1642. 

  1642.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1643. 

  1643.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1644. 

  1644.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1645. 

  1645.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1646. 

  1646.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1647. 

  1647.  5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    1648. 

  1648.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    1649. 

  1649.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training
    1650. 

  1650. 2025-05-22 16:52:13,883 - tf_lstm.py - INFO - 验证集损失函数为：[0.51271 0.16722 0.0488  0.01307 0.00364 0.00146 0.00101 0.00092 0.0009
    1651. 

  1651.  0.0009  0.00089 0.00089 0.00088 0.00088 0.00088 0.00088 0.00087 0.00087
    1652. 

  1652.  0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086
    1653. 

  1653.  0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085
    1654. 

  1654.  0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084
    1655. 

  1655.  0.00084 0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083
    1656. 

  1656.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    1657. 

  1657.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    1658. 

  1658.  0.00083 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1659. 

  1659.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1660. 

  1660.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1661. 

  1661.  0.00082] - training
    1662. 

  1662. 2025-05-22 16:52:13,924 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ee5bde140b01fd24f34ff - insert_trained_model_into_mongo
    1663. 

  1663. 2025-05-22 16:52:13,932 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ee5bde140b01fd24f3501 - insert_scaler_model_into_mongo
    1664. 

  1664. 2025-05-22 16:52:13,956 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    1665. 

  1665. 2025-05-22 16:52:13,957 - tf_lstm.py - INFO - 训练集损失函数为：[8.9291e-01 3.1420e-01 9.8070e-02 2.7090e-02 6.6100e-03 1.4600e-03
    1666. 

  1666.  3.3000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05
    1667. 

  1667.  6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1668. 

  1668.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1669. 

  1669.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1670. 

  1670.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1671. 

  1671.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1672. 

  1672.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1673. 

  1673.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1674. 

  1674.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1675. 

  1675.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1676. 

  1676.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1677. 

  1677.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1678. 

  1678.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1679. 

  1679.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1680. 

  1680.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1681. 

  1681.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training
    1682. 

  1682. 2025-05-22 16:52:13,957 - tf_lstm.py - INFO - 验证集损失函数为：[5.0656e-01 1.6547e-01 4.7970e-02 1.2260e-02 2.8100e-03 6.3000e-04
    1683. 

  1683.  1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1684. 

  1684.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1685. 

  1685.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1686. 

  1686.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1687. 

  1687.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1688. 

  1688.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1689. 

  1689.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1690. 

  1690.  8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1691. 

  1691.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1692. 

  1692.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1693. 

  1693.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1694. 

  1694.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1695. 

  1695.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1696. 

  1696.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1697. 

  1697.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1698. 

  1698.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
    1699. 

  1699. 2025-05-22 16:52:13,993 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ee5bdd1747a06d97e0ef3 - insert_trained_model_into_mongo
    1700. 

  1700. 2025-05-22 16:52:14,030 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ee5bed1747a06d97e0ef5 - insert_scaler_model_into_mongo
    1701. 

  1701. 2025-05-22 16:52:15,315 - task_worker.py - ERROR - Area 1002 failed: 'Datetime' - region_task
    1702. 

  1702. 2025-05-22 16:54:08,472 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1703. 

  1703.  - _tfmw_add_deprecation_warning
    1704. 

  1704. 2025-05-22 16:54:12,196 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    1705. 

  1705. 2025-05-22 16:54:36,162 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1706. 

  1706.  - _tfmw_add_deprecation_warning
    1707. 

  1707. 2025-05-22 16:55:09,151 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1708. 

  1708.  - _tfmw_add_deprecation_warning
    1709. 

  1709. 2025-05-22 16:55:09,153 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1710. 

  1710.  - _tfmw_add_deprecation_warning
    1711. 

  1711. 2025-05-22 16:55:09,901 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources
    1712. 

  1712. 2025-05-22 16:55:09,941 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    1713. 

  1713. 2025-05-22 16:55:09,941 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    1714. 

  1714. 2025-05-22 16:55:09,977 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
    1715. 

  1715. 2025-05-22 16:55:10,026 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    1716. 

  1716. 2025-05-22 16:55:10,027 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    1717. 

  1717. 2025-05-22 16:55:10,027 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    1718. 

  1718. 2025-05-22 16:55:10,027 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    1719. 

  1719. 2025-05-22 16:55:10,029 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    1720. 

  1720. 2025-05-22 16:55:10,029 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    1721. 

  1721. 2025-05-22 16:55:12,772 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    1722. 

  1722. 2025-05-22 16:55:12,773 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    1723. 

  1723. 2025-05-22 16:55:12,785 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    1724. 

  1724. 2025-05-22 16:55:12,785 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    1725. 

  1725. 2025-05-22 16:56:01,718 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    1726. 

  1726. 2025-05-22 16:56:01,720 - tf_lstm.py - INFO - 训练集损失函数为：[9.0185e-01 3.1664e-01 9.8400e-02 2.7020e-02 6.5600e-03 1.4500e-03
    1727. 

  1727.  3.2000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05
    1728. 

  1728.  6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1729. 

  1729.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1730. 

  1730.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1731. 

  1731.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1732. 

  1732.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1733. 

  1733.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1734. 

  1734.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1735. 

  1735.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1736. 

  1736.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1737. 

  1737.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1738. 

  1738.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1739. 

  1739.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1740. 

  1740.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1741. 

  1741.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1742. 

  1742.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training
    1743. 

  1743. 2025-05-22 16:56:01,720 - tf_lstm.py - INFO - 训练集损失函数为：[9.0446e-01 3.1836e-01 9.9350e-02 2.7650e-02 7.0800e-03 1.9400e-03
    1744. 

  1744.  8.1000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04
    1745. 

  1745.  5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    1746. 

  1746.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    1747. 

  1747.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    1748. 

  1748.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1749. 

  1749.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1750. 

  1750.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1751. 

  1751.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1752. 

  1752.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1753. 

  1753.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1754. 

  1754.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1755. 

  1755.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1756. 

  1756.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04
    1757. 

  1757.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    1758. 

  1758.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    1759. 

  1759.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training
    1760. 

  1760. 2025-05-22 16:56:01,720 - tf_lstm.py - INFO - 验证集损失函数为：[5.1114e-01 1.6636e-01 4.7960e-02 1.2180e-02 2.7800e-03 6.2000e-04
    1761. 

  1761.  1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1762. 

  1762.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1763. 

  1763.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1764. 

  1764.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1765. 

  1765.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1766. 

  1766.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1767. 

  1767.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05
    1768. 

  1768.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1769. 

  1769.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1770. 

  1770.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1771. 

  1771.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1772. 

  1772.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1773. 

  1773.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1774. 

  1774.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1775. 

  1775.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1776. 

  1776.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
    1777. 

  1777. 2025-05-22 16:56:01,720 - tf_lstm.py - INFO - 验证集损失函数为：[0.51384 0.16792 0.04904 0.0131  0.00363 0.00145 0.001   0.00092 0.0009
    1778. 

  1778.  0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087
    1779. 

  1779.  0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085
    1780. 

  1780.  0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084
    1781. 

  1781.  0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084
    1782. 

  1782.  0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    1783. 

  1783.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    1784. 

  1784.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082
    1785. 

  1785.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1786. 

  1786.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1787. 

  1787.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1788. 

  1788.  0.00082] - training
    1789. 

  1789. 2025-05-22 16:56:01,831 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ee6a10749b0ee0fe21ed9 - insert_trained_model_into_mongo
    1790. 

  1790. 2025-05-22 16:56:01,843 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ee6a10749b0ee0fe21edb - insert_scaler_model_into_mongo
    1791. 

  1791. 2025-05-22 16:56:01,864 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ee6a13f317b0620e79546 - insert_trained_model_into_mongo
    1792. 

  1792. 2025-05-22 16:56:01,899 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ee6a13f317b0620e79548 - insert_scaler_model_into_mongo
    1793. 

  1793. 2025-05-22 18:30:17,846 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1794. 

  1794.  - _tfmw_add_deprecation_warning
    1795. 

  1795. 2025-05-22 18:30:17,971 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    1796. 

  1796. 2025-05-22 18:30:20,914 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1797. 

  1797.  - _tfmw_add_deprecation_warning
    1798. 

  1798. 2025-05-22 18:30:24,052 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1799. 

  1799.  - _tfmw_add_deprecation_warning
    1800. 

  1800. 2025-05-22 18:30:24,054 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1801. 

  1801.  - _tfmw_add_deprecation_warning
    1802. 

  1802. 2025-05-22 18:30:24,282 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
    1803. 

  1803. 2025-05-22 18:30:24,299 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    1804. 

  1804. 2025-05-22 18:30:24,300 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    1805. 

  1805. 2025-05-22 18:30:24,308 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
    1806. 

  1806. 2025-05-22 18:30:24,308 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
    1807. 

  1807. 2025-05-22 18:30:24,335 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    1808. 

  1808. 2025-05-22 18:30:24,335 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    1809. 

  1809. 2025-05-22 18:30:24,335 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    1810. 

  1810. 2025-05-22 18:30:24,335 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    1811. 

  1811. 2025-05-22 18:30:24,336 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    1812. 

  1812. 2025-05-22 18:30:24,336 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    1813. 

  1813. 2025-05-22 18:30:25,304 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    1814. 

  1814. 2025-05-22 18:30:25,304 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    1815. 

  1815. 2025-05-22 18:30:25,304 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    1816. 

  1816. 2025-05-22 18:30:25,305 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    1817. 

  1817. 2025-05-22 18:30:39,377 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    1818. 

  1818. 2025-05-22 18:30:39,377 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    1819. 

  1819. 2025-05-22 18:30:39,377 - tf_lstm.py - INFO - 训练集损失函数为：[9.0965e-01 3.2084e-01 1.0057e-01 2.8150e-02 7.2500e-03 1.9900e-03
    1820. 

  1820.  8.3000e-04 6.0000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04
    1821. 

  1821.  5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04
    1822. 

  1822.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    1823. 

  1823.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    1824. 

  1824.  5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1825. 

  1825.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1826. 

  1826.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1827. 

  1827.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1828. 

  1828.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1829. 

  1829.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1830. 

  1830.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1831. 

  1831.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1832. 

  1832.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04
    1833. 

  1833.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    1834. 

  1834.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    1835. 

  1835.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training
    1836. 

  1836. 2025-05-22 18:30:39,377 - tf_lstm.py - INFO - 训练集损失函数为：[9.0351e-01 3.1783e-01 9.9160e-02 2.7360e-02 6.6700e-03 1.4700e-03
    1837. 

  1837.  3.3000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05
    1838. 

  1838.  6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05
    1839. 

  1839.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1840. 

  1840.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1841. 

  1841.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1842. 

  1842.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1843. 

  1843.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1844. 

  1844.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1845. 

  1845.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1846. 

  1846.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1847. 

  1847.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1848. 

  1848.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1849. 

  1849.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1850. 

  1850.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1851. 

  1851.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    1852. 

  1852.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training
    1853. 

  1853. 2025-05-22 18:30:39,378 - tf_lstm.py - INFO - 验证集损失函数为：[0.51728 0.16966 0.04982 0.01337 0.00372 0.00148 0.00101 0.00092 0.0009
    1854. 

  1854.  0.00089 0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087
    1855. 

  1855.  0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085
    1856. 

  1856.  0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084
    1857. 

  1857.  0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084
    1858. 

  1858.  0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    1859. 

  1859.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    1860. 

  1860.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082
    1861. 

  1861.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1862. 

  1862.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1863. 

  1863.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    1864. 

  1864.  0.00082] - training
    1865. 

  1865. 2025-05-22 18:30:39,378 - tf_lstm.py - INFO - 验证集损失函数为：[5.1250e-01 1.6734e-01 4.8480e-02 1.2370e-02 2.8300e-03 6.3000e-04
    1866. 

  1866.  1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1867. 

  1867.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1868. 

  1868.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1869. 

  1869.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1870. 

  1870.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1871. 

  1871.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1872. 

  1872.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1873. 

  1873.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05
    1874. 

  1874.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1875. 

  1875.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1876. 

  1876.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1877. 

  1877.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1878. 

  1878.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1879. 

  1879.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1880. 

  1880.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1881. 

  1881.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
    1882. 

  1882. 2025-05-22 18:30:39,422 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682efccfd9cdd24a561b9430 - insert_trained_model_into_mongo
    1883. 

  1883. 2025-05-22 18:30:39,444 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682efccf11d24a1b0567e1cd - insert_trained_model_into_mongo
    1884. 

  1884. 2025-05-22 18:30:39,447 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682efccfd9cdd24a561b9432 - insert_scaler_model_into_mongo
    1885. 

  1885. 2025-05-22 18:30:39,452 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682efccf11d24a1b0567e1cf - insert_scaler_model_into_mongo
    1886. 

  1886. 2025-05-22 18:35:43,479 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1887. 

  1887.  - _tfmw_add_deprecation_warning
    1888. 

  1888. 2025-05-22 18:35:43,599 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    1889. 

  1889. 2025-05-22 18:35:46,348 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1890. 

  1890.  - _tfmw_add_deprecation_warning
    1891. 

  1891. 2025-05-22 18:35:49,304 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1892. 

  1892.  - _tfmw_add_deprecation_warning
    1893. 

  1893. 2025-05-22 18:35:49,313 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    1894. 

  1894.  - _tfmw_add_deprecation_warning
    1895. 

  1895. 2025-05-22 18:35:49,509 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources
    1896. 

  1896. 2025-05-22 18:35:49,518 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
    1897. 

  1897. 2025-05-22 18:35:49,527 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    1898. 

  1898. 2025-05-22 18:35:49,535 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
    1899. 

  1899. 2025-05-22 18:35:49,536 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    1900. 

  1900. 2025-05-22 18:35:49,545 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
    1901. 

  1901. 2025-05-22 18:35:49,563 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    1902. 

  1902. 2025-05-22 18:35:49,563 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    1903. 

  1903. 2025-05-22 18:35:49,564 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    1904. 

  1904. 2025-05-22 18:35:49,572 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
    1905. 

  1905. 2025-05-22 18:35:49,572 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    1906. 

  1906. 2025-05-22 18:35:49,573 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    1907. 

  1907. 2025-05-22 18:35:50,543 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    1908. 

  1908. 2025-05-22 18:35:50,543 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    1909. 

  1909. 2025-05-22 18:35:50,543 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    1910. 

  1910. 2025-05-22 18:35:50,543 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    1911. 

  1911. 2025-05-22 18:36:05,459 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    1912. 

  1912. 2025-05-22 18:36:05,459 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    1913. 

  1913. 2025-05-22 18:36:05,460 - tf_lstm.py - INFO - 训练集损失函数为：[9.0471e-01 3.1927e-01 1.0005e-01 2.7970e-02 7.1900e-03 1.9800e-03
    1914. 

  1914.  8.3000e-04 6.0000e-04 5.5000e-04 5.5000e-04 5.4000e-04 5.4000e-04
    1915. 

  1915.  5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04
    1916. 

  1916.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    1917. 

  1917.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    1918. 

  1918.  5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1919. 

  1919.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1920. 

  1920.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1921. 

  1921.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1922. 

  1922.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1923. 

  1923.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1924. 

  1924.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1925. 

  1925.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    1926. 

  1926.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04
    1927. 

  1927.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    1928. 

  1928.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    1929. 

  1929.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training
    1930. 

  1930. 2025-05-22 18:36:05,460 - tf_lstm.py - INFO - 验证集损失函数为：[5.1764e-01 1.6935e-01 4.9040e-02 1.2470e-02 2.8400e-03 6.3000e-04
    1931. 

  1931.  1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1932. 

  1932.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1933. 

  1933.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1934. 

  1934.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1935. 

  1935.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1936. 

  1936.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1937. 

  1937.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1938. 

  1938.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    1939. 

  1939.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1940. 

  1940.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1941. 

  1941.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1942. 

  1942.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1943. 

  1943.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1944. 

  1944.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1945. 

  1945.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    1946. 

  1946.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
    1947. 

  1947. 2025-05-22 18:36:05,514 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682efe15d416e8f832fd0f55 - insert_trained_model_into_mongo
    1948. 

  1948. 2025-05-22 18:36:05,514 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682efe155dc17e5973916862 - insert_trained_model_into_mongo
    1949. 

  1949. 2025-05-22 18:36:05,546 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682efe155dc17e5973916864 - insert_scaler_model_into_mongo
    1950. 

  1950. 2025-05-22 18:36:05,561 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682efe15d416e8f832fd0f57 - insert_scaler_model_into_mongo
    1951. 

  1951. 2025-05-22 18:36:06,898 - task_worker.py - ERROR - Area 1002 failed: 'types.SimpleNamespace' object has no attribute 'cap' - region_task
    1952.