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south-forecast.2025-05-22.0.log

south-forecast.2025-05-22.0.log 115 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.