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south-forecast.2025-04-03.0.log

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  1. 2025-04-03 09:26:58,615 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
    2. 

  2.  - _tfmw_add_deprecation_warning
    3. 

  3. 2025-04-03 09:26:58,902 - tf_fmi_train.py - INFO - Program starts execution! - model_training
    4. 

  4. 2025-04-03 09:26:58,958 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    5. 

  5. 2025-04-03 09:26:58,966 - data_cleaning.py - INFO - 列清洗：清洗的列有：{'HubDireciton', 'HubSpeed'} - data_column_cleaning
    6. 

  6. 2025-04-03 09:26:58,984 - data_cleaning.py - INFO - 行清洗：清洗的行数有：881，缺失的列有： - key_field_row_cleaning
    7. 

  7. 2025-04-03 09:26:58,997 - data_handler.py - INFO - 2022-05-05 23:45:00 ~ 2022-05-09 00:00:00 - missing_time_splite
    8. 

  8. 2025-04-03 09:26:58,998 - data_handler.py - INFO - 缺失点数：288.0 - missing_time_splite
    9. 

  9. 2025-04-03 09:26:59,031 - data_handler.py - INFO - 2022-06-25 07:45:00 ~ 2022-06-25 08:15:00 - missing_time_splite
    10. 

  10. 2025-04-03 09:26:59,031 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
    11. 

  11. 2025-04-03 09:26:59,031 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
    12. 

  12. 2025-04-03 09:26:59,033 - data_handler.py - INFO - 2022-06-27 06:45:00 ~ 2022-06-27 07:15:00 - missing_time_splite
    13. 

  13. 2025-04-03 09:26:59,033 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
    14. 

  14. 2025-04-03 09:26:59,033 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
    15. 

  15. 2025-04-03 09:26:59,036 - data_handler.py - INFO - 2022-07-01 06:30:00 ~ 2022-07-01 23:45:00 - missing_time_splite
    16. 

  16. 2025-04-03 09:26:59,037 - data_handler.py - INFO - 缺失点数：68.0 - missing_time_splite
    17. 

  17. 2025-04-03 09:26:59,042 - data_handler.py - INFO - 2022-07-08 06:15:00 ~ 2022-07-08 23:45:00 - missing_time_splite
    18. 

  18. 2025-04-03 09:26:59,042 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    19. 

  19. 2025-04-03 09:26:59,043 - data_handler.py - INFO - 2022-07-09 06:15:00 ~ 2022-07-09 23:45:00 - missing_time_splite
    20. 

  20. 2025-04-03 09:26:59,043 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    21. 

  21. 2025-04-03 09:26:59,044 - data_handler.py - INFO - 2022-07-10 06:15:00 ~ 2022-07-10 23:45:00 - missing_time_splite
    22. 

  22. 2025-04-03 09:26:59,044 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    23. 

  23. 2025-04-03 09:26:59,045 - data_handler.py - INFO - 2022-07-11 06:00:00 ~ 2022-07-11 23:45:00 - missing_time_splite
    24. 

  24. 2025-04-03 09:26:59,046 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    25. 

  25. 2025-04-03 09:26:59,046 - data_handler.py - INFO - 2022-07-12 06:00:00 ~ 2022-07-12 23:45:00 - missing_time_splite
    26. 

  26. 2025-04-03 09:26:59,046 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    27. 

  27. 2025-04-03 09:26:59,047 - data_handler.py - INFO - 2022-07-13 06:00:00 ~ 2022-07-13 23:45:00 - missing_time_splite
    28. 

  28. 2025-04-03 09:26:59,048 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    29. 

  29. 2025-04-03 09:26:59,048 - data_handler.py - INFO - 2022-07-14 23:00:00 ~ 2022-07-14 23:45:00 - missing_time_splite
    30. 

  30. 2025-04-03 09:26:59,049 - data_handler.py - INFO - 缺失点数：2.0 - missing_time_splite
    31. 

  31. 2025-04-03 09:26:59,049 - data_handler.py - INFO - 需要补值的点数：2.0 - missing_time_splite
    32. 

  32. 2025-04-03 09:26:59,062 - data_handler.py - INFO - 2022-08-01 20:00:00 ~ 2022-08-01 23:45:00 - missing_time_splite
    33. 

  33. 2025-04-03 09:26:59,062 - data_handler.py - INFO - 缺失点数：14.0 - missing_time_splite
    34. 

  34. 2025-04-03 09:26:59,063 - data_handler.py - INFO - 2022-08-02 21:00:00 ~ 2022-08-02 23:45:00 - missing_time_splite
    35. 

  35. 2025-04-03 09:26:59,063 - data_handler.py - INFO - 缺失点数：10.0 - missing_time_splite
    36. 

  36. 2025-04-03 09:26:59,065 - data_handler.py - INFO - 2022-08-04 00:00:00 ~ 2022-08-04 23:15:00 - missing_time_splite
    37. 

  37. 2025-04-03 09:26:59,065 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    38. 

  38. 2025-04-03 09:26:59,066 - data_handler.py - INFO - 2022-08-05 00:00:00 ~ 2022-08-05 23:15:00 - missing_time_splite
    39. 

  39. 2025-04-03 09:26:59,066 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    40. 

  40. 2025-04-03 09:26:59,066 - data_handler.py - INFO - 2022-08-06 00:00:00 ~ 2022-08-06 23:15:00 - missing_time_splite
    41. 

  41. 2025-04-03 09:26:59,066 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    42. 

  42. 2025-04-03 09:26:59,067 - data_handler.py - INFO - 2022-08-07 00:00:00 ~ 2022-08-07 23:15:00 - missing_time_splite
    43. 

  43. 2025-04-03 09:26:59,068 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    44. 

  44. 2025-04-03 09:26:59,068 - data_handler.py - INFO - 数据总数：8207, 时序缺失的间隔：3, 其中，较长的时间间隔：14 - missing_time_splite
    45. 

  45. 2025-04-03 09:26:59,068 - data_handler.py - INFO - 需要补值的总点数：4.0 - missing_time_splite
    46. 

  46. 2025-04-03 09:26:59,070 - data_handler.py - INFO - 再次测算，需要插值的总点数为：4.0 - fill_train_data
    47. 

  47. 2025-04-03 09:26:59,075 - data_handler.py - INFO - 2022-05-02 08:00:00 ~ 2022-05-05 23:45:00 有 352 个点, 填补 0 个点. - data_fill
    48. 

  48. 2025-04-03 09:26:59,079 - data_handler.py - INFO - 2022-05-09 00:00:00 ~ 2022-07-01 06:30:00 有 5115 个点, 填补 2 个点. - data_fill
    49. 

  49. 2025-04-03 09:26:59,081 - data_handler.py - INFO - 2022-07-01 23:45:00 ~ 2022-07-08 06:15:00 有 603 个点, 填补 0 个点. - data_fill
    50. 

  50. 2025-04-03 09:26:59,082 - data_handler.py - INFO - 2022-07-08 23:45:00 ~ 2022-07-09 06:15:00 有 27 个点, 填补 0 个点. - data_fill
    51. 

  51. 2025-04-03 09:26:59,083 - data_handler.py - INFO - 2022-07-09 23:45:00 ~ 2022-07-10 06:15:00 有 27 个点, 填补 0 个点. - data_fill
    52. 

  52. 2025-04-03 09:26:59,084 - data_handler.py - INFO - 2022-07-10 23:45:00 ~ 2022-07-11 06:00:00 有 26 个点, 填补 0 个点. - data_fill
    53. 

  53. 2025-04-03 09:26:59,085 - data_handler.py - INFO - 2022-07-11 23:45:00 ~ 2022-07-12 06:00:00 有 26 个点, 填补 0 个点. - data_fill
    54. 

  54. 2025-04-03 09:26:59,086 - data_handler.py - INFO - 2022-07-12 23:45:00 ~ 2022-07-13 06:00:00 有 26 个点, 填补 0 个点. - data_fill
    55. 

  55. 2025-04-03 09:26:59,088 - data_handler.py - INFO - 2022-07-13 23:45:00 ~ 2022-08-01 20:00:00 有 1810 个点, 填补 2 个点. - data_fill
    56. 

  56. 2025-04-03 09:26:59,089 - data_handler.py - INFO - 2022-08-01 23:45:00 ~ 2022-08-02 21:00:00 有 86 个点, 填补 0 个点. - data_fill
    57. 

  57. 2025-04-03 09:26:59,090 - data_handler.py - INFO - 2022-08-02 23:45:00 ~ 2022-08-04 00:00:00 有 98 个点, 填补 0 个点. - data_fill
    58. 

  58. 2025-04-03 09:26:59,091 - data_handler.py - INFO - 2022-08-04 23:15:00 ~ 2022-08-05 00:00:00 有 4 个点, 填补 0 个点. - data_fill
    59. 

  59. 2025-04-03 09:26:59,092 - data_handler.py - INFO - 2022-08-05 23:15:00 ~ 2022-08-06 00:00:00 有 4 个点, 填补 0 个点. - data_fill
    60. 

  60. 2025-04-03 09:26:59,093 - data_handler.py - INFO - 2022-08-06 23:15:00 ~ 2022-08-07 00:00:00 有 4 个点, 填补 0 个点. - data_fill
    61. 

  61. 2025-04-03 09:26:59,094 - data_handler.py - INFO - 2022-08-07 23:15:00 ~ 2022-08-07 23:45:00 有 3 个点, 填补 0 个点. - data_fill
    62. 

  62. 2025-04-03 09:26:59,094 - data_handler.py - INFO - 训练集分成了15段，实际一共补值4点 - data_fill
    63. 

  63. 2025-04-03 09:27:01,585 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    64. 

  64. 2025-04-03 09:27:01,586 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    65. 

  65. 2025-04-03 09:27:01,586 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    66. 

  66. 2025-04-03 09:27:01,586 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    67. 

  67. 2025-04-03 09:27:01,586 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    68. 

  68. 2025-04-03 09:27:01,586 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    69. 

  69. 2025-04-03 09:27:01,586 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    70. 

  70. 2025-04-03 09:27:01,586 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    71. 

  71. 2025-04-03 09:27:01,919 - dbmg.py - INFO - ❌ 系统异常: Authentication failed., full error: {'ok': 0.0, 'errmsg': 'Authentication failed.', 'code': 18, 'codeName': 'AuthenticationFailed'} - get_keras_model_from_mongo
    72. 

  72. 2025-04-03 09:27:01,920 - tf_fmi.py - INFO - 加强训练加载模型权重失败:("Authentication failed., full error: {'ok': 0.0, 'errmsg': 'Authentication failed.', 'code': 18, 'codeName': 'AuthenticationFailed'}",) - train_init
    73. 

  73. 2025-04-03 09:27:14,119 - tf_fmi.py - INFO - -----模型训练经过31轮迭代----- - training
    74. 

  74. 2025-04-03 09:27:14,120 - tf_fmi.py - INFO - 训练集损失函数为：[53.51689 30.66457 18.66789 11.65896  7.50273  4.84363  3.09119  1.95949
    75. 

  75.   1.25144  0.82907  0.58984  0.46101  0.39091  0.3534   0.33623  0.32777
    76. 

  76.   0.31812  0.31297  0.30957  0.30708  0.30657  0.30585  0.30357  0.30433
    77. 

  77.   0.30124  0.30502  0.29952  0.30102  0.30003  0.29883  0.30341] - training
    78. 

  78. 2025-04-03 09:27:14,120 - tf_fmi.py - INFO - 验证集损失函数为：[33.29909 22.46415 14.20688  9.14478  5.98742  3.91006  2.55501  1.67482
    79. 

  79.   1.12169  0.7885   0.59235  0.48276  0.42948  0.41301  0.37812  0.35537
    80. 

  80.   0.35367  0.35108  0.34602  0.34273  0.33807  0.34047  0.33835  0.33889
    81. 

  81.   0.34738  0.34364  0.34219  0.34872  0.34872  0.34948  0.3491 ] - training
    82. 

  82. 2025-04-03 09:27:14,206 - dbmg.py - INFO - ❌ 数据库操作 - 详细错误: Authentication failed., full error: {'ok': 0.0, 'errmsg': 'Authentication failed.', 'code': 18, 'codeName': 'AuthenticationFailed'} - insert_trained_model_into_mongo
    83. 

  83. 2025-04-03 09:29:33,755 - 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.
    84. 

  84.  - _tfmw_add_deprecation_warning
    85. 

  85. 2025-04-03 09:29:33,856 - tf_fmi_train.py - INFO - Program starts execution! - model_training
    86. 

  86. 2025-04-03 09:29:33,912 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    87. 

  87. 2025-04-03 09:29:33,921 - data_cleaning.py - INFO - 列清洗：清洗的列有：{'HubSpeed', 'HubDireciton'} - data_column_cleaning
    88. 

  88. 2025-04-03 09:29:33,936 - data_cleaning.py - INFO - 行清洗：清洗的行数有：881，缺失的列有： - key_field_row_cleaning
    89. 

  89. 2025-04-03 09:29:33,946 - data_handler.py - INFO - 2022-05-05 23:45:00 ~ 2022-05-09 00:00:00 - missing_time_splite
    90. 

  90. 2025-04-03 09:29:33,946 - data_handler.py - INFO - 缺失点数：288.0 - missing_time_splite
    91. 

  91. 2025-04-03 09:29:33,980 - data_handler.py - INFO - 2022-06-25 07:45:00 ~ 2022-06-25 08:15:00 - missing_time_splite
    92. 

  92. 2025-04-03 09:29:33,981 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
    93. 

  93. 2025-04-03 09:29:33,981 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
    94. 

  94. 2025-04-03 09:29:33,982 - data_handler.py - INFO - 2022-06-27 06:45:00 ~ 2022-06-27 07:15:00 - missing_time_splite
    95. 

  95. 2025-04-03 09:29:33,982 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
    96. 

  96. 2025-04-03 09:29:33,982 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
    97. 

  97. 2025-04-03 09:29:33,986 - data_handler.py - INFO - 2022-07-01 06:30:00 ~ 2022-07-01 23:45:00 - missing_time_splite
    98. 

  98. 2025-04-03 09:29:33,986 - data_handler.py - INFO - 缺失点数：68.0 - missing_time_splite
    99. 

  99. 2025-04-03 09:29:33,991 - data_handler.py - INFO - 2022-07-08 06:15:00 ~ 2022-07-08 23:45:00 - missing_time_splite
    100. 

  100. 2025-04-03 09:29:33,991 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    101. 

  101. 2025-04-03 09:29:33,992 - data_handler.py - INFO - 2022-07-09 06:15:00 ~ 2022-07-09 23:45:00 - missing_time_splite
    102. 

  102. 2025-04-03 09:29:33,992 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    103. 

  103. 2025-04-03 09:29:33,993 - data_handler.py - INFO - 2022-07-10 06:15:00 ~ 2022-07-10 23:45:00 - missing_time_splite
    104. 

  104. 2025-04-03 09:29:33,993 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    105. 

  105. 2025-04-03 09:29:33,994 - data_handler.py - INFO - 2022-07-11 06:00:00 ~ 2022-07-11 23:45:00 - missing_time_splite
    106. 

  106. 2025-04-03 09:29:33,994 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    107. 

  107. 2025-04-03 09:29:33,995 - data_handler.py - INFO - 2022-07-12 06:00:00 ~ 2022-07-12 23:45:00 - missing_time_splite
    108. 

  108. 2025-04-03 09:29:33,995 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    109. 

  109. 2025-04-03 09:29:33,996 - data_handler.py - INFO - 2022-07-13 06:00:00 ~ 2022-07-13 23:45:00 - missing_time_splite
    110. 

  110. 2025-04-03 09:29:33,996 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    111. 

  111. 2025-04-03 09:29:33,997 - data_handler.py - INFO - 2022-07-14 23:00:00 ~ 2022-07-14 23:45:00 - missing_time_splite
    112. 

  112. 2025-04-03 09:29:33,997 - data_handler.py - INFO - 缺失点数：2.0 - missing_time_splite
    113. 

  113. 2025-04-03 09:29:33,997 - data_handler.py - INFO - 需要补值的点数：2.0 - missing_time_splite
    114. 

  114. 2025-04-03 09:29:34,011 - data_handler.py - INFO - 2022-08-01 20:00:00 ~ 2022-08-01 23:45:00 - missing_time_splite
    115. 

  115. 2025-04-03 09:29:34,011 - data_handler.py - INFO - 缺失点数：14.0 - missing_time_splite
    116. 

  116. 2025-04-03 09:29:34,013 - data_handler.py - INFO - 2022-08-02 21:00:00 ~ 2022-08-02 23:45:00 - missing_time_splite
    117. 

  117. 2025-04-03 09:29:34,013 - data_handler.py - INFO - 缺失点数：10.0 - missing_time_splite
    118. 

  118. 2025-04-03 09:29:34,014 - data_handler.py - INFO - 2022-08-04 00:00:00 ~ 2022-08-04 23:15:00 - missing_time_splite
    119. 

  119. 2025-04-03 09:29:34,015 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    120. 

  120. 2025-04-03 09:29:34,016 - data_handler.py - INFO - 2022-08-05 00:00:00 ~ 2022-08-05 23:15:00 - missing_time_splite
    121. 

  121. 2025-04-03 09:29:34,016 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    122. 

  122. 2025-04-03 09:29:34,016 - data_handler.py - INFO - 2022-08-06 00:00:00 ~ 2022-08-06 23:15:00 - missing_time_splite
    123. 

  123. 2025-04-03 09:29:34,016 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    124. 

  124. 2025-04-03 09:29:34,017 - data_handler.py - INFO - 2022-08-07 00:00:00 ~ 2022-08-07 23:15:00 - missing_time_splite
    125. 

  125. 2025-04-03 09:29:34,017 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    126. 

  126. 2025-04-03 09:29:34,018 - data_handler.py - INFO - 数据总数：8207, 时序缺失的间隔：3, 其中，较长的时间间隔：14 - missing_time_splite
    127. 

  127. 2025-04-03 09:29:34,018 - data_handler.py - INFO - 需要补值的总点数：4.0 - missing_time_splite
    128. 

  128. 2025-04-03 09:29:34,020 - data_handler.py - INFO - 再次测算，需要插值的总点数为：4.0 - fill_train_data
    129. 

  129. 2025-04-03 09:29:34,021 - data_handler.py - INFO - 2022-05-02 08:00:00 ~ 2022-05-05 23:45:00 有 352 个点, 填补 0 个点. - data_fill
    130. 

  130. 2025-04-03 09:29:34,024 - data_handler.py - INFO - 2022-05-09 00:00:00 ~ 2022-07-01 06:30:00 有 5115 个点, 填补 2 个点. - data_fill
    131. 

  131. 2025-04-03 09:29:34,026 - data_handler.py - INFO - 2022-07-01 23:45:00 ~ 2022-07-08 06:15:00 有 603 个点, 填补 0 个点. - data_fill
    132. 

  132. 2025-04-03 09:29:34,026 - data_handler.py - INFO - 2022-07-08 23:45:00 ~ 2022-07-09 06:15:00 有 27 个点, 填补 0 个点. - data_fill
    133. 

  133. 2025-04-03 09:29:34,028 - data_handler.py - INFO - 2022-07-09 23:45:00 ~ 2022-07-10 06:15:00 有 27 个点, 填补 0 个点. - data_fill
    134. 

  134. 2025-04-03 09:29:34,028 - data_handler.py - INFO - 2022-07-10 23:45:00 ~ 2022-07-11 06:00:00 有 26 个点, 填补 0 个点. - data_fill
    135. 

  135. 2025-04-03 09:29:34,030 - data_handler.py - INFO - 2022-07-11 23:45:00 ~ 2022-07-12 06:00:00 有 26 个点, 填补 0 个点. - data_fill
    136. 

  136. 2025-04-03 09:29:34,031 - data_handler.py - INFO - 2022-07-12 23:45:00 ~ 2022-07-13 06:00:00 有 26 个点, 填补 0 个点. - data_fill
    137. 

  137. 2025-04-03 09:29:34,033 - data_handler.py - INFO - 2022-07-13 23:45:00 ~ 2022-08-01 20:00:00 有 1810 个点, 填补 2 个点. - data_fill
    138. 

  138. 2025-04-03 09:29:34,034 - data_handler.py - INFO - 2022-08-01 23:45:00 ~ 2022-08-02 21:00:00 有 86 个点, 填补 0 个点. - data_fill
    139. 

  139. 2025-04-03 09:29:34,035 - data_handler.py - INFO - 2022-08-02 23:45:00 ~ 2022-08-04 00:00:00 有 98 个点, 填补 0 个点. - data_fill
    140. 

  140. 2025-04-03 09:29:34,036 - data_handler.py - INFO - 2022-08-04 23:15:00 ~ 2022-08-05 00:00:00 有 4 个点, 填补 0 个点. - data_fill
    141. 

  141. 2025-04-03 09:29:34,037 - data_handler.py - INFO - 2022-08-05 23:15:00 ~ 2022-08-06 00:00:00 有 4 个点, 填补 0 个点. - data_fill
    142. 

  142. 2025-04-03 09:29:34,038 - data_handler.py - INFO - 2022-08-06 23:15:00 ~ 2022-08-07 00:00:00 有 4 个点, 填补 0 个点. - data_fill
    143. 

  143. 2025-04-03 09:29:34,039 - data_handler.py - INFO - 2022-08-07 23:15:00 ~ 2022-08-07 23:45:00 有 3 个点, 填补 0 个点. - data_fill
    144. 

  144. 2025-04-03 09:29:34,039 - data_handler.py - INFO - 训练集分成了15段，实际一共补值4点 - data_fill
    145. 

  145. 2025-04-03 09:29:53,779 - 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.
    146. 

  146.  - _tfmw_add_deprecation_warning
    147. 

  147. 2025-04-03 09:29:53,878 - tf_fmi_train.py - INFO - Program starts execution! - model_training
    148. 

  148. 2025-04-03 09:29:53,935 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    149. 

  149. 2025-04-03 09:29:53,944 - data_cleaning.py - INFO - 列清洗：清洗的列有：{'HubSpeed', 'HubDireciton'} - data_column_cleaning
    150. 

  150. 2025-04-03 09:29:53,959 - data_cleaning.py - INFO - 行清洗：清洗的行数有：881，缺失的列有： - key_field_row_cleaning
    151. 

  151. 2025-04-03 09:29:53,969 - data_handler.py - INFO - 2022-05-05 23:45:00 ~ 2022-05-09 00:00:00 - missing_time_splite
    152. 

  152. 2025-04-03 09:29:53,969 - data_handler.py - INFO - 缺失点数：288.0 - missing_time_splite
    153. 

  153. 2025-04-03 09:29:54,003 - data_handler.py - INFO - 2022-06-25 07:45:00 ~ 2022-06-25 08:15:00 - missing_time_splite
    154. 

  154. 2025-04-03 09:29:54,003 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
    155. 

  155. 2025-04-03 09:29:54,003 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
    156. 

  156. 2025-04-03 09:29:54,005 - data_handler.py - INFO - 2022-06-27 06:45:00 ~ 2022-06-27 07:15:00 - missing_time_splite
    157. 

  157. 2025-04-03 09:29:54,005 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
    158. 

  158. 2025-04-03 09:29:54,005 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
    159. 

  159. 2025-04-03 09:29:54,009 - data_handler.py - INFO - 2022-07-01 06:30:00 ~ 2022-07-01 23:45:00 - missing_time_splite
    160. 

  160. 2025-04-03 09:29:54,009 - data_handler.py - INFO - 缺失点数：68.0 - missing_time_splite
    161. 

  161. 2025-04-03 09:29:54,014 - data_handler.py - INFO - 2022-07-08 06:15:00 ~ 2022-07-08 23:45:00 - missing_time_splite
    162. 

  162. 2025-04-03 09:29:54,014 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    163. 

  163. 2025-04-03 09:29:54,016 - data_handler.py - INFO - 2022-07-09 06:15:00 ~ 2022-07-09 23:45:00 - missing_time_splite
    164. 

  164. 2025-04-03 09:29:54,016 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    165. 

  165. 2025-04-03 09:29:54,017 - data_handler.py - INFO - 2022-07-10 06:15:00 ~ 2022-07-10 23:45:00 - missing_time_splite
    166. 

  166. 2025-04-03 09:29:54,017 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    167. 

  167. 2025-04-03 09:29:54,018 - data_handler.py - INFO - 2022-07-11 06:00:00 ~ 2022-07-11 23:45:00 - missing_time_splite
    168. 

  168. 2025-04-03 09:29:54,018 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    169. 

  169. 2025-04-03 09:29:54,019 - data_handler.py - INFO - 2022-07-12 06:00:00 ~ 2022-07-12 23:45:00 - missing_time_splite
    170. 

  170. 2025-04-03 09:29:54,019 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    171. 

  171. 2025-04-03 09:29:54,020 - data_handler.py - INFO - 2022-07-13 06:00:00 ~ 2022-07-13 23:45:00 - missing_time_splite
    172. 

  172. 2025-04-03 09:29:54,020 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    173. 

  173. 2025-04-03 09:29:54,021 - data_handler.py - INFO - 2022-07-14 23:00:00 ~ 2022-07-14 23:45:00 - missing_time_splite
    174. 

  174. 2025-04-03 09:29:54,021 - data_handler.py - INFO - 缺失点数：2.0 - missing_time_splite
    175. 

  175. 2025-04-03 09:29:54,021 - data_handler.py - INFO - 需要补值的点数：2.0 - missing_time_splite
    176. 

  176. 2025-04-03 09:29:54,035 - data_handler.py - INFO - 2022-08-01 20:00:00 ~ 2022-08-01 23:45:00 - missing_time_splite
    177. 

  177. 2025-04-03 09:29:54,035 - data_handler.py - INFO - 缺失点数：14.0 - missing_time_splite
    178. 

  178. 2025-04-03 09:29:54,036 - data_handler.py - INFO - 2022-08-02 21:00:00 ~ 2022-08-02 23:45:00 - missing_time_splite
    179. 

  179. 2025-04-03 09:29:54,036 - data_handler.py - INFO - 缺失点数：10.0 - missing_time_splite
    180. 

  180. 2025-04-03 09:29:54,038 - data_handler.py - INFO - 2022-08-04 00:00:00 ~ 2022-08-04 23:15:00 - missing_time_splite
    181. 

  181. 2025-04-03 09:29:54,039 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    182. 

  182. 2025-04-03 09:29:54,039 - data_handler.py - INFO - 2022-08-05 00:00:00 ~ 2022-08-05 23:15:00 - missing_time_splite
    183. 

  183. 2025-04-03 09:29:54,040 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    184. 

  184. 2025-04-03 09:29:54,040 - data_handler.py - INFO - 2022-08-06 00:00:00 ~ 2022-08-06 23:15:00 - missing_time_splite
    185. 

  185. 2025-04-03 09:29:54,041 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    186. 

  186. 2025-04-03 09:29:54,041 - data_handler.py - INFO - 2022-08-07 00:00:00 ~ 2022-08-07 23:15:00 - missing_time_splite
    187. 

  187. 2025-04-03 09:29:54,042 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    188. 

  188. 2025-04-03 09:29:54,042 - data_handler.py - INFO - 数据总数：8207, 时序缺失的间隔：3, 其中，较长的时间间隔：14 - missing_time_splite
    189. 

  189. 2025-04-03 09:29:54,042 - data_handler.py - INFO - 需要补值的总点数：4.0 - missing_time_splite
    190. 

  190. 2025-04-03 09:29:54,043 - data_handler.py - INFO - 再次测算，需要插值的总点数为：4.0 - fill_train_data
    191. 

  191. 2025-04-03 09:29:54,045 - data_handler.py - INFO - 2022-05-02 08:00:00 ~ 2022-05-05 23:45:00 有 352 个点, 填补 0 个点. - data_fill
    192. 

  192. 2025-04-03 09:29:54,048 - data_handler.py - INFO - 2022-05-09 00:00:00 ~ 2022-07-01 06:30:00 有 5115 个点, 填补 2 个点. - data_fill
    193. 

  193. 2025-04-03 09:29:54,049 - data_handler.py - INFO - 2022-07-01 23:45:00 ~ 2022-07-08 06:15:00 有 603 个点, 填补 0 个点. - data_fill
    194. 

  194. 2025-04-03 09:29:54,051 - data_handler.py - INFO - 2022-07-08 23:45:00 ~ 2022-07-09 06:15:00 有 27 个点, 填补 0 个点. - data_fill
    195. 

  195. 2025-04-03 09:29:54,052 - data_handler.py - INFO - 2022-07-09 23:45:00 ~ 2022-07-10 06:15:00 有 27 个点, 填补 0 个点. - data_fill
    196. 

  196. 2025-04-03 09:29:54,053 - data_handler.py - INFO - 2022-07-10 23:45:00 ~ 2022-07-11 06:00:00 有 26 个点, 填补 0 个点. - data_fill
    197. 

  197. 2025-04-03 09:29:54,054 - data_handler.py - INFO - 2022-07-11 23:45:00 ~ 2022-07-12 06:00:00 有 26 个点, 填补 0 个点. - data_fill
    198. 

  198. 2025-04-03 09:29:54,055 - data_handler.py - INFO - 2022-07-12 23:45:00 ~ 2022-07-13 06:00:00 有 26 个点, 填补 0 个点. - data_fill
    199. 

  199. 2025-04-03 09:29:54,057 - data_handler.py - INFO - 2022-07-13 23:45:00 ~ 2022-08-01 20:00:00 有 1810 个点, 填补 2 个点. - data_fill
    200. 

  200. 2025-04-03 09:29:54,058 - data_handler.py - INFO - 2022-08-01 23:45:00 ~ 2022-08-02 21:00:00 有 86 个点, 填补 0 个点. - data_fill
    201. 

  201. 2025-04-03 09:29:54,060 - data_handler.py - INFO - 2022-08-02 23:45:00 ~ 2022-08-04 00:00:00 有 98 个点, 填补 0 个点. - data_fill
    202. 

  202. 2025-04-03 09:29:54,061 - data_handler.py - INFO - 2022-08-04 23:15:00 ~ 2022-08-05 00:00:00 有 4 个点, 填补 0 个点. - data_fill
    203. 

  203. 2025-04-03 09:29:54,062 - data_handler.py - INFO - 2022-08-05 23:15:00 ~ 2022-08-06 00:00:00 有 4 个点, 填补 0 个点. - data_fill
    204. 

  204. 2025-04-03 09:29:54,063 - data_handler.py - INFO - 2022-08-06 23:15:00 ~ 2022-08-07 00:00:00 有 4 个点, 填补 0 个点. - data_fill
    205. 

  205. 2025-04-03 09:29:54,064 - data_handler.py - INFO - 2022-08-07 23:15:00 ~ 2022-08-07 23:45:00 有 3 个点, 填补 0 个点. - data_fill
    206. 

  206. 2025-04-03 09:29:54,064 - data_handler.py - INFO - 训练集分成了15段，实际一共补值4点 - data_fill
    207. 

  207. 2025-04-03 09:29:56,533 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    208. 

  208. 2025-04-03 09:29:56,533 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    209. 

  209. 2025-04-03 09:29:56,533 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    210. 

  210. 2025-04-03 09:29:56,533 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    211. 

  211. 2025-04-03 09:29:56,533 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    212. 

  212. 2025-04-03 09:29:56,533 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    213. 

  213. 2025-04-03 09:29:56,533 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    214. 

  214. 2025-04-03 09:29:56,533 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    215. 

  215. 2025-04-03 09:29:56,751 - dbmg.py - INFO - ❌ 系统异常: Authentication failed., full error: {'ok': 0.0, 'errmsg': 'Authentication failed.', 'code': 18, 'codeName': 'AuthenticationFailed'} - get_keras_model_from_mongo
    216. 

  216. 2025-04-03 09:29:56,751 - tf_fmi.py - INFO - 加强训练加载模型权重失败:("Authentication failed., full error: {'ok': 0.0, 'errmsg': 'Authentication failed.', 'code': 18, 'codeName': 'AuthenticationFailed'}",) - train_init
    217. 

  217. 2025-04-03 09:30:07,199 - tf_fmi.py - INFO - -----模型训练经过26轮迭代----- - training
    218. 

  218. 2025-04-03 09:30:07,199 - tf_fmi.py - INFO - 训练集损失函数为：[51.47301 28.56529 17.00252 10.45491  6.56836  4.06275  2.4503   1.46647
    219. 

  219.   0.90028  0.59797  0.44225  0.36502  0.32612  0.30597  0.29727  0.29397
    220. 

  220.   0.29415  0.29314  0.29399  0.29276  0.29808  0.29488  0.29392  0.29338
    221. 

  221.   0.29251  0.2949 ] - training
    222. 

  222. 2025-04-03 09:30:07,199 - tf_fmi.py - INFO - 验证集损失函数为：[32.08155 20.83876 12.88028  8.15244  5.13598  3.18782  1.98071  1.25191
    223. 

  223.   0.83589  0.59431  0.47115  0.4052   0.37547  0.36021  0.34598  0.33878
    224. 

  224.   0.35231  0.35726  0.3452   0.35579  0.36291  0.3551   0.35916  0.3541
    225. 

  225.   0.34667  0.35812] - training
    226. 

  226. 2025-04-03 09:30:07,261 - dbmg.py - INFO - ❌ 数据库操作 - 详细错误: Authentication failed., full error: {'ok': 0.0, 'errmsg': 'Authentication failed.', 'code': 18, 'codeName': 'AuthenticationFailed'} - insert_trained_model_into_mongo
    227. 

  227. 2025-04-03 13:33:12,420 - 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.
    228. 

  228.  - _tfmw_add_deprecation_warning
    229. 

  229. 2025-04-03 13:33:12,613 - tf_cnn_train.py - INFO - Program starts execution! - model_training
    230. 

  230. 2025-04-03 13:33:12,674 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    231. 

  231. 2025-04-03 13:33:12,683 - data_cleaning.py - INFO - 列清洗：清洗的列有：{'HubSpeed', 'HubDireciton'} - data_column_cleaning
    232. 

  232. 2025-04-03 13:33:12,700 - data_cleaning.py - INFO - 行清洗：清洗的行数有：881，缺失的列有： - key_field_row_cleaning
    233. 

  233. 2025-04-03 13:33:12,721 - data_handler.py - INFO - 2022-05-05 23:45:00 ~ 2022-05-09 00:00:00 - missing_time_splite
    234. 

  234. 2025-04-03 13:33:12,721 - data_handler.py - INFO - 缺失点数：288.0 - missing_time_splite
    235. 

  235. 2025-04-03 13:33:12,754 - data_handler.py - INFO - 2022-06-25 07:45:00 ~ 2022-06-25 08:15:00 - missing_time_splite
    236. 

  236. 2025-04-03 13:33:12,754 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
    237. 

  237. 2025-04-03 13:33:12,754 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
    238. 

  238. 2025-04-03 13:33:12,756 - data_handler.py - INFO - 2022-06-27 06:45:00 ~ 2022-06-27 07:15:00 - missing_time_splite
    239. 

  239. 2025-04-03 13:33:12,756 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
    240. 

  240. 2025-04-03 13:33:12,756 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
    241. 

  241. 2025-04-03 13:33:12,760 - data_handler.py - INFO - 2022-07-01 06:30:00 ~ 2022-07-01 23:45:00 - missing_time_splite
    242. 

  242. 2025-04-03 13:33:12,760 - data_handler.py - INFO - 缺失点数：68.0 - missing_time_splite
    243. 

  243. 2025-04-03 13:33:12,765 - data_handler.py - INFO - 2022-07-08 06:15:00 ~ 2022-07-08 23:45:00 - missing_time_splite
    244. 

  244. 2025-04-03 13:33:12,765 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    245. 

  245. 2025-04-03 13:33:12,767 - data_handler.py - INFO - 2022-07-09 06:15:00 ~ 2022-07-09 23:45:00 - missing_time_splite
    246. 

  246. 2025-04-03 13:33:12,767 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    247. 

  247. 2025-04-03 13:33:12,768 - data_handler.py - INFO - 2022-07-10 06:15:00 ~ 2022-07-10 23:45:00 - missing_time_splite
    248. 

  248. 2025-04-03 13:33:12,768 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    249. 

  249. 2025-04-03 13:33:12,769 - data_handler.py - INFO - 2022-07-11 06:00:00 ~ 2022-07-11 23:45:00 - missing_time_splite
    250. 

  250. 2025-04-03 13:33:12,769 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    251. 

  251. 2025-04-03 13:33:12,770 - data_handler.py - INFO - 2022-07-12 06:00:00 ~ 2022-07-12 23:45:00 - missing_time_splite
    252. 

  252. 2025-04-03 13:33:12,770 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    253. 

  253. 2025-04-03 13:33:12,772 - data_handler.py - INFO - 2022-07-13 06:00:00 ~ 2022-07-13 23:45:00 - missing_time_splite
    254. 

  254. 2025-04-03 13:33:12,772 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    255. 

  255. 2025-04-03 13:33:12,773 - data_handler.py - INFO - 2022-07-14 23:00:00 ~ 2022-07-14 23:45:00 - missing_time_splite
    256. 

  256. 2025-04-03 13:33:12,773 - data_handler.py - INFO - 缺失点数：2.0 - missing_time_splite
    257. 

  257. 2025-04-03 13:33:12,773 - data_handler.py - INFO - 需要补值的点数：2.0 - missing_time_splite
    258. 

  258. 2025-04-03 13:33:12,785 - data_handler.py - INFO - 2022-08-01 20:00:00 ~ 2022-08-01 23:45:00 - missing_time_splite
    259. 

  259. 2025-04-03 13:33:12,785 - data_handler.py - INFO - 缺失点数：14.0 - missing_time_splite
    260. 

  260. 2025-04-03 13:33:12,787 - data_handler.py - INFO - 2022-08-02 21:00:00 ~ 2022-08-02 23:45:00 - missing_time_splite
    261. 

  261. 2025-04-03 13:33:12,788 - data_handler.py - INFO - 缺失点数：10.0 - missing_time_splite
    262. 

  262. 2025-04-03 13:33:12,789 - data_handler.py - INFO - 2022-08-04 00:00:00 ~ 2022-08-04 23:15:00 - missing_time_splite
    263. 

  263. 2025-04-03 13:33:12,789 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    264. 

  264. 2025-04-03 13:33:12,790 - data_handler.py - INFO - 2022-08-05 00:00:00 ~ 2022-08-05 23:15:00 - missing_time_splite
    265. 

  265. 2025-04-03 13:33:12,790 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    266. 

  266. 2025-04-03 13:33:12,791 - data_handler.py - INFO - 2022-08-06 00:00:00 ~ 2022-08-06 23:15:00 - missing_time_splite
    267. 

  267. 2025-04-03 13:33:12,791 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    268. 

  268. 2025-04-03 13:33:12,792 - data_handler.py - INFO - 2022-08-07 00:00:00 ~ 2022-08-07 23:15:00 - missing_time_splite
    269. 

  269. 2025-04-03 13:33:12,792 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    270. 

  270. 2025-04-03 13:33:12,793 - data_handler.py - INFO - 数据总数：8207, 时序缺失的间隔：3, 其中，较长的时间间隔：14 - missing_time_splite
    271. 

  271. 2025-04-03 13:33:12,793 - data_handler.py - INFO - 需要补值的总点数：4.0 - missing_time_splite
    272. 

  272. 2025-04-03 13:33:12,793 - data_handler.py - INFO - 再次测算，需要插值的总点数为：4.0 - fill_train_data
    273. 

  273. 2025-04-03 13:33:12,795 - data_handler.py - INFO - 2022-05-02 08:00:00 ~ 2022-05-05 23:45:00 有 352 个点, 填补 0 个点. - data_fill
    274. 

  274. 2025-04-03 13:33:12,799 - data_handler.py - INFO - 2022-05-09 00:00:00 ~ 2022-07-01 06:30:00 有 5115 个点, 填补 2 个点. - data_fill
    275. 

  275. 2025-04-03 13:33:12,800 - data_handler.py - INFO - 2022-07-01 23:45:00 ~ 2022-07-08 06:15:00 有 603 个点, 填补 0 个点. - data_fill
    276. 

  276. 2025-04-03 13:33:12,801 - data_handler.py - INFO - 2022-07-08 23:45:00 ~ 2022-07-09 06:15:00 有 27 个点, 填补 0 个点. - data_fill
    277. 

  277. 2025-04-03 13:33:12,802 - data_handler.py - INFO - 2022-07-09 23:45:00 ~ 2022-07-10 06:15:00 有 27 个点, 填补 0 个点. - data_fill
    278. 

  278. 2025-04-03 13:33:12,803 - data_handler.py - INFO - 2022-07-10 23:45:00 ~ 2022-07-11 06:00:00 有 26 个点, 填补 0 个点. - data_fill
    279. 

  279. 2025-04-03 13:33:12,804 - data_handler.py - INFO - 2022-07-11 23:45:00 ~ 2022-07-12 06:00:00 有 26 个点, 填补 0 个点. - data_fill
    280. 

  280. 2025-04-03 13:33:12,805 - data_handler.py - INFO - 2022-07-12 23:45:00 ~ 2022-07-13 06:00:00 有 26 个点, 填补 0 个点. - data_fill
    281. 

  281. 2025-04-03 13:33:12,807 - data_handler.py - INFO - 2022-07-13 23:45:00 ~ 2022-08-01 20:00:00 有 1810 个点, 填补 2 个点. - data_fill
    282. 

  282. 2025-04-03 13:33:12,808 - data_handler.py - INFO - 2022-08-01 23:45:00 ~ 2022-08-02 21:00:00 有 86 个点, 填补 0 个点. - data_fill
    283. 

  283. 2025-04-03 13:33:12,809 - data_handler.py - INFO - 2022-08-02 23:45:00 ~ 2022-08-04 00:00:00 有 98 个点, 填补 0 个点. - data_fill
    284. 

  284. 2025-04-03 13:33:12,810 - data_handler.py - INFO - 2022-08-04 23:15:00 ~ 2022-08-05 00:00:00 有 4 个点, 填补 0 个点. - data_fill
    285. 

  285. 2025-04-03 13:33:12,811 - data_handler.py - INFO - 2022-08-05 23:15:00 ~ 2022-08-06 00:00:00 有 4 个点, 填补 0 个点. - data_fill
    286. 

  286. 2025-04-03 13:33:12,812 - data_handler.py - INFO - 2022-08-06 23:15:00 ~ 2022-08-07 00:00:00 有 4 个点, 填补 0 个点. - data_fill
    287. 

  287. 2025-04-03 13:33:12,813 - data_handler.py - INFO - 2022-08-07 23:15:00 ~ 2022-08-07 23:45:00 有 3 个点, 填补 0 个点. - data_fill
    288. 

  288. 2025-04-03 13:33:12,813 - data_handler.py - INFO - 训练集分成了15段，实际一共补值4点 - data_fill
    289. 

  289. 2025-04-03 13:33:15,280 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    290. 

  290. 2025-04-03 13:33:15,281 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    291. 

  291. 2025-04-03 13:33:15,281 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    292. 

  292. 2025-04-03 13:33:15,281 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    293. 

  293. 2025-04-03 13:33:15,281 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    294. 

  294. 2025-04-03 13:33:15,281 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    295. 

  295. 2025-04-03 13:33:15,281 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    296. 

  296. 2025-04-03 13:33:15,281 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    297. 

  297. 2025-04-03 13:33:15,493 - dbmg.py - INFO - ❌ 系统异常: Authentication failed., full error: {'ok': 0.0, 'errmsg': 'Authentication failed.', 'code': 18, 'codeName': 'AuthenticationFailed'} - get_keras_model_from_mongo
    298. 

  298. 2025-04-03 13:33:15,493 - tf_cnn.py - INFO - 加强训练加载模型权重失败:("Authentication failed., full error: {'ok': 0.0, 'errmsg': 'Authentication failed.', 'code': 18, 'codeName': 'AuthenticationFailed'}",) - train_init
    299. 

  299. 2025-04-03 13:33:26,756 - tf_cnn.py - INFO - -----模型训练经过76轮迭代----- - training
    300. 

  300. 2025-04-03 13:33:26,756 - tf_cnn.py - INFO - 训练集损失函数为：[5.3196  2.96034 1.83915 1.399   1.15213 0.97754 0.84255 0.73772 0.66046
    301. 

  301.  0.60034 0.55292 0.51518 0.48333 0.45676 0.43364 0.41425 0.39769 0.3834
    302. 

  302.  0.37138 0.36097 0.35203 0.3447  0.33776 0.33146 0.32607 0.32143 0.31722
    303. 

  303.  0.31405 0.31122 0.30854 0.306   0.30391 0.30194 0.30016 0.29861 0.29733
    304. 

  304.  0.29619 0.29507 0.29394 0.29281 0.2917  0.29055 0.28939 0.28831 0.28746
    305. 

  305.  0.28662 0.28581 0.285   0.2843  0.28371 0.28309 0.28248 0.28181 0.28114
    306. 

  306.  0.28057 0.28002 0.27949 0.27907 0.27876 0.27847 0.27816 0.27786 0.27756
    307. 

  307.  0.27726 0.27697 0.27668 0.27638 0.27609 0.27581 0.27552 0.27525 0.27498
    308. 

  308.  0.27482 0.27454 0.27427 0.27401] - training
    309. 

  309. 2025-04-03 13:33:26,757 - tf_cnn.py - INFO - 验证集损失函数为：[3.92513 2.23538 1.60418 1.28774 1.08642 0.93493 0.81426 0.72426 0.65625
    310. 

  310.  0.6028  0.5611  0.52649 0.49804 0.47311 0.45227 0.43444 0.41905 0.40589
    311. 

  311.  0.39496 0.38506 0.37721 0.36995 0.3632  0.35745 0.35234 0.3479  0.34436
    312. 

  312.  0.34159 0.33884 0.33624 0.33404 0.33224 0.33051 0.32884 0.32741 0.32638
    313. 

  313.  0.32527 0.32424 0.32322 0.32202 0.32096 0.3199  0.31874 0.31774 0.31694
    314. 

  314.  0.31608 0.31535 0.31433 0.31367 0.31316 0.31234 0.3115  0.31067 0.30995
    315. 

  315.  0.30914 0.30852 0.30783 0.30739 0.30679 0.30646 0.30603 0.30568 0.30537
    316. 

  316.  0.3051  0.30508 0.3049  0.30503 0.30513 0.30515 0.30524 0.3053  0.30525
    317. 

  317.  0.30495 0.30523 0.30534 0.30523] - training
    318. 

  318. 2025-04-03 13:33:26,794 - dbmg.py - INFO - ❌ 数据库操作 - 详细错误: Authentication failed., full error: {'ok': 0.0, 'errmsg': 'Authentication failed.', 'code': 18, 'codeName': 'AuthenticationFailed'} - insert_trained_model_into_mongo
    319. 

  319. 2025-04-03 13:51:00,688 - 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.
    320. 

  320.  - _tfmw_add_deprecation_warning
    321. 

  321. 2025-04-03 13:51:26,825 - 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.
    322. 

  322.  - _tfmw_add_deprecation_warning
    323. 

  323. 2025-04-03 13:51:26,937 - tf_cnn_pre.py - INFO - 算法状态异常：Traceback (most recent call last):
    324. 

  324.   File "E:\compete\app\common\dbmg.py", line 436, in get_scaler_model_from_mongo
    325. 

  325.     scaler_doc = collection.find_one(
    326. 

  326.                  ^^^^^^^^^^^^^^^^^^^^
    327. 

  327.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\collection.py", line 1495, in find_one
    328. 

  328.     for result in cursor.limit(-1):
    329. 

  329.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\cursor.py", line 1243, in next
    330. 

  330.     if len(self.__data) or self._refresh():
    331. 

  331.                            ^^^^^^^^^^^^^^^
    332. 

  332.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\cursor.py", line 1160, in _refresh
    333. 

  333.     self.__send_message(q)
    334. 

  334.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\cursor.py", line 1039, in __send_message
    335. 

  335.     response = client._run_operation(
    336. 

  336.                ^^^^^^^^^^^^^^^^^^^^^^
    337. 

  337.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\_csot.py", line 108, in csot_wrapper
    338. 

  338.     return func(self, *args, **kwargs)
    339. 

  339.            ^^^^^^^^^^^^^^^^^^^^^^^^^^^
    340. 

  340.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 1425, in _run_operation
    341. 

  341.     return self._retryable_read(
    342. 

  342.            ^^^^^^^^^^^^^^^^^^^^^
    343. 

  343.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 1534, in _retryable_read
    344. 

  344.     return self._retry_internal(
    345. 

  345.            ^^^^^^^^^^^^^^^^^^^^^
    346. 

  346.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\_csot.py", line 108, in csot_wrapper
    347. 

  347.     return func(self, *args, **kwargs)
    348. 

  348.            ^^^^^^^^^^^^^^^^^^^^^^^^^^^
    349. 

  349.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 1501, in _retry_internal
    350. 

  350.     ).run()
    351. 

  351.       ^^^^^
    352. 

  352.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 2347, in run
    353. 

  353.     return self._read() if self._is_read else self._write()
    354. 

  354.            ^^^^^^^^^^^^
    355. 

  355.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 2479, in _read
    356. 

  356.     with self._client._conn_from_server(self._read_pref, self._server, self._session) as (
    357. 

  357.   File "D:\anaconda3\envs\py312\Lib\contextlib.py", line 137, in __enter__
    358. 

  358.     return next(self.gen)
    359. 

  359.            ^^^^^^^^^^^^^^
    360. 

  360.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 1351, in _conn_from_server
    361. 

  361.     with self._checkout(server, session) as conn:
    362. 

  362.   File "D:\anaconda3\envs\py312\Lib\contextlib.py", line 137, in __enter__
    363. 

  363.     return next(self.gen)
    364. 

  364.            ^^^^^^^^^^^^^^
    365. 

  365.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 1260, in _checkout
    366. 

  366.     with server.checkout(handler=err_handler) as conn:
    367. 

  367.   File "D:\anaconda3\envs\py312\Lib\contextlib.py", line 137, in __enter__
    368. 

  368.     return next(self.gen)
    369. 

  369.            ^^^^^^^^^^^^^^
    370. 

  370.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\pool.py", line 1763, in checkout
    371. 

  371.     conn = self._get_conn(checkout_started_time, handler=handler)
    372. 

  372.            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    373. 

  373.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\pool.py", line 1921, in _get_conn
    374. 

  374.     conn = self.connect(handler=handler)
    375. 

  375.            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    376. 

  376.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\pool.py", line 1725, in connect
    377. 

  377.     conn.authenticate()
    378. 

  378.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\pool.py", line 1098, in authenticate
    379. 

  379.     auth.authenticate(creds, self, reauthenticate=reauthenticate)
    380. 

  380.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\auth.py", line 656, in authenticate
    381. 

  381.     auth_func(credentials, conn)
    382. 

  382.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\auth.py", line 560, in _authenticate_default
    383. 

  383.     return _authenticate_scram(credentials, conn, "SCRAM-SHA-1")
    384. 

  384.            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    385. 

  385.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\auth.py", line 299, in _authenticate_scram
    386. 

  386.     res = conn.command(source, cmd)
    387. 

  387.           ^^^^^^^^^^^^^^^^^^^^^^^^^
    388. 

  388.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\helpers.py", line 342, in inner
    389. 

  389.     return func(*args, **kwargs)
    390. 

  390.            ^^^^^^^^^^^^^^^^^^^^^
    391. 

  391.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\pool.py", line 988, in command
    392. 

  392.     return command(
    393. 

  393.            ^^^^^^^^
    394. 

  394.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\network.py", line 212, in command
    395. 

  395.     helpers._check_command_response(
    396. 

  396.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\helpers.py", line 248, in _check_command_response
    397. 

  397.     raise OperationFailure(errmsg, code, response, max_wire_version)
    398. 

  398. pymongo.errors.OperationFailure: Authentication failed., full error: {'ok': 0.0, 'errmsg': 'Authentication failed.', 'code': 18, 'codeName': 'AuthenticationFailed'}
    399. 

  399. 

  400. The above exception was the direct cause of the following exception:
    401. 

  401. 

  402. Traceback (most recent call last):
    403. 

  403.   File "E:\compete\app\predict\tf_cnn_pre.py", line 41, in model_prediction
    404. 

  404.     feature_scaler, target_scaler = mgUtils.get_scaler_model_from_mongo(params)
    405. 

  405.                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    406. 

  406.   File "E:\compete\app\common\dbmg.py", line 474, in get_scaler_model_from_mongo
    407. 

  407.     raise RuntimeError(f"🔌 数据库操作失败: {str(e)}") from e
    408. 

  408. RuntimeError: 🔌 数据库操作失败: Authentication failed., full error: {'ok': 0.0, 'errmsg': 'Authentication failed.', 'code': 18, 'codeName': 'AuthenticationFailed'}
    409. 

  409.  - model_prediction
    410. 

  410. 2025-04-03 13:51:26,942 - tf_cnn_pre.py - INFO - cnn预测任务：用了 0.01752614974975586 秒  - model_prediction
    411. 

  411. 2025-04-03 13:59:47,178 - 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.
    412. 

  412.  - _tfmw_add_deprecation_warning
    413. 

  413. 2025-04-03 13:59:47,295 - tf_cnn_pre.py - INFO - 算法状态异常：Traceback (most recent call last):
    414. 

  414.   File "E:\compete\app\common\dbmg.py", line 436, in get_scaler_model_from_mongo
    415. 

  415.     scaler_doc = collection.find_one(
    416. 

  416.                  ^^^^^^^^^^^^^^^^^^^^
    417. 

  417.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\collection.py", line 1495, in find_one
    418. 

  418.     for result in cursor.limit(-1):
    419. 

  419.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\cursor.py", line 1243, in next
    420. 

  420.     if len(self.__data) or self._refresh():
    421. 

  421.                            ^^^^^^^^^^^^^^^
    422. 

  422.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\cursor.py", line 1160, in _refresh
    423. 

  423.     self.__send_message(q)
    424. 

  424.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\cursor.py", line 1039, in __send_message
    425. 

  425.     response = client._run_operation(
    426. 

  426.                ^^^^^^^^^^^^^^^^^^^^^^
    427. 

  427.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\_csot.py", line 108, in csot_wrapper
    428. 

  428.     return func(self, *args, **kwargs)
    429. 

  429.            ^^^^^^^^^^^^^^^^^^^^^^^^^^^
    430. 

  430.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 1425, in _run_operation
    431. 

  431.     return self._retryable_read(
    432. 

  432.            ^^^^^^^^^^^^^^^^^^^^^
    433. 

  433.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 1534, in _retryable_read
    434. 

  434.     return self._retry_internal(
    435. 

  435.            ^^^^^^^^^^^^^^^^^^^^^
    436. 

  436.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\_csot.py", line 108, in csot_wrapper
    437. 

  437.     return func(self, *args, **kwargs)
    438. 

  438.            ^^^^^^^^^^^^^^^^^^^^^^^^^^^
    439. 

  439.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 1501, in _retry_internal
    440. 

  440.     ).run()
    441. 

  441.       ^^^^^
    442. 

  442.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 2347, in run
    443. 

  443.     return self._read() if self._is_read else self._write()
    444. 

  444.            ^^^^^^^^^^^^
    445. 

  445.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 2479, in _read
    446. 

  446.     with self._client._conn_from_server(self._read_pref, self._server, self._session) as (
    447. 

  447.   File "D:\anaconda3\envs\py312\Lib\contextlib.py", line 137, in __enter__
    448. 

  448.     return next(self.gen)
    449. 

  449.            ^^^^^^^^^^^^^^
    450. 

  450.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 1351, in _conn_from_server
    451. 

  451.     with self._checkout(server, session) as conn:
    452. 

  452.   File "D:\anaconda3\envs\py312\Lib\contextlib.py", line 137, in __enter__
    453. 

  453.     return next(self.gen)
    454. 

  454.            ^^^^^^^^^^^^^^
    455. 

  455.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 1260, in _checkout
    456. 

  456.     with server.checkout(handler=err_handler) as conn:
    457. 

  457.   File "D:\anaconda3\envs\py312\Lib\contextlib.py", line 137, in __enter__
    458. 

  458.     return next(self.gen)
    459. 

  459.            ^^^^^^^^^^^^^^
    460. 

  460.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\pool.py", line 1763, in checkout
    461. 

  461.     conn = self._get_conn(checkout_started_time, handler=handler)
    462. 

  462.            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    463. 

  463.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\pool.py", line 1921, in _get_conn
    464. 

  464.     conn = self.connect(handler=handler)
    465. 

  465.            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    466. 

  466.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\pool.py", line 1725, in connect
    467. 

  467.     conn.authenticate()
    468. 

  468.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\pool.py", line 1098, in authenticate
    469. 

  469.     auth.authenticate(creds, self, reauthenticate=reauthenticate)
    470. 

  470.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\auth.py", line 656, in authenticate
    471. 

  471.     auth_func(credentials, conn)
    472. 

  472.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\auth.py", line 560, in _authenticate_default
    473. 

  473.     return _authenticate_scram(credentials, conn, "SCRAM-SHA-1")
    474. 

  474.            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    475. 

  475.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\auth.py", line 299, in _authenticate_scram
    476. 

  476.     res = conn.command(source, cmd)
    477. 

  477.           ^^^^^^^^^^^^^^^^^^^^^^^^^
    478. 

  478.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\helpers.py", line 342, in inner
    479. 

  479.     return func(*args, **kwargs)
    480. 

  480.            ^^^^^^^^^^^^^^^^^^^^^
    481. 

  481.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\pool.py", line 988, in command
    482. 

  482.     return command(
    483. 

  483.            ^^^^^^^^
    484. 

  484.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\network.py", line 212, in command
    485. 

  485.     helpers._check_command_response(
    486. 

  486.   File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\helpers.py", line 248, in _check_command_response
    487. 

  487.     raise OperationFailure(errmsg, code, response, max_wire_version)
    488. 

  488. pymongo.errors.OperationFailure: Authentication failed., full error: {'ok': 0.0, 'errmsg': 'Authentication failed.', 'code': 18, 'codeName': 'AuthenticationFailed'}
    489. 

  489. 

  490. The above exception was the direct cause of the following exception:
    491. 

  491. 

  492. Traceback (most recent call last):
    493. 

  493.   File "E:\compete\app\predict\tf_cnn_pre.py", line 41, in model_prediction
    494. 

  494.     feature_scaler, target_scaler = mgUtils.get_scaler_model_from_mongo(params)
    495. 

  495.                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    496. 

  496.   File "E:\compete\app\common\dbmg.py", line 474, in get_scaler_model_from_mongo
    497. 

  497.     raise RuntimeError(f"🔌 数据库操作失败: {str(e)}") from e
    498. 

  498. RuntimeError: 🔌 数据库操作失败: Authentication failed., full error: {'ok': 0.0, 'errmsg': 'Authentication failed.', 'code': 18, 'codeName': 'AuthenticationFailed'}
    499. 

  499.  - model_prediction
    500. 

  500. 2025-04-03 13:59:47,299 - tf_cnn_pre.py - INFO - cnn预测任务：用了 0.01714491844177246 秒  - model_prediction
    501.