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2025-03-28
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south-forecast.2025-03-28.0.log

south-forecast.2025-03-28.0.log 19 KB
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  1. 2025-03-28 09:40:35,370 - 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. 2025-03-28 09:40:35,651 - tf_fmi_train.py - INFO - Program starts execution! - model_training
    4. 

  4. 2025-03-28 09:40:35,706 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    5. 

  5. 2025-03-28 09:40:35,716 - data_cleaning.py - INFO - 列清洗：清洗的列有：{'HubSpeed', 'HubDireciton'} - data_column_cleaning
    6. 

  6. 2025-03-28 09:41:23,101 - 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. 2025-03-28 09:41:23,531 - tf_fmi_train.py - INFO - Program starts execution! - model_training
    9. 

  9. 2025-03-28 09:41:49,477 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    10. 

  10. 2025-03-28 09:41:50,284 - data_cleaning.py - INFO - 列清洗：清洗的列有：{'HubDireciton', 'HubSpeed'} - data_column_cleaning
    11. 

  11. 2025-03-28 09:42:35,806 - 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.
    12. 

  12.  - _tfmw_add_deprecation_warning
    13. 

  13. 2025-03-28 09:42:40,835 - tf_fmi_train.py - INFO - Program starts execution! - model_training
    14. 

  14. 2025-03-28 09:42:47,459 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    15. 

  15. 2025-03-28 09:42:51,045 - data_cleaning.py - INFO - 列清洗：清洗的列有：{'HubDireciton', 'HubSpeed'} - data_column_cleaning
    16. 

  16. 2025-03-28 09:45:13,770 - 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.
    17. 

  17.  - _tfmw_add_deprecation_warning
    18. 

  18. 2025-03-28 09:45:32,937 - tf_fmi_train.py - INFO - Program starts execution! - model_training
    19. 

  19. 2025-03-28 09:45:59,675 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    20. 

  20. 2025-03-28 09:47:20,649 - 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.
    21. 

  21.  - _tfmw_add_deprecation_warning
    22. 

  22. 2025-03-28 09:47:24,365 - tf_fmi_train.py - INFO - Program starts execution! - model_training
    23. 

  23. 2025-03-28 09:47:32,614 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    24. 

  24. 2025-03-28 09:47:33,522 - data_cleaning.py - INFO - 列清洗：清洗的列有：{'HubDireciton', 'HubSpeed'} - data_column_cleaning
    25. 

  25. 2025-03-28 09:47:35,902 - data_cleaning.py - INFO - 行清洗：清洗的行数有：881，缺失的列有： - key_field_row_cleaning
    26. 

  26. 2025-03-28 09:47:46,273 - data_handler.py - INFO - 2022-05-05 23:45:00 ~ 2022-05-09 00:00:00 - missing_time_splite
    27. 

  27. 2025-03-28 09:47:46,273 - data_handler.py - INFO - 缺失点数：288.0 - missing_time_splite
    28. 

  28. 2025-03-28 09:47:46,306 - data_handler.py - INFO - 2022-06-25 07:45:00 ~ 2022-06-25 08:15:00 - missing_time_splite
    29. 

  29. 2025-03-28 09:47:46,307 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
    30. 

  30. 2025-03-28 09:47:46,307 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
    31. 

  31. 2025-03-28 09:47:46,308 - data_handler.py - INFO - 2022-06-27 06:45:00 ~ 2022-06-27 07:15:00 - missing_time_splite
    32. 

  32. 2025-03-28 09:47:46,308 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
    33. 

  33. 2025-03-28 09:47:46,308 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
    34. 

  34. 2025-03-28 09:47:46,312 - data_handler.py - INFO - 2022-07-01 06:30:00 ~ 2022-07-01 23:45:00 - missing_time_splite
    35. 

  35. 2025-03-28 09:47:46,312 - data_handler.py - INFO - 缺失点数：68.0 - missing_time_splite
    36. 

  36. 2025-03-28 09:47:46,317 - data_handler.py - INFO - 2022-07-08 06:15:00 ~ 2022-07-08 23:45:00 - missing_time_splite
    37. 

  37. 2025-03-28 09:47:46,318 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    38. 

  38. 2025-03-28 09:47:46,319 - data_handler.py - INFO - 2022-07-09 06:15:00 ~ 2022-07-09 23:45:00 - missing_time_splite
    39. 

  39. 2025-03-28 09:47:46,320 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    40. 

  40. 2025-03-28 09:47:46,321 - data_handler.py - INFO - 2022-07-10 06:15:00 ~ 2022-07-10 23:45:00 - missing_time_splite
    41. 

  41. 2025-03-28 09:47:46,321 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    42. 

  42. 2025-03-28 09:47:46,322 - data_handler.py - INFO - 2022-07-11 06:00:00 ~ 2022-07-11 23:45:00 - missing_time_splite
    43. 

  43. 2025-03-28 09:47:46,322 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    44. 

  44. 2025-03-28 09:47:46,323 - data_handler.py - INFO - 2022-07-12 06:00:00 ~ 2022-07-12 23:45:00 - missing_time_splite
    45. 

  45. 2025-03-28 09:47:46,324 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    46. 

  46. 2025-03-28 09:47:46,325 - data_handler.py - INFO - 2022-07-13 06:00:00 ~ 2022-07-13 23:45:00 - missing_time_splite
    47. 

  47. 2025-03-28 09:47:46,325 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    48. 

  48. 2025-03-28 09:47:46,326 - data_handler.py - INFO - 2022-07-14 23:00:00 ~ 2022-07-14 23:45:00 - missing_time_splite
    49. 

  49. 2025-03-28 09:47:46,326 - data_handler.py - INFO - 缺失点数：2.0 - missing_time_splite
    50. 

  50. 2025-03-28 09:47:46,326 - data_handler.py - INFO - 需要补值的点数：2.0 - missing_time_splite
    51. 

  51. 2025-03-28 09:47:46,340 - data_handler.py - INFO - 2022-08-01 20:00:00 ~ 2022-08-01 23:45:00 - missing_time_splite
    52. 

  52. 2025-03-28 09:47:46,340 - data_handler.py - INFO - 缺失点数：14.0 - missing_time_splite
    53. 

  53. 2025-03-28 09:47:46,342 - data_handler.py - INFO - 2022-08-02 21:00:00 ~ 2022-08-02 23:45:00 - missing_time_splite
    54. 

  54. 2025-03-28 09:47:46,342 - data_handler.py - INFO - 缺失点数：10.0 - missing_time_splite
    55. 

  55. 2025-03-28 09:47:46,344 - data_handler.py - INFO - 2022-08-04 00:00:00 ~ 2022-08-04 23:15:00 - missing_time_splite
    56. 

  56. 2025-03-28 09:47:46,344 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    57. 

  57. 2025-03-28 09:47:46,345 - data_handler.py - INFO - 2022-08-05 00:00:00 ~ 2022-08-05 23:15:00 - missing_time_splite
    58. 

  58. 2025-03-28 09:47:46,345 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    59. 

  59. 2025-03-28 09:47:46,346 - data_handler.py - INFO - 2022-08-06 00:00:00 ~ 2022-08-06 23:15:00 - missing_time_splite
    60. 

  60. 2025-03-28 09:47:46,346 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    61. 

  61. 2025-03-28 09:47:46,347 - data_handler.py - INFO - 2022-08-07 00:00:00 ~ 2022-08-07 23:15:00 - missing_time_splite
    62. 

  62. 2025-03-28 09:47:46,347 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    63. 

  63. 2025-03-28 09:47:46,348 - 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
    64. 

  64. 2025-03-28 09:47:46,348 - data_handler.py - INFO - 需要补值的总点数：4.0 - missing_time_splite
    65. 

  65. 2025-03-28 09:47:46,350 - data_handler.py - INFO - 再次测算，需要插值的总点数为：4.0 - fill_train_data
    66. 

  66. 2025-03-28 09:47:47,803 - 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
    67. 

  67. 2025-03-28 09:47:47,810 - 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
    68. 

  68. 2025-03-28 09:47:47,815 - 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
    69. 

  69. 2025-03-28 09:47:47,819 - 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
    70. 

  70. 2025-03-28 09:47:47,824 - 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
    71. 

  71. 2025-03-28 09:47:47,828 - 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
    72. 

  72. 2025-03-28 09:47:47,833 - 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
    73. 

  73. 2025-03-28 09:47:47,837 - 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
    74. 

  74. 2025-03-28 09:47:47,843 - 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
    75. 

  75. 2025-03-28 09:47:47,847 - 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
    76. 

  76. 2025-03-28 09:47:47,852 - 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
    77. 

  77. 2025-03-28 09:47:47,856 - 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
    78. 

  78. 2025-03-28 09:47:47,860 - 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
    79. 

  79. 2025-03-28 09:47:47,865 - 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
    80. 

  80. 2025-03-28 09:47:47,869 - 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
    81. 

  81. 2025-03-28 09:47:47,870 - data_handler.py - INFO - 训练集分成了15段，实际一共补值4点 - data_fill
    82. 

  82. 2025-03-28 09:47:58,110 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    83. 

  83. 2025-03-28 09:47:58,110 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    84. 

  84. 2025-03-28 09:47:58,110 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    85. 

  85. 2025-03-28 09:47:58,110 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    86. 

  86. 2025-03-28 09:47:58,110 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    87. 

  87. 2025-03-28 09:47:58,110 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    88. 

  88. 2025-03-28 09:47:58,110 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    89. 

  89. 2025-03-28 09:47:58,110 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    90. 

  90. 2025-03-28 09:48:03,145 - 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
    91. 

  91. 2025-03-28 09:48:03,145 - 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
    92. 

  92. 2025-03-28 09:48:42,795 - 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.
    93. 

  93.  - _tfmw_add_deprecation_warning
    94. 

  94. 2025-03-28 09:48:42,894 - tf_fmi_train.py - INFO - Program starts execution! - model_training
    95. 

  95. 2025-03-28 09:48:42,947 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    96. 

  96. 2025-03-28 09:48:42,956 - data_cleaning.py - INFO - 列清洗：清洗的列有：{'HubSpeed', 'HubDireciton'} - data_column_cleaning
    97. 

  97. 2025-03-28 09:48:42,970 - data_cleaning.py - INFO - 行清洗：清洗的行数有：881，缺失的列有： - key_field_row_cleaning
    98. 

  98. 2025-03-28 09:48:42,981 - data_handler.py - INFO - 2022-05-05 23:45:00 ~ 2022-05-09 00:00:00 - missing_time_splite
    99. 

  99. 2025-03-28 09:48:42,981 - data_handler.py - INFO - 缺失点数：288.0 - missing_time_splite
    100. 

  100. 2025-03-28 09:48:43,016 - data_handler.py - INFO - 2022-06-25 07:45:00 ~ 2022-06-25 08:15:00 - missing_time_splite
    101. 

  101. 2025-03-28 09:48:43,016 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
    102. 

  102. 2025-03-28 09:48:43,016 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
    103. 

  103. 2025-03-28 09:48:43,017 - data_handler.py - INFO - 2022-06-27 06:45:00 ~ 2022-06-27 07:15:00 - missing_time_splite
    104. 

  104. 2025-03-28 09:48:43,017 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
    105. 

  105. 2025-03-28 09:48:43,017 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
    106. 

  106. 2025-03-28 09:48:43,022 - data_handler.py - INFO - 2022-07-01 06:30:00 ~ 2022-07-01 23:45:00 - missing_time_splite
    107. 

  107. 2025-03-28 09:48:43,022 - data_handler.py - INFO - 缺失点数：68.0 - missing_time_splite
    108. 

  108. 2025-03-28 09:48:43,027 - data_handler.py - INFO - 2022-07-08 06:15:00 ~ 2022-07-08 23:45:00 - missing_time_splite
    109. 

  109. 2025-03-28 09:48:43,027 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    110. 

  110. 2025-03-28 09:48:43,028 - data_handler.py - INFO - 2022-07-09 06:15:00 ~ 2022-07-09 23:45:00 - missing_time_splite
    111. 

  111. 2025-03-28 09:48:43,028 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    112. 

  112. 2025-03-28 09:48:43,029 - data_handler.py - INFO - 2022-07-10 06:15:00 ~ 2022-07-10 23:45:00 - missing_time_splite
    113. 

  113. 2025-03-28 09:48:43,030 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    114. 

  114. 2025-03-28 09:48:43,030 - data_handler.py - INFO - 2022-07-11 06:00:00 ~ 2022-07-11 23:45:00 - missing_time_splite
    115. 

  115. 2025-03-28 09:48:43,031 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    116. 

  116. 2025-03-28 09:48:43,032 - data_handler.py - INFO - 2022-07-12 06:00:00 ~ 2022-07-12 23:45:00 - missing_time_splite
    117. 

  117. 2025-03-28 09:48:43,032 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    118. 

  118. 2025-03-28 09:48:43,033 - data_handler.py - INFO - 2022-07-13 06:00:00 ~ 2022-07-13 23:45:00 - missing_time_splite
    119. 

  119. 2025-03-28 09:48:43,033 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    120. 

  120. 2025-03-28 09:48:43,033 - data_handler.py - INFO - 2022-07-14 23:00:00 ~ 2022-07-14 23:45:00 - missing_time_splite
    121. 

  121. 2025-03-28 09:48:43,034 - data_handler.py - INFO - 缺失点数：2.0 - missing_time_splite
    122. 

  122. 2025-03-28 09:48:43,034 - data_handler.py - INFO - 需要补值的点数：2.0 - missing_time_splite
    123. 

  123. 2025-03-28 09:48:43,047 - data_handler.py - INFO - 2022-08-01 20:00:00 ~ 2022-08-01 23:45:00 - missing_time_splite
    124. 

  124. 2025-03-28 09:48:43,047 - data_handler.py - INFO - 缺失点数：14.0 - missing_time_splite
    125. 

  125. 2025-03-28 09:48:43,049 - data_handler.py - INFO - 2022-08-02 21:00:00 ~ 2022-08-02 23:45:00 - missing_time_splite
    126. 

  126. 2025-03-28 09:48:43,049 - data_handler.py - INFO - 缺失点数：10.0 - missing_time_splite
    127. 

  127. 2025-03-28 09:48:43,050 - data_handler.py - INFO - 2022-08-04 00:00:00 ~ 2022-08-04 23:15:00 - missing_time_splite
    128. 

  128. 2025-03-28 09:48:43,051 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    129. 

  129. 2025-03-28 09:48:43,052 - data_handler.py - INFO - 2022-08-05 00:00:00 ~ 2022-08-05 23:15:00 - missing_time_splite
    130. 

  130. 2025-03-28 09:48:43,052 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    131. 

  131. 2025-03-28 09:48:43,053 - data_handler.py - INFO - 2022-08-06 00:00:00 ~ 2022-08-06 23:15:00 - missing_time_splite
    132. 

  132. 2025-03-28 09:48:43,053 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    133. 

  133. 2025-03-28 09:48:43,054 - data_handler.py - INFO - 2022-08-07 00:00:00 ~ 2022-08-07 23:15:00 - missing_time_splite
    134. 

  134. 2025-03-28 09:48:43,054 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    135. 

  135. 2025-03-28 09:48:43,055 - 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
    136. 

  136. 2025-03-28 09:48:43,055 - data_handler.py - INFO - 需要补值的总点数：4.0 - missing_time_splite
    137. 

  137. 2025-03-28 09:48:43,056 - data_handler.py - INFO - 再次测算，需要插值的总点数为：4.0 - fill_train_data
    138. 

  138. 2025-03-28 09:48:43,057 - 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
    139. 

  139. 2025-03-28 09:48:43,061 - 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
    140. 

  140. 2025-03-28 09:48:43,062 - 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
    141. 

  141. 2025-03-28 09:48:43,063 - 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
    142. 

  142. 2025-03-28 09:48:43,064 - 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
    143. 

  143. 2025-03-28 09:48:43,065 - 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
    144. 

  144. 2025-03-28 09:48:43,067 - 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
    145. 

  145. 2025-03-28 09:48:43,067 - 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
    146. 

  146. 2025-03-28 09:48:43,069 - 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
    147. 

  147. 2025-03-28 09:48:43,071 - 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
    148. 

  148. 2025-03-28 09:48:43,072 - 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
    149. 

  149. 2025-03-28 09:48:43,073 - 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
    150. 

  150. 2025-03-28 09:48:43,074 - 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
    151. 

  151. 2025-03-28 09:48:43,075 - 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
    152. 

  152. 2025-03-28 09:48:43,076 - 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
    153. 

  153. 2025-03-28 09:48:43,076 - data_handler.py - INFO - 训练集分成了15段，实际一共补值4点 - data_fill
    154. 

  154. 2025-03-28 09:48:45,483 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    155. 

  155. 2025-03-28 09:48:45,484 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    156. 

  156. 2025-03-28 09:48:45,484 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    157. 

  157. 2025-03-28 09:48:45,484 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    158. 

  158. 2025-03-28 09:48:45,484 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    159. 

  159. 2025-03-28 09:48:45,484 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    160. 

  160. 2025-03-28 09:48:45,484 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    161. 

  161. 2025-03-28 09:48:45,484 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    162. 

  162. 2025-03-28 09:48:45,672 - 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
    163. 

  163. 2025-03-28 09:48:45,672 - 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
    164. 

  164. 2025-03-28 09:48:56,555 - tf_fmi.py - INFO - -----模型训练经过27轮迭代----- - training
    165. 

  165. 2025-03-28 09:48:56,556 - tf_fmi.py - INFO - 训练集损失函数为：[50.81253 27.42134 15.70889  9.45657  5.92459  3.68006  2.2472   1.37757
    166. 

  166.   0.88398  0.61301  0.46807  0.38933  0.34411  0.32137  0.30921  0.30322
    167. 

  167.   0.29953  0.29853  0.29652  0.29489  0.29435  0.29366  0.29466  0.29338
    168. 

  168.   0.29248  0.29236  0.2922 ] - training
    169. 

  169. 2025-03-28 09:48:56,556 - tf_fmi.py - INFO - 验证集损失函数为：[31.24954 19.522   11.71835  7.35976  4.65779  2.91401  1.82848  1.18742
    170. 

  170.   0.80744  0.58592  0.46629  0.40836  0.38216  0.37268  0.37405  0.36883
    171. 

  171.   0.36879  0.38428  0.37288  0.37732  0.38255  0.39734  0.39197  0.41757
    172. 

  172.   0.39943  0.39559  0.42329] - training
    173. 

  173. 2025-03-28 09:48:56,621 - 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
    174.