Página inicial Explorar Ajuda
Entrar
liudawei
/
compete
1
0
Fork 0
Arquivos Issues 0 Pull Requests 0 Wiki
Branch: master
Branches Tags
compete
/
app
/
logs
/
2025-04-07
/
south-forecast.2025-04-07.0.log

south-forecast.2025-04-07.0.log 19 KB
Link permanente Histórico Raw

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164 
  1. 2025-04-07 13:49:37,112 - 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. 2025-04-07 13:49:37,470 - tf_fmi_train.py - INFO - Program starts execution! - model_training
    4. 

  4. 2025-04-07 13:49:37,529 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    5. 

  5. 2025-04-07 13:49:37,544 - data_cleaning.py - INFO - 列清洗：清洗的列有：{'HubSpeed', 'HubDireciton'} - data_column_cleaning
    6. 

  6. 2025-04-07 13:49:37,566 - 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. 2025-04-07 13:49:37,603 - data_handler.py - INFO - 2022-05-05 23:45:00 ~ 2022-05-09 00:00:00 - missing_time_splite
    8. 

  8. 2025-04-07 13:49:37,604 - data_handler.py - INFO - 缺失点数：288.0 - missing_time_splite
    9. 

  9. 2025-04-07 13:49:37,636 - data_handler.py - INFO - 2022-06-25 07:45:00 ~ 2022-06-25 08:15:00 - missing_time_splite
    10. 

  10. 2025-04-07 13:49:37,636 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
    11. 

  11. 2025-04-07 13:49:37,637 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
    12. 

  12. 2025-04-07 13:49:37,639 - data_handler.py - INFO - 2022-06-27 06:45:00 ~ 2022-06-27 07:15:00 - missing_time_splite
    13. 

  13. 2025-04-07 13:49:37,639 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
    14. 

  14. 2025-04-07 13:49:37,639 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
    15. 

  15. 2025-04-07 13:49:37,642 - data_handler.py - INFO - 2022-07-01 06:30:00 ~ 2022-07-01 23:45:00 - missing_time_splite
    16. 

  16. 2025-04-07 13:49:37,643 - data_handler.py - INFO - 缺失点数：68.0 - missing_time_splite
    17. 

  17. 2025-04-07 13:49:37,648 - data_handler.py - INFO - 2022-07-08 06:15:00 ~ 2022-07-08 23:45:00 - missing_time_splite
    18. 

  18. 2025-04-07 13:49:37,648 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    19. 

  19. 2025-04-07 13:49:37,649 - data_handler.py - INFO - 2022-07-09 06:15:00 ~ 2022-07-09 23:45:00 - missing_time_splite
    20. 

  20. 2025-04-07 13:49:37,650 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    21. 

  21. 2025-04-07 13:49:37,650 - data_handler.py - INFO - 2022-07-10 06:15:00 ~ 2022-07-10 23:45:00 - missing_time_splite
    22. 

  22. 2025-04-07 13:49:37,651 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    23. 

  23. 2025-04-07 13:49:37,652 - data_handler.py - INFO - 2022-07-11 06:00:00 ~ 2022-07-11 23:45:00 - missing_time_splite
    24. 

  24. 2025-04-07 13:49:37,652 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    25. 

  25. 2025-04-07 13:49:37,653 - data_handler.py - INFO - 2022-07-12 06:00:00 ~ 2022-07-12 23:45:00 - missing_time_splite
    26. 

  26. 2025-04-07 13:49:37,653 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    27. 

  27. 2025-04-07 13:49:37,654 - data_handler.py - INFO - 2022-07-13 06:00:00 ~ 2022-07-13 23:45:00 - missing_time_splite
    28. 

  28. 2025-04-07 13:49:37,654 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    29. 

  29. 2025-04-07 13:49:37,654 - data_handler.py - INFO - 2022-07-14 23:00:00 ~ 2022-07-14 23:45:00 - missing_time_splite
    30. 

  30. 2025-04-07 13:49:37,655 - data_handler.py - INFO - 缺失点数：2.0 - missing_time_splite
    31. 

  31. 2025-04-07 13:49:37,655 - data_handler.py - INFO - 需要补值的点数：2.0 - missing_time_splite
    32. 

  32. 2025-04-07 13:49:37,668 - data_handler.py - INFO - 2022-08-01 20:00:00 ~ 2022-08-01 23:45:00 - missing_time_splite
    33. 

  33. 2025-04-07 13:49:37,668 - data_handler.py - INFO - 缺失点数：14.0 - missing_time_splite
    34. 

  34. 2025-04-07 13:49:37,670 - data_handler.py - INFO - 2022-08-02 21:00:00 ~ 2022-08-02 23:45:00 - missing_time_splite
    35. 

  35. 2025-04-07 13:49:37,670 - data_handler.py - INFO - 缺失点数：10.0 - missing_time_splite
    36. 

  36. 2025-04-07 13:49:37,671 - data_handler.py - INFO - 2022-08-04 00:00:00 ~ 2022-08-04 23:15:00 - missing_time_splite
    37. 

  37. 2025-04-07 13:49:37,671 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    38. 

  38. 2025-04-07 13:49:37,672 - data_handler.py - INFO - 2022-08-05 00:00:00 ~ 2022-08-05 23:15:00 - missing_time_splite
    39. 

  39. 2025-04-07 13:49:37,673 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    40. 

  40. 2025-04-07 13:49:37,673 - data_handler.py - INFO - 2022-08-06 00:00:00 ~ 2022-08-06 23:15:00 - missing_time_splite
    41. 

  41. 2025-04-07 13:49:37,674 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    42. 

  42. 2025-04-07 13:49:37,674 - data_handler.py - INFO - 2022-08-07 00:00:00 ~ 2022-08-07 23:15:00 - missing_time_splite
    43. 

  43. 2025-04-07 13:49:37,674 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    44. 

  44. 2025-04-07 13:49:37,675 - 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. 2025-04-07 13:49:37,675 - data_handler.py - INFO - 需要补值的总点数：4.0 - missing_time_splite
    46. 

  46. 2025-04-07 13:49:37,676 - data_handler.py - INFO - 再次测算，需要插值的总点数为：4.0 - fill_train_data
    47. 

  47. 2025-04-07 13:49:37,689 - 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. 2025-04-07 13:49:37,696 - 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. 2025-04-07 13:49:37,697 - 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. 2025-04-07 13:49:37,698 - 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. 2025-04-07 13:49:37,699 - 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. 2025-04-07 13:49:37,700 - 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. 2025-04-07 13:49:37,701 - 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. 2025-04-07 13:49:37,702 - 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. 2025-04-07 13:49:37,704 - 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. 2025-04-07 13:49:37,705 - 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. 2025-04-07 13:49:37,707 - 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. 2025-04-07 13:49:37,708 - 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. 2025-04-07 13:49:37,708 - 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. 2025-04-07 13:49:37,710 - 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. 2025-04-07 13:49:37,711 - 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. 2025-04-07 13:49:37,711 - data_handler.py - INFO - 训练集分成了15段，实际一共补值4点 - data_fill
    63. 

  63. 2025-04-07 13:49:40,227 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    64. 

  64. 2025-04-07 13:49:40,227 - 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. 2025-04-07 13:49:40,227 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    66. 

  66. 2025-04-07 13:49:40,228 - 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. 2025-04-07 13:49:40,228 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    68. 

  68. 2025-04-07 13:49:40,228 - 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. 2025-04-07 13:49:40,228 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    70. 

  70. 2025-04-07 13:49:40,228 - 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. 2025-04-07 13:49:40,596 - 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. 2025-04-07 13:49:40,596 - 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. 2025-04-07 13:49:52,160 - tf_fmi.py - INFO - -----模型训练经过26轮迭代----- - training
    74. 

  74. 2025-04-07 13:49:52,161 - tf_fmi.py - INFO - 训练集损失函数为：[51.50243 28.06182 16.38248 10.17221  6.57951  4.203    2.61306  1.60485
    75. 

  75.   1.00219  0.66421  0.4868   0.39559  0.35092  0.33071  0.3226   0.31509
    76. 

  76.   0.31194  0.31384  0.317    0.30989  0.30833  0.31057  0.30849  0.30717
    77. 

  77.   0.30716  0.31247] - training
    78. 

  78. 2025-04-07 13:49:52,161 - tf_fmi.py - INFO - 验证集损失函数为：[31.6882  20.1309  12.39297  8.02596  5.24482  3.35177  2.13436  1.35984
    79. 

  79.   0.88974  0.62046  0.48093  0.41653  0.37632  0.35305  0.34172  0.3387
    80. 

  80.   0.34334  0.34849  0.35522  0.352    0.34934  0.36202  0.36299  0.37276
    81. 

  81.   0.37557  0.37552] - training
    82. 

  82. 2025-04-07 13:49:52,237 - 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. 2025-04-07 13:49:52,291 - tf_fmi_train.py - INFO - fmi训练任务：用了 14.820847511291504 秒  - model_training
    84. 

  84. 2025-04-07 13:51:33,865 - 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.
    85. 

  85.  - _tfmw_add_deprecation_warning
    86. 

  86. 2025-04-07 13:51:33,984 - tf_lstm_train.py - INFO - Program starts execution! - model_training
    87. 

  87. 2025-04-07 13:51:34,039 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    88. 

  88. 2025-04-07 13:51:34,049 - data_cleaning.py - INFO - 列清洗：清洗的列有：{'HubDireciton', 'HubSpeed'} - data_column_cleaning
    89. 

  89. 2025-04-07 13:51:34,063 - data_cleaning.py - INFO - 行清洗：清洗的行数有：881，缺失的列有： - key_field_row_cleaning
    90. 

  90. 2025-04-07 13:51:34,074 - data_handler.py - INFO - 2022-05-05 23:45:00 ~ 2022-05-09 00:00:00 - missing_time_splite
    91. 

  91. 2025-04-07 13:51:34,075 - data_handler.py - INFO - 缺失点数：288.0 - missing_time_splite
    92. 

  92. 2025-04-07 13:51:34,107 - data_handler.py - INFO - 2022-06-25 07:45:00 ~ 2022-06-25 08:15:00 - missing_time_splite
    93. 

  93. 2025-04-07 13:51:34,107 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
    94. 

  94. 2025-04-07 13:51:34,108 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
    95. 

  95. 2025-04-07 13:51:34,109 - data_handler.py - INFO - 2022-06-27 06:45:00 ~ 2022-06-27 07:15:00 - missing_time_splite
    96. 

  96. 2025-04-07 13:51:34,109 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
    97. 

  97. 2025-04-07 13:51:34,109 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
    98. 

  98. 2025-04-07 13:51:34,113 - data_handler.py - INFO - 2022-07-01 06:30:00 ~ 2022-07-01 23:45:00 - missing_time_splite
    99. 

  99. 2025-04-07 13:51:34,113 - data_handler.py - INFO - 缺失点数：68.0 - missing_time_splite
    100. 

  100. 2025-04-07 13:51:34,118 - data_handler.py - INFO - 2022-07-08 06:15:00 ~ 2022-07-08 23:45:00 - missing_time_splite
    101. 

  101. 2025-04-07 13:51:34,118 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    102. 

  102. 2025-04-07 13:51:34,119 - data_handler.py - INFO - 2022-07-09 06:15:00 ~ 2022-07-09 23:45:00 - missing_time_splite
    103. 

  103. 2025-04-07 13:51:34,120 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    104. 

  104. 2025-04-07 13:51:34,120 - data_handler.py - INFO - 2022-07-10 06:15:00 ~ 2022-07-10 23:45:00 - missing_time_splite
    105. 

  105. 2025-04-07 13:51:34,121 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    106. 

  106. 2025-04-07 13:51:34,122 - data_handler.py - INFO - 2022-07-11 06:00:00 ~ 2022-07-11 23:45:00 - missing_time_splite
    107. 

  107. 2025-04-07 13:51:34,122 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    108. 

  108. 2025-04-07 13:51:34,123 - data_handler.py - INFO - 2022-07-12 06:00:00 ~ 2022-07-12 23:45:00 - missing_time_splite
    109. 

  109. 2025-04-07 13:51:34,123 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    110. 

  110. 2025-04-07 13:51:34,124 - data_handler.py - INFO - 2022-07-13 06:00:00 ~ 2022-07-13 23:45:00 - missing_time_splite
    111. 

  111. 2025-04-07 13:51:34,124 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    112. 

  112. 2025-04-07 13:51:34,125 - data_handler.py - INFO - 2022-07-14 23:00:00 ~ 2022-07-14 23:45:00 - missing_time_splite
    113. 

  113. 2025-04-07 13:51:34,125 - data_handler.py - INFO - 缺失点数：2.0 - missing_time_splite
    114. 

  114. 2025-04-07 13:51:34,125 - data_handler.py - INFO - 需要补值的点数：2.0 - missing_time_splite
    115. 

  115. 2025-04-07 13:51:34,138 - data_handler.py - INFO - 2022-08-01 20:00:00 ~ 2022-08-01 23:45:00 - missing_time_splite
    116. 

  116. 2025-04-07 13:51:34,138 - data_handler.py - INFO - 缺失点数：14.0 - missing_time_splite
    117. 

  117. 2025-04-07 13:51:34,140 - data_handler.py - INFO - 2022-08-02 21:00:00 ~ 2022-08-02 23:45:00 - missing_time_splite
    118. 

  118. 2025-04-07 13:51:34,140 - data_handler.py - INFO - 缺失点数：10.0 - missing_time_splite
    119. 

  119. 2025-04-07 13:51:34,141 - data_handler.py - INFO - 2022-08-04 00:00:00 ~ 2022-08-04 23:15:00 - missing_time_splite
    120. 

  120. 2025-04-07 13:51:34,141 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    121. 

  121. 2025-04-07 13:51:34,142 - data_handler.py - INFO - 2022-08-05 00:00:00 ~ 2022-08-05 23:15:00 - missing_time_splite
    122. 

  122. 2025-04-07 13:51:34,142 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    123. 

  123. 2025-04-07 13:51:34,143 - data_handler.py - INFO - 2022-08-06 00:00:00 ~ 2022-08-06 23:15:00 - missing_time_splite
    124. 

  124. 2025-04-07 13:51:34,143 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    125. 

  125. 2025-04-07 13:51:34,144 - data_handler.py - INFO - 2022-08-07 00:00:00 ~ 2022-08-07 23:15:00 - missing_time_splite
    126. 

  126. 2025-04-07 13:51:34,144 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    127. 

  127. 2025-04-07 13:51:34,144 - 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
    128. 

  128. 2025-04-07 13:51:34,144 - data_handler.py - INFO - 需要补值的总点数：4.0 - missing_time_splite
    129. 

  129. 2025-04-07 13:51:34,145 - data_handler.py - INFO - 再次测算，需要插值的总点数为：4.0 - fill_train_data
    130. 

  130. 2025-04-07 13:51:34,147 - 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
    131. 

  131. 2025-04-07 13:51:34,150 - 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
    132. 

  132. 2025-04-07 13:51:34,152 - 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
    133. 

  133. 2025-04-07 13:51:34,153 - 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
    134. 

  134. 2025-04-07 13:51:34,154 - 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
    135. 

  135. 2025-04-07 13:51:34,155 - 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
    136. 

  136. 2025-04-07 13:51:34,156 - 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
    137. 

  137. 2025-04-07 13:51:34,157 - 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
    138. 

  138. 2025-04-07 13:51:34,159 - 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
    139. 

  139. 2025-04-07 13:51:34,160 - 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
    140. 

  140. 2025-04-07 13:51:34,162 - 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
    141. 

  141. 2025-04-07 13:51:34,163 - 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
    142. 

  142. 2025-04-07 13:51:34,164 - 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
    143. 

  143. 2025-04-07 13:51:34,165 - 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
    144. 

  144. 2025-04-07 13:51:34,166 - 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
    145. 

  145. 2025-04-07 13:51:34,166 - data_handler.py - INFO - 训练集分成了15段，实际一共补值4点 - data_fill
    146. 

  146. 2025-04-07 13:51:36,672 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    147. 

  147. 2025-04-07 13:51:36,673 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    148. 

  148. 2025-04-07 13:51:36,673 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    149. 

  149. 2025-04-07 13:51:36,673 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    150. 

  150. 2025-04-07 13:51:36,673 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    151. 

  151. 2025-04-07 13:51:36,673 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    152. 

  152. 2025-04-07 13:51:36,673 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    153. 

  153. 2025-04-07 13:51:36,673 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
    154. 

  154. 2025-04-07 13:51:36,889 - 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
    155. 

  155. 2025-04-07 13:51:36,889 - tf_lstm.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
    156. 

  156. 2025-04-07 13:51:45,083 - tf_lstm.py - INFO - -----模型训练经过27轮迭代----- - training
    157. 

  157. 2025-04-07 13:51:45,083 - tf_lstm.py - INFO - 训练集损失函数为：[0.98759 0.49017 0.39408 0.34942 0.32057 0.30133 0.28753 0.27798 0.2714
    158. 

  158.  0.26692 0.26468 0.26134 0.25836 0.25676 0.25471 0.25288 0.25181 0.24975
    159. 

  159.  0.24782 0.24746 0.247   0.24641 0.24581 0.24394 0.24685 0.24457 0.24353] - training
    160. 

  160. 2025-04-07 13:51:45,083 - tf_lstm.py - INFO - 验证集损失函数为：[0.61675 0.46075 0.40829 0.37418 0.35326 0.33725 0.32495 0.31847 0.31484
    161. 

  161.  0.31264 0.30734 0.30485 0.30338 0.29994 0.29824 0.29777 0.29661 0.29866
    162. 

  162.  0.3054  0.31325 0.30852 0.29957 0.30066 0.3148  0.30761 0.30359 0.30121] - training
    163. 

  163. 2025-04-07 13:51:45,109 - 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
    164. 

  164. 2025-04-07 13:51:45,111 - tf_lstm_train.py - INFO - lstm训练任务：用了 11.127336978912354 秒  - model_training
    165.