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2025-03-25
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south-forecast.2025-03-25.0.log

south-forecast.2025-03-25.0.log 9.7 KB
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  1. 2025-03-25 08:48:02,492 - 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. 2025-03-25 08:48:02,832 - tf_lstm_train.py - INFO - Program starts execution! - model_training
    4. 

  4. 2025-03-25 08:48:02,886 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    5. 

  5. 2025-03-25 08:48:02,895 - data_cleaning.py - INFO - 列清洗：清洗的列有：{'HubSpeed', 'HubDireciton'} - data_column_cleaning
    6. 

  6. 2025-03-25 08:48:02,913 - 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. 2025-03-25 08:48:02,933 - data_handler.py - INFO - 2022-05-05 23:45:00 ~ 2022-05-09 00:00:00 - missing_time_splite
    8. 

  8. 2025-03-25 08:48:02,933 - data_handler.py - INFO - 缺失点数：288.0 - missing_time_splite
    9. 

  9. 2025-03-25 08:48:02,966 - data_handler.py - INFO - 2022-06-25 07:45:00 ~ 2022-06-25 08:15:00 - missing_time_splite
    10. 

  10. 2025-03-25 08:48:02,966 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
    11. 

  11. 2025-03-25 08:48:02,966 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
    12. 

  12. 2025-03-25 08:48:02,967 - data_handler.py - INFO - 2022-06-27 06:45:00 ~ 2022-06-27 07:15:00 - missing_time_splite
    13. 

  13. 2025-03-25 08:48:02,968 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
    14. 

  14. 2025-03-25 08:48:02,968 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
    15. 

  15. 2025-03-25 08:48:02,971 - data_handler.py - INFO - 2022-07-01 06:30:00 ~ 2022-07-01 23:45:00 - missing_time_splite
    16. 

  16. 2025-03-25 08:48:02,971 - data_handler.py - INFO - 缺失点数：68.0 - missing_time_splite
    17. 

  17. 2025-03-25 08:48:02,976 - data_handler.py - INFO - 2022-07-08 06:15:00 ~ 2022-07-08 23:45:00 - missing_time_splite
    18. 

  18. 2025-03-25 08:48:02,977 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    19. 

  19. 2025-03-25 08:48:02,978 - data_handler.py - INFO - 2022-07-09 06:15:00 ~ 2022-07-09 23:45:00 - missing_time_splite
    20. 

  20. 2025-03-25 08:48:02,978 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    21. 

  21. 2025-03-25 08:48:02,979 - data_handler.py - INFO - 2022-07-10 06:15:00 ~ 2022-07-10 23:45:00 - missing_time_splite
    22. 

  22. 2025-03-25 08:48:02,979 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
    23. 

  23. 2025-03-25 08:48:02,980 - data_handler.py - INFO - 2022-07-11 06:00:00 ~ 2022-07-11 23:45:00 - missing_time_splite
    24. 

  24. 2025-03-25 08:48:02,980 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    25. 

  25. 2025-03-25 08:48:02,981 - data_handler.py - INFO - 2022-07-12 06:00:00 ~ 2022-07-12 23:45:00 - missing_time_splite
    26. 

  26. 2025-03-25 08:48:02,981 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    27. 

  27. 2025-03-25 08:48:02,982 - data_handler.py - INFO - 2022-07-13 06:00:00 ~ 2022-07-13 23:45:00 - missing_time_splite
    28. 

  28. 2025-03-25 08:48:02,982 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
    29. 

  29. 2025-03-25 08:48:02,984 - data_handler.py - INFO - 2022-07-14 23:00:00 ~ 2022-07-14 23:45:00 - missing_time_splite
    30. 

  30. 2025-03-25 08:48:02,984 - data_handler.py - INFO - 缺失点数：2.0 - missing_time_splite
    31. 

  31. 2025-03-25 08:48:02,984 - data_handler.py - INFO - 需要补值的点数：2.0 - missing_time_splite
    32. 

  32. 2025-03-25 08:48:02,997 - data_handler.py - INFO - 2022-08-01 20:00:00 ~ 2022-08-01 23:45:00 - missing_time_splite
    33. 

  33. 2025-03-25 08:48:02,997 - data_handler.py - INFO - 缺失点数：14.0 - missing_time_splite
    34. 

  34. 2025-03-25 08:48:02,999 - data_handler.py - INFO - 2022-08-02 21:00:00 ~ 2022-08-02 23:45:00 - missing_time_splite
    35. 

  35. 2025-03-25 08:48:02,999 - data_handler.py - INFO - 缺失点数：10.0 - missing_time_splite
    36. 

  36. 2025-03-25 08:48:03,000 - data_handler.py - INFO - 2022-08-04 00:00:00 ~ 2022-08-04 23:15:00 - missing_time_splite
    37. 

  37. 2025-03-25 08:48:03,001 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    38. 

  38. 2025-03-25 08:48:03,001 - data_handler.py - INFO - 2022-08-05 00:00:00 ~ 2022-08-05 23:15:00 - missing_time_splite
    39. 

  39. 2025-03-25 08:48:03,001 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    40. 

  40. 2025-03-25 08:48:03,002 - data_handler.py - INFO - 2022-08-06 00:00:00 ~ 2022-08-06 23:15:00 - missing_time_splite
    41. 

  41. 2025-03-25 08:48:03,002 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    42. 

  42. 2025-03-25 08:48:03,003 - data_handler.py - INFO - 2022-08-07 00:00:00 ~ 2022-08-07 23:15:00 - missing_time_splite
    43. 

  43. 2025-03-25 08:48:03,003 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
    44. 

  44. 2025-03-25 08:48:03,005 - 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. 2025-03-25 08:48:03,005 - data_handler.py - INFO - 需要补值的总点数：4.0 - missing_time_splite
    46. 

  46. 2025-03-25 08:48:03,005 - data_handler.py - INFO - 再次测算，需要插值的总点数为：4.0 - fill_train_data
    47. 

  47. 2025-03-25 08:48:03,014 - 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. 2025-03-25 08:48:03,017 - 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. 2025-03-25 08:48:03,019 - 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. 2025-03-25 08:48:03,020 - 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. 2025-03-25 08:48:03,021 - 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. 2025-03-25 08:48:03,022 - 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. 2025-03-25 08:48:03,023 - 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. 2025-03-25 08:48:03,024 - 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. 2025-03-25 08:48:03,026 - 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. 2025-03-25 08:48:03,027 - 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. 2025-03-25 08:48:03,028 - 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. 2025-03-25 08:48:03,029 - 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. 2025-03-25 08:48:03,031 - 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. 2025-03-25 08:48:03,032 - 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. 2025-03-25 08:48:03,033 - 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. 2025-03-25 08:48:03,033 - data_handler.py - INFO - 训练集分成了15段，实际一共补值4点 - data_fill
    63. 

  63. 2025-03-25 08:48:05,457 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    64. 

  64. 2025-03-25 08:48:05,457 - 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. 2025-03-25 08:48:05,457 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    66. 

  66. 2025-03-25 08:48:05,457 - 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. 2025-03-25 08:48:05,457 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    68. 

  68. 2025-03-25 08:48:05,457 - 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. 2025-03-25 08:48:05,457 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
    70. 

  70. 2025-03-25 08:48:05,457 - 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. 2025-03-25 08:48:05,669 - dbmg.py - INFO - ⚠️ 未找到模型 'fmi' 的有效记录 - get_keras_model_from_mongo
    72. 

  72. 2025-03-25 08:48:05,670 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    73. 

  73. 2025-03-25 08:48:13,323 - tf_lstm.py - INFO - -----模型训练经过28轮迭代----- - training
    74. 

  74. 2025-03-25 08:48:13,323 - tf_lstm.py - INFO - 训练集损失函数为：[0.98709 0.4857  0.38789 0.34428 0.31769 0.2972  0.28397 0.27552 0.27048
    75. 

  75.  0.26568 0.26268 0.2593  0.25744 0.25489 0.25417 0.25239 0.2496  0.25125
    76. 

  76.  0.24856 0.2473  0.24671 0.24601 0.24413 0.24449 0.24437 0.24263 0.24266
    77. 

  77.  0.24314] - training
    78. 

  78. 2025-03-25 08:48:13,323 - tf_lstm.py - INFO - 验证集损失函数为：[0.61994 0.45012 0.40129 0.37275 0.3428  0.32806 0.32041 0.3169  0.31217
    79. 

  79.  0.30889 0.30421 0.30505 0.30078 0.3012  0.29778 0.29507 0.29904 0.2944
    80. 

  80.  0.30124 0.30423 0.31063 0.30335 0.30427 0.3074  0.30603 0.30948 0.30855
    81. 

  81.  0.30437] - training
    82. 

  82. 2025-03-25 08:48:13,364 - dbmg.py - INFO - ✅ 模型 fmi 保存成功 | 文档ID: 67e1fd4dce6f69e640c3ad38 - insert_trained_model_into_mongo
    83. 

  83. 2025-03-25 08:48:13,394 - dbmg.py - INFO - ✅ 缩放器 fmi 保存成功 | 文档ID: 67e1fd4dce6f69e640c3ad3a - insert_scaler_model_into_mongo
    84. 

  84. 2025-03-25 08:48:41,782 - 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. 2025-03-25 08:48:42,030 - dbmg.py - INFO - ✅ 成功加载 fmi 的缩放器 (版本时间: 2025-03-25 08:48:13) - get_scaler_model_from_mongo
    87. 

  87. 2025-03-25 08:48:42,121 - dbmg.py - INFO - fmi 模型成功从 MongoDB 加载！ - get_keras_model_from_mongo
    88. 

  88. 2025-03-25 08:48:42,122 - dbmg.py - INFO - 🧹 已清理临时文件: C:\Users\ADMINI~1\AppData\Local\Temp\tmph1bvs151.keras - get_keras_model_from_mongo
    89. 

  89. 2025-03-25 08:48:42,429 - tf_lstm.py - INFO - 执行预测方法 - predict
    90.