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更改上传数据日期格式

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jingboyitiji 2025-03-13 15:38:12 +08:00
parent 9dee0f810c
commit 49a0998126
7 changed files with 26 additions and 15 deletions
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4
config_jingbo.py
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@ -198,7 +198,7 @@ table_name = 'v_tbl_crude_oil_warning'
# 开关 # 开关
is_train = True # 是否训练 is_train = True # 是否训练
is_debug = True # 是否调试 is_debug = False # 是否调试
is_eta = True # 是否使用eta接口 is_eta = True # 是否使用eta接口
is_market = True # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效 is_market = True # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
is_timefurture = True # 是否使用时间特征 is_timefurture = True # 是否使用时间特征
@ -206,7 +206,7 @@ is_fivemodels = False # 是否使用之前保存的最佳的5个模型
is_edbcode = False # 特征使用edbcoding列表中的 is_edbcode = False # 特征使用edbcoding列表中的
is_edbnamelist = False # 自定义特征，对应上面的edbnamelist is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
is_update_eta = False # 预测结果上传到eta is_update_eta = False # 预测结果上传到eta
is_update_report = False # 是否上传报告 is_update_report = True # 是否上传报告
is_update_warning_data = False # 是否上传预警数据 is_update_warning_data = False # 是否上传预警数据
is_update_predict_value = True # 是否上传预测值到市场信息平台 is_update_predict_value = True # 是否上传预测值到市场信息平台
is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征 is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征

4
config_jingbo_yuedu.py
View File

@ -194,7 +194,7 @@ table_name = 'v_tbl_crude_oil_warning'
# 开关 # 开关
is_train = False # 是否训练 is_train = True # 是否训练
is_debug = False # 是否调试 is_debug = False # 是否调试
is_eta = True # 是否使用eta接口 is_eta = True # 是否使用eta接口
is_market = True # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效 is_market = True # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
@ -203,7 +203,7 @@ is_fivemodels = False # 是否使用之前保存的最佳的5个模型
is_edbcode = False # 特征使用edbcoding列表中的 is_edbcode = False # 特征使用edbcoding列表中的
is_edbnamelist = False # 自定义特征，对应上面的edbnamelist is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
is_update_eta = False # 预测结果上传到eta is_update_eta = False # 预测结果上传到eta
is_update_report = False # 是否上传报告 is_update_report = True # 是否上传报告
is_update_warning_data = False # 是否上传预警数据 is_update_warning_data = False # 是否上传预警数据
is_update_predict_value = True # 是否上传预测值到市场信息平台 is_update_predict_value = True # 是否上传预测值到市场信息平台
is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征 is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征

4
config_jingbo_zhoudu.py
View File

@ -195,14 +195,14 @@ table_name = 'v_tbl_crude_oil_warning'
# 开关 # 开关
is_train = True # 是否训练 is_train = True # 是否训练
is_debug = False # 是否调试 is_debug = False # 是否调试
is_eta = False # 是否使用eta接口 is_eta = True # 是否使用eta接口
is_market = True # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效 is_market = True # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
is_timefurture = True # 是否使用时间特征 is_timefurture = True # 是否使用时间特征
is_fivemodels = False # 是否使用之前保存的最佳的5个模型 is_fivemodels = False # 是否使用之前保存的最佳的5个模型
is_edbcode = False # 特征使用edbcoding列表中的 is_edbcode = False # 特征使用edbcoding列表中的
is_edbnamelist = False # 自定义特征，对应上面的edbnamelist is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
is_update_eta = False # 预测结果上传到eta is_update_eta = False # 预测结果上传到eta
is_update_report = False # 是否上传报告 is_update_report = True # 是否上传报告
is_update_warning_data = False # 是否上传预警数据 is_update_warning_data = False # 是否上传预警数据
is_update_predict_value = True # 是否上传预测值到市场信息平台 is_update_predict_value = True # 是否上传预测值到市场信息平台
is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征 is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征

7
main_yuanyou.py
View File

@ -105,17 +105,20 @@ def push_market_value():
# 准备要推送的数据 # 准备要推送的数据
first_mean = predictdata_df['top_models_mean'].iloc[0] first_mean = predictdata_df['top_models_mean'].iloc[0]
last_mean = predictdata_df['top_models_mean'].iloc[-1] last_mean = predictdata_df['top_models_mean'].iloc[-1]
# 保留两位小数
first_mean = round(first_mean, 2)
last_mean = round(last_mean, 2)
predictdata = [ predictdata = [
{ {
"dataItemNo": global_config['bdwd_items']['ciri'], "dataItemNo": global_config['bdwd_items']['ciri'],
"dataDate": global_config['end_time'], "dataDate": global_config['end_time'].replace('-',''),
"dataStatus": "add", "dataStatus": "add",
"dataValue": first_mean "dataValue": first_mean
}, },
{ {
"dataItemNo": global_config['bdwd_items']['benzhou'], "dataItemNo": global_config['bdwd_items']['benzhou'],
"dataDate": global_config['end_time'], "dataDate": global_config['end_time'].replace('-',''),
"dataStatus": "add", "dataStatus": "add",
"dataValue": last_mean "dataValue": last_mean
} }

13
main_yuanyou_yuedu.py
View File

@ -107,29 +107,34 @@ def push_market_value():
cieryue_mean = predictdata_df['top_models_mean'].iloc[1] cieryue_mean = predictdata_df['top_models_mean'].iloc[1]
cisanyue_mean = predictdata_df['top_models_mean'].iloc[2] cisanyue_mean = predictdata_df['top_models_mean'].iloc[2]
cisieryue_mean = predictdata_df['top_models_mean'].iloc[3] cisieryue_mean = predictdata_df['top_models_mean'].iloc[3]
# 保留两位小数
ciyue_mean = round(ciyue_mean, 2)
cieryue_mean = round(cieryue_mean, 2)
cisanyue_mean = round(cisanyue_mean, 2)
cisieryue_mean = round(cisieryue_mean, 2)
predictdata = [ predictdata = [
{ {
"dataItemNo": global_config['bdwd_items']['ciyue'], "dataItemNo": global_config['bdwd_items']['ciyue'],
"dataDate": global_config['end_time'], "dataDate": global_config['end_time'].replace('-',''),
"dataStatus": "add", "dataStatus": "add",
"dataValue": ciyue_mean "dataValue": ciyue_mean
}, },
{ {
"dataItemNo": global_config['bdwd_items']['cieryue'], "dataItemNo": global_config['bdwd_items']['cieryue'],
"dataDate": global_config['end_time'], "dataDate": global_config['end_time'].replace('-',''),
"dataStatus": "add", "dataStatus": "add",
"dataValue": cieryue_mean "dataValue": cieryue_mean
}, },
{ {
"dataItemNo": global_config['bdwd_items']['cisanyue'], "dataItemNo": global_config['bdwd_items']['cisanyue'],
"dataDate": global_config['end_time'], "dataDate": global_config['end_time'].replace('-',''),
"dataStatus": "add", "dataStatus": "add",
"dataValue": cisanyue_mean "dataValue": cisanyue_mean
}, },
{ {
"dataItemNo": global_config['bdwd_items']['cisiyue'], "dataItemNo": global_config['bdwd_items']['cisiyue'],
"dataDate": global_config['end_time'], "dataDate": global_config['end_time'].replace('-',''),
"dataStatus": "add", "dataStatus": "add",
"dataValue": cisieryue_mean "dataValue": cisieryue_mean
} }

7
main_yuanyou_zhoudu.py
View File

@ -105,17 +105,20 @@ def push_market_value():
# 准备要推送的数据 # 准备要推送的数据
first_mean = predictdata_df['top_models_mean'].iloc[0] first_mean = predictdata_df['top_models_mean'].iloc[0]
last_mean = predictdata_df['top_models_mean'].iloc[-1] last_mean = predictdata_df['top_models_mean'].iloc[-1]
# 保留两位小数
first_mean = round(first_mean, 2)
last_mean = round(last_mean, 2)
predictdata = [ predictdata = [
{ {
"dataItemNo": global_config['bdwd_items']['cizhou'], "dataItemNo": global_config['bdwd_items']['cizhou'],
"dataDate": global_config['end_time'], "dataDate": global_config['end_time'].replace('-',''),
"dataStatus": "add", "dataStatus": "add",
"dataValue": first_mean "dataValue": first_mean
}, },
{ {
"dataItemNo": global_config['bdwd_items']['gezhou'], "dataItemNo": global_config['bdwd_items']['gezhou'],
"dataDate": global_config['end_time'], "dataDate": global_config['end_time'].replace('-',''),
"dataStatus": "add", "dataStatus": "add",
"dataValue": last_mean "dataValue": last_mean
} }

2
models/nerulforcastmodels.py
View File

@ -165,7 +165,7 @@ def ex_Model(df, horizon, input_size, train_steps, val_check_steps, early_stop_p
# VanillaTransformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ), //报错了 # VanillaTransformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ), //报错了
# Autoformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ), //报错了 # Autoformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ), //报错了
NBEATS(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ), # NBEATS(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ),
# NBEATSx (h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',activation='ReLU', ), //报错 # NBEATSx (h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',activation='ReLU', ), //报错
# HINT(h=horizon), # HINT(h=horizon),