聚烯烃配置八大维度

config_juxiting.py

@@ -190,7 +190,17 @@ query_data_list_item_nos_data = {
     }
 }
 
-
+# 八大维度数据项编码
+bdwd_items = {
+    'ciri': 'jxtppbdwdcr',
+    'benzhou': 'jxtppbdwdbz',
+    'cizhou': 'jxtppbdwdcz',
+    'gezhou': 'jxtppbdwdgz',
+    'ciyue': 'jxtppbdwdcy',
+    'cieryue': 'jxtppbdwdcey',
+    'cisanyue': 'jxtppbdwdcsy',
+    'cisiyue': 'jxtppbdwdcsiy',
+}
 # 北京环境数据库
 host = '192.168.101.27'
 port = 3306
@@ -201,9 +211,9 @@ table_name = 'v_tbl_crude_oil_warning'
 
 
 # 开关
-is_train = True  # 是否训练
+is_train = False  # 是否训练
 is_debug = False  # 是否调试
-is_eta = True  # 是否使用eta接口
+is_eta = False  # 是否使用eta接口
 is_market = False  # 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_fivemodels = False  # 是否使用之前保存的最佳的5个模型

lib/dataread.py

@@ -2259,3 +2259,59 @@ def get_baichuan_data(baichuanidnamedict):
     df1.drop(columns=['DATA_DATE'], inplace=True)
 
     return df1
+
+
+
+def push_market_value():
+    config.logger.info('发送预测结果到市场信息平台')
+    # 读取预测数据和模型评估数据
+    predict_file_path = os.path.join(config.dataset, 'predict.csv')
+    model_eval_file_path = os.path.join(config.dataset, 'model_evaluation.csv')
+    try:
+        predictdata_df = pd.read_csv(predict_file_path)
+        top_models_df = pd.read_csv(model_eval_file_path)
+    except FileNotFoundError as e:
+        config.logger.error(f"文件未找到: {e}")
+        return
+
+    predictdata = predictdata_df.copy()
+
+    # 取模型前十
+    top_models = top_models_df['模型(Model)'].head(10).tolist()
+
+    # 计算前十模型的均值
+    predictdata_df['top_models_mean'] = predictdata_df[top_models].mean(axis=1)
+
+    # 打印日期和前十模型均值
+    print(predictdata_df[['ds', 'top_models_mean']])
+
+    # 准备要推送的数据
+    first_mean = predictdata_df['top_models_mean'].iloc[0]
+    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 = [
+        {
+            "dataItemNo": global_config['bdwd_items']['ciri'],
+            "dataDate": global_config['end_time'].replace('-',''),
+            "dataStatus": "add",
+            "dataValue": first_mean
+        },
+        {
+            "dataItemNo": global_config['bdwd_items']['benzhou'],
+            "dataDate": global_config['end_time'].replace('-',''),
+            "dataStatus": "add",
+            "dataValue": last_mean
+        }
+    ]
+
+    print(predictdata)
+
+    # 推送数据到市场信息平台
+    try:
+        push_market_data(predictdata)
+    except Exception as e:
+        config.logger.error(f"推送数据失败: {e}")
+

main_juxiting.py

@@ -347,7 +347,7 @@ def predict_main():
     # # 模型报告
     logger.info('制作报告ing')
     title = f'{settings}--{end_time}-预测报告'  # 报告标题
-    reportname = f'Brent原油大模型日度预测--{end_time}.pdf'  # 报告文件名
+    reportname = f'聚烯烃PP大模型日度预测--{end_time}.pdf'  # 报告文件名
     reportname = reportname.replace(':', '-')  # 替换冒号
     pp_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
                   reportname=reportname, sqlitedb=sqlitedb),
@@ -355,6 +355,8 @@ def predict_main():
     logger.info('制作报告end')
     logger.info('模型训练完成')
 
+    push_market_value()
+
     # # LSTM 单变量模型
     # ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
 
@@ -380,12 +382,12 @@ def predict_main():
 if __name__ == '__main__':
     # global end_time
     # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
-    # for i_time in pd.date_range('2022-1-1', '2025-3-26', freq='M'):
+    for i_time in pd.date_range('2025-2-1', '2025-3-18', freq='M'):
-    #     try:
+        try:
-    #         global_config['end_time'] = i_time.strftime('%Y-%m-%d')
+            global_config['end_time'] = i_time.strftime('%Y-%m-%d')
-    #         predict_main()
+            predict_main()
-    #     except Exception as e:
+        except Exception as e:
-    #         logger.info(f'预测失败：{e}')
+            logger.info(f'预测失败：{e}')
-    #         continue
+            continue
 
-    predict_main()
+    # predict_main()

main_juxiting_yuedu.py

@@ -346,7 +346,7 @@ def predict_main():
     # # 模型报告
     logger.info('制作报告ing')
     title = f'{settings}--{end_time}-预测报告'  # 报告标题
-    reportname = f'Brent原油大模型月度预测--{end_time}.pdf'  # 报告文件名
+    reportname = f'聚烯烃PP大模型月度预测--{end_time}.pdf'  # 报告文件名
     reportname = reportname.replace(':', '-')  # 替换冒号
     pp_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
                   reportname=reportname, sqlitedb=sqlitedb),

main_juxiting_zhoudu.py

@@ -346,7 +346,7 @@ def predict_main():
     # # 模型报告
     logger.info('制作报告ing')
     title = f'{settings}--{end_time}-预测报告'  # 报告标题
-    reportname = f'Brent原油大模型周度预测--{end_time}.pdf'  # 报告文件名
+    reportname = f'聚烯烃PP大模型周度预测--{end_time}.pdf'  # 报告文件名
     reportname = reportname.replace(':', '-')  # 替换冒号
     pp_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
                   reportname=reportname, sqlitedb=sqlitedb),

main_yuanyou.py

@@ -80,60 +80,6 @@ global_config.update({
 })
 
 
-def push_market_value():
-    logger.info('发送预测结果到市场信息平台')
-    # 读取预测数据和模型评估数据
-    predict_file_path = os.path.join(config.dataset, 'predict.csv')
-    model_eval_file_path = os.path.join(config.dataset, 'model_evaluation.csv')
-    try:
-        predictdata_df = pd.read_csv(predict_file_path)
-        top_models_df = pd.read_csv(model_eval_file_path)
-    except FileNotFoundError as e:
-        logger.error(f"文件未找到: {e}")
-        return
-
-    predictdata = predictdata_df.copy()
-
-    # 取模型前十
-    top_models = top_models_df['模型(Model)'].head(10).tolist()
-
-    # 计算前十模型的均值
-    predictdata_df['top_models_mean'] = predictdata_df[top_models].mean(axis=1)
-
-    # 打印日期和前十模型均值
-    print(predictdata_df[['ds', 'top_models_mean']])
-
-    # 准备要推送的数据
-    first_mean = predictdata_df['top_models_mean'].iloc[0]
-    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 = [
-        {
-            "dataItemNo": global_config['bdwd_items']['ciri'],
-            "dataDate": global_config['end_time'].replace('-',''),
-            "dataStatus": "add",
-            "dataValue": first_mean
-        },
-        {
-            "dataItemNo": global_config['bdwd_items']['benzhou'],
-            "dataDate": global_config['end_time'].replace('-',''),
-            "dataStatus": "add",
-            "dataValue": last_mean
-        }
-    ]
-
-    print(predictdata)
-
-    # 推送数据到市场信息平台
-    try:
-        push_market_data(predictdata)
-    except Exception as e:
-        logger.error(f"推送数据失败: {e}")
-
-
 def predict_main():
     """
     主预测函数，用于从 ETA 获取数据、处理数据、训练模型并进行预测。

models/nerulforcastmodels.py

@@ -455,10 +455,10 @@ def ex_Model_Juxiting(df, horizon, input_size, train_steps, val_check_steps, ear
     df_predict.to_csv(os.path.join(config.dataset, "predict.csv"), index=False)
 
     # 将预测结果保存到数据库
-    save_to_database(sqlitedb, df_predict, 'predict', end_time)
+    save_to_database(config.sqlitedb, df_predict, 'predict', end_time)
 
     # 把预测值上传到eta
-    if is_update_eta:
+    if config.is_update_eta:
         df_predict['ds'] = pd.to_datetime(df_predict['ds'])
         dates = df_predict['ds'].dt.strftime('%Y-%m-%d')