月度配置更新预测结果上传到市场信息平台

config_jingbo.py

@@ -90,8 +90,8 @@ ClassifyId = 1214
 
 
 # 变量定义--测试环境
-server_host = '192.168.100.53'
+# server_host = '192.168.100.53'  # 内网
-
+server_host = '183.242.74.28 '  # 外网
 login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
 # 上传报告
 upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"

config_jingbo_yuedu.py

@@ -90,15 +90,14 @@ ClassifyId = 1214
 
 
 # 变量定义--测试环境
-server_host = '192.168.100.53'
+# server_host = '192.168.100.53'  # 内网
-
+server_host = '183.242.74.28 '  # 外网
 login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
 upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
 upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
 query_data_list_item_nos_url = f"http://{server_host}:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
-# 数据项推送接口
+# 上传数据项值
-push_data_balue_list = f"http://{server_host}:8080/jingbo-dev/api/dw/dataValue/pushDataValueList"
+push_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/dw/dataValue/pushDataValueList"
-
 
 login_data = {
     "data": {
@@ -113,6 +112,7 @@ login_data = {
 }
 
 upload_data = {
+    "groupNo": '',  # 用户组id
     "funcModule": '研究报告信息',
     "funcOperation": '上传原油价格预测报告',
     "data": {
@@ -130,6 +130,7 @@ upload_data = {
 
 
 warning_data = {
+    "groupNo": '',  # 用户组id
     "funcModule": '原油特征停更预警',
     "funcOperation": '原油特征停更预警',
     "data": {
@@ -149,6 +150,39 @@ query_data_list_item_nos_data = {
     }
 }
 
+push_data_value_list_data = {
+    "funcModule": "数据表信息列表",
+    "funcOperation": "新增",
+    "data": [
+        {"dataItemNo": "91230600716676129",
+         "dataDate": "20230113",
+         "dataStatus": "add",
+         "dataValue": 100.11
+         },
+        {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
+         "dataDate": "20230113",
+         "dataStatus": "add",
+         "dataValue": 100.55
+         },
+        {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
+         "dataDate": "20230113",
+         "dataStatus": "add",
+         "dataValue": 100.55
+         }
+    ]
+}
+# 八大维度数据项编码
+bdwd_items = {
+    'ciri': 'yyycbdwdcr',
+    'benzhou': 'yyycbdwdbz',
+    'cizhou': 'yyycbdwdcz',
+    'gezhou': 'yyycbdwdgz',
+    'ciyue': 'yyycbdwdcy',
+    'cieryue': 'yyycbdwdcey',
+    'cisanyue': 'yyycbdwdcsy',
+    'cisiyue': 'yyycbdwdcsiy',
+}
+
 
 # 北京环境数据库
 host = '192.168.101.27'
@@ -171,6 +205,7 @@ 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_report = False  # 是否上传报告
 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_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_tow_month = True  # 是否删除两个月不更新的特征
 

main_yuanyou_yuedu.py

@@ -29,6 +29,7 @@ global_config.update({
     'test_size': test_size,
     'modelsindex': modelsindex,
     'rote': rote,
+    'bdwd_items': bdwd_items,
 
     # 特征工程开关
     'is_del_corr': is_del_corr,
@@ -36,6 +37,7 @@ global_config.update({
     'is_eta': is_eta,
     'is_update_eta': is_update_eta,
     'is_fivemodels': is_fivemodels,
+    'is_update_predict_value': is_update_predict_value,
     'early_stop_patience_steps': early_stop_patience_steps,
 
     # 时间参数
@@ -54,6 +56,10 @@ global_config.update({
     'query_data_list_item_nos_url': query_data_list_item_nos_url,
     'query_data_list_item_nos_data': query_data_list_item_nos_data,
 
+    # 上传数据项
+    'push_data_value_list_url': push_data_value_list_url,
+    'push_data_value_list_data': push_data_value_list_data,
+
     # eta 配置
     'APPID': APPID,
     'SECRET': SECRET,
@@ -73,6 +79,75 @@ 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
+    ciyue_date = predictdata_df['ds'].iloc[0].replace('-', '')
+    cieryue_date = predictdata_df['ds'].iloc[1].replace('-', '')
+    cisanyue_date = predictdata_df['ds'].iloc[2].replace('-', '')
+    cisiyue_date = predictdata_df['ds'].iloc[3].replace('-', '')
+    ciyue_mean = predictdata_df['top_models_mean'].iloc[0]
+    cieryue_mean = predictdata_df['top_models_mean'].iloc[1]
+    cisanyue_mean = predictdata_df['top_models_mean'].iloc[2]
+    cisieryue_mean = predictdata_df['top_models_mean'].iloc[3]
+
+    predictdata = [
+        {
+            "dataItemNo": global_config['bdwd_items']['ciyue'],
+            "dataDate": ciyue_date,
+            "dataStatus": "add",
+            "dataValue": ciyue_mean
+        },
+        {
+            "dataItemNo": global_config['bdwd_items']['cieryue'],
+            "dataDate": cieryue_date,
+            "dataStatus": "add",
+            "dataValue": cieryue_mean
+        },
+        {
+            "dataItemNo": global_config['bdwd_items']['cisanyue'],
+            "dataDate": cisanyue_date,
+            "dataStatus": "add",
+            "dataValue": cisanyue_mean
+        },
+        {
+            "dataItemNo": global_config['bdwd_items']['cisiyue'],
+            "dataDate": cisiyue_date,
+            "dataStatus": "add",
+            "dataValue": cisieryue_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。
@@ -113,28 +188,19 @@ def predict_main():
         None
     """
     end_time = global_config['end_time']
-    signature = BinanceAPI(APPID, SECRET)
-    etadata = EtaReader(signature=signature,
-                        classifylisturl=classifylisturl,
-                        classifyidlisturl=classifyidlisturl,
-                        edbcodedataurl=edbcodedataurl,
-                        edbcodelist=edbcodelist,
-                        edbdatapushurl=edbdatapushurl,
-                        edbdeleteurl=edbdeleteurl,
-                        edbbusinessurl=edbbusinessurl
-                        )
     # 获取数据
     if is_eta:
         logger.info('从eta获取数据...')
         signature = BinanceAPI(APPID, SECRET)
         etadata = EtaReader(signature=signature,
-                            classifylisturl=classifylisturl,
+                            classifylisturl=global_config['classifylisturl'],
-                            classifyidlisturl=classifyidlisturl,
+                            classifyidlisturl=global_config['classifyidlisturl'],
-                            edbcodedataurl=edbcodedataurl,
+                            edbcodedataurl=global_config['edbcodedataurl'],
-                            edbcodelist=edbcodelist,
+                            edbcodelist=global_config['edbcodelist'],
-                            edbdatapushurl=edbdatapushurl,
+                            edbdatapushurl=global_config['edbdatapushurl'],
-                            edbdeleteurl=edbdeleteurl,
+                            edbdeleteurl=global_config['edbdeleteurl'],
-                            edbbusinessurl=edbbusinessurl,
+                            edbbusinessurl=global_config['edbbusinessurl'],
+                            classifyId=global_config['ClassifyId'],
                             )
         df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(
             data_set=data_set, dataset=dataset)  # 原始数据，未处理
@@ -348,16 +414,18 @@ def predict_main():
     model_results3 = model_losss(sqlitedb, end_time=end_time)
     logger.info('训练数据绘图end')
 
-    # # 模型报告
+    # 模型报告
-    # logger.info('制作报告ing')
+    logger.info('制作报告ing')
-    # title = f'{settings}--{end_time}-预测报告'  # 报告标题
+    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'Brent原油大模型月度预测--{end_time}.pdf'  # 报告文件名
-    # reportname = reportname.replace(':', '-')  # 替换冒号
+    reportname = reportname.replace(':', '-')  # 替换冒号
-    # brent_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
+    brent_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
-    #                  reportname=reportname, sqlitedb=sqlitedb),
+                     reportname=reportname, sqlitedb=sqlitedb),
 
-    # logger.info('制作报告end')
+    logger.info('制作报告end')
-    # logger.info('模型训练完成')
+    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)