原油预警内容上传到市场信息平台

config_jingbo.py

@@ -89,8 +89,7 @@ data = {
 ClassifyId = 1214
 
 
-
+# 变量定义--线上环境
-################################################################################################################  变量定义--线上环境
 # server_host = '10.200.32.39'
 # login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
 # upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
@@ -111,7 +110,6 @@ ClassifyId = 1214
 # }
 
 
-
 # upload_data = {
 #     "funcModule":'研究报告信息',
 #     "funcOperation":'上传原油价格预测报告',
@@ -119,12 +117,12 @@ ClassifyId = 1214
 #         "groupNo":'', # 用户组id
 #         "ownerAccount":'27663', #报告所属用户账号  27663 - 刘小朋
 #         "reportType":'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
-#         "fileName": '', #文件名称 
+#         "fileName": '', #文件名称
 #         "fileBase64": '' ,#文件内容base64
 #         "categoryNo":'yyjgycbg', # 研究报告分类编码
 #         "smartBusinessClassCode":'YCJGYCBG', #分析报告分类编码
-#         "reportEmployeeCode":"E40482" ,# 报告人  E40482  - 管理员  0000027663 - 刘小朋  
+#         "reportEmployeeCode":"E40482" ,# 报告人  E40482  - 管理员  0000027663 - 刘小朋
-#         "reportDeptCode" :"002000621000", # 报告部门 - 002000621000  SH期货研究部  
+#         "reportDeptCode" :"002000621000", # 报告部门 - 002000621000  SH期货研究部
 #         "productGroupCode":"RAW_MATERIAL"  # 商品分类
 #   }
 # }
@@ -151,7 +149,6 @@ ClassifyId = 1214
 # }
 
 
-
 # push_data_value_list_data = {
 #     "funcModule": "数据表信息列表",
 #     "funcOperation": "新增",
@@ -189,13 +186,12 @@ ClassifyId = 1214
 # # 生产环境数据库
 # host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
 # port = 3306
-# dbusername ='jingbo'  
+# dbusername ='jingbo'
 # password = 'shihua@123'
 # dbname = 'jingbo'
 # table_name = 'v_tbl_crude_oil_warning'
 
 
-
 # # 变量定义--测试环境
 server_host = '192.168.100.53'  # 内网
 # server_host = '183.242.74.28'  # 外网
@@ -208,6 +204,8 @@ upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/cru
 query_data_list_item_nos_url = f"http://{server_host}:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
 # 上传数据项值
 push_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/dw/dataValue/pushDataValueList"
+# 上传停更数据到市场信息平台
+push_waring_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/crudeSaveOrupdate"
 
 login_data = {
     "data": {
@@ -281,6 +279,26 @@ push_data_value_list_data = {
          }
     ]
 }
+
+push_waring_data_value_list_data = {
+    "data": {
+        "crudeOilWarningDtoList": [
+            {
+                "lastUpdateDate": "20240501",
+                "updateSuspensionCycle": 1,
+                "dataSource": "8",
+                "frequency": "1",
+                "indicatorName": "美元指数",
+                "indicatorId": "myzs001",
+                "warningDate": "2024-05-13"
+            }
+        ],
+        "dataSource": "8"
+    },
+    "funcModule": "商品数据同步",
+    "funcOperation": "同步"
+}
+
 # 八大维度数据项编码
 bdwd_items = {
     'ciri': 'yyycbdwdcr',

lib/dataread.py

@@ -40,15 +40,6 @@ pdfmetrics.registerFont(TTFont('SimSun', 'SimSun.ttf'))
 plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
 plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
 
-# from config_jingbo_pro import *
-# from config_jingbo import *
-# from config_jingbo_yuedu import *
-# from config_yongan import *
-# from config_juxiting import *
-# from config_juxiting_zhoudu import *
-# from config_juxiting_pro import *
-
-# from config_jingbo import logger
 
 global_config = {
     # 核心配置项
@@ -93,6 +84,9 @@ global_config = {
     # 上传数据项数据
     'push_data_value_list_url': None,
     'push_data_value_list_data': None,
+    # 上传停更预警数据
+    'push_waring_data_value_list_url': None,
+    'push_waring_data_value_list_data': None,
 
     # 字段映射
     'offsite_col': None,               # 站点字段
@@ -1222,6 +1216,14 @@ class Config:
     def push_data_value_list_data(
         self): return global_config['push_data_value_list_data']
 
+    @property
+    def push_waring_data_value_list_url(
+        self): return global_config['push_waring_data_value_list_url']
+
+    @property
+    def push_waring_data_value_list_data(
+        self): return global_config['push_waring_data_value_list_data']
+
     @property
     def bdwd_items(self): return global_config['bdwd_items']
 
@@ -2203,6 +2205,43 @@ def push_market_data(data):
     return json_data
 
 
+def push_waring_market_data(data):
+    '''
+    上传停更预警数据到市场信息平台
+    data: 预测价格数据,示例：
+    {
+    "data": {
+        "crudeOilWarningDtoList": [
+            {
+                "lastUpdateDate": "20240510",
+                "updateSuspensionCycle": 1,
+                "dataSource": "8",
+                "frequency": "1",
+                "indicatorName": "美元指数",
+                "indicatorId": "myzs001",
+                "warningDate": "2024-05-12"
+            }
+        ],
+        "dataSource": "8"
+    },
+    "funcModule": "商品数据同步",
+    "funcOperation": "同步"
+}
+    '''
+    # 获取token
+    token = get_head_auth_report()
+    # 定义请求参数
+    global_config['push_waring_data_value_list_data']['data']["crudeOilWarningDtoList"] = data
+    # 发送请求
+    headers = {"Authorization": token}
+    config.logger.info('上传数据中...')
+    items_res = requests.post(url=config.push_waring_data_value_list_url, headers=headers,
+                              json=config.push_waring_data_value_list_data, timeout=(3, 35))
+    json_data = json.loads(items_res.text)
+    config.logger.info(f"上传结果:{json_data}")
+    return json_data
+
+
 def get_high_low_data(df):
     # 读取excel 从第五行开始
     df1 = pd.read_excel(os.path.join(config.dataset, '数据项下载.xls'), header=5, names=[

main_yuanyou.py

@@ -63,6 +63,10 @@ global_config.update({
     'push_data_value_list_url': push_data_value_list_url,
     'push_data_value_list_data': push_data_value_list_data,
 
+    # 上传数据项
+    'push_waring_data_value_list_url': push_waring_data_value_list_url,
+    'push_waring_data_value_list_data': push_waring_data_value_list_data,
+
     # eta 配置
     'APPID': APPID,
     'SECRET': SECRET,
@@ -83,7 +87,6 @@ global_config.update({
 })
 
 
-
 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
@@ -120,13 +123,13 @@ def push_market_value():
     predictdata = [
         {
             "dataItemNo": global_config['bdwd_items']['ciri'],
-            "dataDate": global_config['end_time'].replace('-',''),
+            "dataDate": global_config['end_time'].replace('-', ''),
             "dataStatus": "add",
             "dataValue": first_mean
         },
         {
             "dataItemNo": global_config['bdwd_items']['benzhou'],
-            "dataDate": global_config['end_time'].replace('-',''),
+            "dataDate": global_config['end_time'].replace('-', ''),
             "dataStatus": "add",
             "dataValue": last_mean
         }
@@ -141,8 +144,6 @@ def push_market_value():
         config.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。
@@ -197,7 +198,7 @@ def predict_main():
     # 获取数据
     if is_eta:
         logger.info('从eta获取数据...')
-        
+
         df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(
             data_set=data_set, dataset=dataset)  # 原始数据，未处理
 
@@ -338,94 +339,122 @@ def predict_main():
         sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
             '%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
 
-    try:
+    # try:
-        if is_weekday:
+    #     # if is_weekday:
-        # if True:
+    #     if True:
-            logger.info('今天是周一，发送特征预警')
+    #         logger.info('今天是周一，发送特征预警')
-            # 上传预警信息到数据库
+    #         # 上传预警信息到数据库
-            warning_data_df = df_zhibiaoliebiao.copy()
+    #         warning_data_df = df_zhibiaoliebiao.copy()
-            warning_data_df = warning_data_df[warning_data_df['停更周期'] > 3][[
+    #         warning_data_df = warning_data_df[warning_data_df['停更周期'] > 3][[
-                '指标名称', '指标id', '频度', '更新周期', '指标来源', '最后更新时间', '停更周期']]
+    #             '指标名称', '指标id', '频度', '更新周期', '指标来源', '最后更新时间', '停更周期']]
-            # 重命名列名
+    #         # 重命名列名
-            warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY',
+    #         warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY',
-                                                     '更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
+    #                                                  '更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
-            from sqlalchemy import create_engine
+    #         from sqlalchemy import create_engine
-            import urllib
+    #         import urllib
-            global password
+    #         global password
-            if '@' in password:
+    #         if '@' in password:
-                password = urllib.parse.quote_plus(password)
+    #             password = urllib.parse.quote_plus(password)
 
-            engine = create_engine(
+    #         engine = create_engine(
-                f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
+    #             f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
-            warning_data_df['WARNING_DATE'] = datetime.date.today().strftime(
+    #         warning_data_df['WARNING_DATE'] = datetime.date.today().strftime(
-                "%Y-%m-%d %H:%M:%S")
+    #             "%Y-%m-%d %H:%M:%S")
-            warning_data_df['TENANT_CODE'] = 'T0004'
+    #         warning_data_df['TENANT_CODE'] = 'T0004'
-            # 插入数据之前查询表数据然后新增id列
+    #         # 插入数据之前查询表数据然后新增id列
-            existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
+    #         existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
-            if not existing_data.empty:
+    #         if not existing_data.empty:
-                max_id = existing_data['ID'].astype(int).max()
+    #             max_id = existing_data['ID'].astype(int).max()
-                warning_data_df['ID'] = range(
+    #             warning_data_df['ID'] = range(
-                    max_id + 1, max_id + 1 + len(warning_data_df))
+    #                 max_id + 1, max_id + 1 + len(warning_data_df))
-            else:
+    #         else:
-                warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
+    #             warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
-            warning_data_df.to_sql(
+    #         warning_data_df.to_sql(
-                table_name,  con=engine, if_exists='append', index=False)
+    #             table_name,  con=engine, if_exists='append', index=False)
-            if is_update_warning_data:
+    #         if is_update_warning_data:
-                upload_warning_info(len(warning_data_df))
+    #             upload_warning_info(len(warning_data_df))
-    except:
+    # except:
-        logger.info('上传预警信息到数据库失败')
+    #     logger.info('上传预警信息到数据库失败')
 
-    if is_corr:
+    # try:
-        df = corr_feature(df=df)
+    # if is_weekday:
+    if True:
+        logger.info('发送特征预警')
+        # 获取取消订阅的指标ID
 
-    df1 = df.copy()  # 备份一下，后面特征筛选完之后加入ds y 列用
+        # 上传预警信息到数据库
-    logger.info(f"开始训练模型...")
+        warning_data_df = df_zhibiaoliebiao.copy()
-    row, col = df.shape
+        warning_data_df = warning_data_df[warning_data_df['停更周期'] > 3][[
+            '指标名称', '指标id', '频度', '更新周期', '指标来源', '最后更新时间', '停更周期']]
+        # 重命名列名
+        warning_data_df = warning_data_df.rename(columns={'指标名称': 'indicatorName', '指标id': 'indicatorId', '频度': 'frequency',
+                                                          '更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'updateSuspensionCycle'})
 
-    now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
+        warning_data_df['warningDate'] = datetime.date.today().strftime(
-    ex_Model(df,
+            "%Y-%m-%d %H:%M:%S")
-             horizon=global_config['horizon'],
+        warning_data_df['dataSource'] = 8
-             input_size=global_config['input_size'],
+        data = warning_data_df.to_json(orient='records', force_ascii=False)
-             train_steps=global_config['train_steps'],
+        data = data.replace('日度', '1')
-             val_check_steps=global_config['val_check_steps'],
+        data = data.replace('周度', '2')
-             early_stop_patience_steps=global_config['early_stop_patience_steps'],
+        data = data.replace('月度', '3')
-             is_debug=global_config['is_debug'],
+        data = json.loads(data)
-             dataset=global_config['dataset'],
+        push_waring_market_data(data)
-             is_train=global_config['is_train'],
+        # if is_update_warning_data:
-             is_fivemodels=global_config['is_fivemodels'],
+        #     upload_warning_info(len(warning_data_df))
-             val_size=global_config['val_size'],
+    # except:
-             test_size=global_config['test_size'],
+    #     logger.info('上传预警信息到数据库失败')
-             settings=global_config['settings'],
-             now=now,
-             etadata=etadata,
-             modelsindex=global_config['modelsindex'],
-             data=data,
-             is_eta=global_config['is_eta'],
-             end_time=global_config['end_time'],
-             )
 
-    logger.info('模型训练完成')
+    # if is_corr:
+    #     df = corr_feature(df=df)
 
-    logger.info('训练数据绘图ing')
+    # df1 = df.copy()  # 备份一下，后面特征筛选完之后加入ds y 列用
-    model_results3 = model_losss(sqlitedb, end_time=end_time)
+    # logger.info(f"开始训练模型...")
-    logger.info('训练数据绘图end')
+    # row, col = df.shape
 
-    # 模型报告
+    # now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
-    logger.info('制作报告ing')
+    # ex_Model(df,
-    title = f'{settings}--{end_time}-预测报告'  # 报告标题
+    #          horizon=global_config['horizon'],
-    reportname = f'Brent原油大模型日度预测--{end_time}.pdf'  # 报告文件名
+    #          input_size=global_config['input_size'],
-    reportname = reportname.replace(':', '-')  # 替换冒号
+    #          train_steps=global_config['train_steps'],
-    brent_export_pdf(dataset=dataset, 
+    #          val_check_steps=global_config['val_check_steps'],
-                        num_models=5 if is_fivemodels else 22, time=end_time,
+    #          early_stop_patience_steps=global_config['early_stop_patience_steps'],
-                        reportname=reportname, 
+    #          is_debug=global_config['is_debug'],
-                        inputsize = global_config['horizon'],
+    #          dataset=global_config['dataset'],
-                        sqlitedb=sqlitedb
+    #          is_train=global_config['is_train'],
-                     ),
+    #          is_fivemodels=global_config['is_fivemodels'],
+    #          val_size=global_config['val_size'],
+    #          test_size=global_config['test_size'],
+    #          settings=global_config['settings'],
+    #          now=now,
+    #          etadata=etadata,
+    #          modelsindex=global_config['modelsindex'],
+    #          data=data,
+    #          is_eta=global_config['is_eta'],
+    #          end_time=global_config['end_time'],
+    #          )
 
-    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()
+    # logger.info('训练数据绘图ing')
+    # model_results3 = model_losss(sqlitedb, end_time=end_time)
+    # logger.info('训练数据绘图end')
+
+    # # 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 = reportname.replace(':', '-')  # 替换冒号
+    # brent_export_pdf(dataset=dataset,
+    #                  num_models=5 if is_fivemodels else 22, time=end_time,
+    #                  reportname=reportname,
+    #                  inputsize=global_config['horizon'],
+    #                  sqlitedb=sqlitedb
+    #                  ),
+
+    # 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)

report/xgboost_report.md

@@ -1,40 +0,0 @@
-# XGBoost 价格预测分析报告 <span style="color:gray; font-size:0.8em">2025-05-08</span>
-
-## 一、模型实现
-
-### 1. 特征工程
-
-- 使用 5 期历史滞后特征
-- 预测未来 10 个时间步长
-- 数据集分割比例：80% 训练集 / 20% 测试集
-
-### 2. 模型配置
-
-|              | learning_rate | max_depth | n_estimators |
-| :----------- | ------------: | --------: | -----------: |
-| 最佳参数组合 |             1 |         4 |          100 |
-
-## 二、性能评估
-
-### 多步预测误差分析
-
-| 预测步长 | 均方根误差(RMSE) |  R² 分数 |
-| -------: | ---------------: | -------: |
-|        1 |          2.30044 | 0.828193 |
-|        2 |          2.74815 | 0.756595 |
-|        3 |          3.16346 | 0.679378 |
-|        4 |          3.62202 |  0.58126 |
-|        5 |          3.77657 | 0.546517 |
-
-## 三、预测结果
-
-### 未来 5 日价格预测
-
-![预测结果](价格预测.png)
-| | 预测值 |
-|:--------------------|---------:|
-| 2025-03-14 00:00:00 | 68.8125 |
-| 2025-03-15 00:00:00 | 68.6643 |
-| 2025-03-16 00:00:00 | 69.283 |
-| 2025-03-17 00:00:00 | 71.7288 |
-| 2025-03-18 00:00:00 | 68.8356 |