原油周度调试上传预测值到市场信息平台

config_jingbo.py

@@ -39,7 +39,6 @@ edbnamelist = [
 ]
 
 
-
 # eta自有数据指标编码
 modelsindex = {
     'NHITS': 'SELF0000001',
@@ -90,15 +89,18 @@ data = {
 ClassifyId = 1214
 
 
-
+# 变量定义--测试环境
-###############################################################################################################  变量定义--测试环境
 server_host = '192.168.100.53'
 
 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"  # 上传报告
+# 上传报告
-upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"  # 停更预警
+upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
-query_data_list_item_nos_url = f"http://{server_host}:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos" # 查询数据项编码
+# 停更预警
-push_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/dw/dataValue/pushDataValueList" # 上传数据项值
+upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
+# 查询数据项编码
+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"
 
 login_data = {
     "data": {
@@ -176,6 +178,12 @@ push_data_value_list_data = {
 bdwd_items = {
     'ciri': 'yyycbdwdcr',
     'benzhou': 'yyycbdwdbz',
+    'cizhou': 'yyycbdwdcz',
+    'gezhou': 'yyycbdwdgz',
+    'ciyue': 'yyycbdwdcy',
+    'cieryue': 'yyycbdwdcey',
+    'cisanyue': 'yyycbdwdcsy',
+    'cisiyue': 'yyycbdwdcsiy',
 }
 
 
@@ -188,7 +196,7 @@ dbname = 'jingbo_test'
 table_name = 'v_tbl_crude_oil_warning'
 
 
-### 开关
+# 开关
 is_train = False  # 是否训练
 is_debug = True  # 是否调试
 is_eta = True  # 是否使用eta接口
@@ -205,9 +213,9 @@ is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不
 is_del_tow_month = True  # 是否删除两个月不更新的特征
 
 
-
 # 连接到数据库
-db_mysql = MySQLDB(host=host, user=dbusername, password=password, database=dbname)
+db_mysql = MySQLDB(host=host, user=dbusername,
+                   password=password, database=dbname)
 db_mysql.connect()
 print("数据库连接成功", host, dbname, dbusername)
 
@@ -222,7 +230,7 @@ add_kdj = False # 是否添加kdj指标
 if add_kdj and is_edbnamelist:
     edbnamelist = edbnamelist+['K', 'D', 'J']
 
-### 模型参数  
+# 模型参数
 y = 'Brent连1合约价格'  # 原油指标数据的目标变量  Brent连1合约价格   Brent活跃合约
 # y = 'Brent连1合约价格' # 原油指标数据的目标变量  Brent连1合约价格   Brent活跃合约
 horizon = 5  # 预测的步长
@@ -234,16 +242,16 @@ early_stop_patience_steps = 5 # 早停的耐心步数
 test_size = 100  # 测试集大小，定义100，后面使用的时候重新赋值
 val_size = test_size  # 验证集大小,同测试集大小
 
-### 特征筛选用到的参数
+# 特征筛选用到的参数
 k = 100  # 特征筛选数量，如果是0或者值比特征数量大，代表全部特征
 corr_threshold = 0.6  # 相关性大于0.6的特征
 rote = 0.06  # 绘图上下界阈值
 
-### 计算准确率
+# 计算准确率
 weight_dict = [0.4, 0.15, 0.1, 0.1, 0.25]  # 权重
 
 
-### 文件
+# 文件
 data_set = '原油指标数据.xlsx'  # 数据集文件
 dataset = 'yuanyoudataset'  # 数据集文件夹
 
@@ -260,7 +268,7 @@ reportname = f'Brent原油大模型预测--{end_time}.pdf' # 报告文件名
 reportname = reportname.replace(':', '-')  # 替换冒号
 if end_time == '':
     end_time = now
-### 邮件配置
+# 邮件配置
 username = '1321340118@qq.com'
 passwd = 'wgczgyhtyyyyjghi'
 # recv=['liurui_test@163.com','52585119@qq.com']
@@ -273,7 +281,7 @@ file=os.path.join(dataset,'reportname')
 ssl = True
 
 
-### 日志配置
+# 日志配置
 
 # 创建日志目录（如果不存在）
 log_dir = 'logs'
@@ -285,8 +293,10 @@ logger = logging.getLogger('my_logger')
 logger.setLevel(logging.INFO)
 
 # 配置文件处理器，将日志记录到文件
-file_handler = logging.handlers.RotatingFileHandler(os.path.join(log_dir, 'pricepredict.log'), maxBytes=1024 * 1024, backupCount=5)
+file_handler = logging.handlers.RotatingFileHandler(os.path.join(
-file_handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
+    log_dir, 'pricepredict.log'), maxBytes=1024 * 1024, backupCount=5)
+file_handler.setFormatter(logging.Formatter(
+    '%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
 
 # 配置控制台处理器，将日志打印到控制台
 console_handler = logging.StreamHandler()
@@ -297,4 +307,3 @@ logger.addHandler(file_handler)
 logger.addHandler(console_handler)
 
 # logger.info('当前配置：'+settings)
-

config_jingbo_zhoudu.py

@@ -96,6 +96,8 @@ 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"
+# 上传数据项值
+push_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/dw/dataValue/pushDataValueList"
 
 login_data = {
     "data": {
@@ -146,6 +148,39 @@ 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'
@@ -168,6 +203,7 @@ 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_zhoudu.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,
@@ -65,12 +71,67 @@ global_config.update({
     'edbdatapushurl': edbdatapushurl,
     'edbdeleteurl': edbdeleteurl,
     'edbbusinessurl': edbbusinessurl,
+    'ClassifyId':   ClassifyId,
     'classifylisturl': classifylisturl,
 
     # 数据库配置
     'sqlitedb': sqlitedb,
 })
 
+
+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_date = predictdata_df['ds'].iloc[0].replace('-', '')
+    last_date = predictdata_df['ds'].iloc[-1].replace('-', '')
+    first_mean = predictdata_df['top_models_mean'].iloc[0]
+    last_mean = predictdata_df['top_models_mean'].iloc[-1]
+
+    predictdata = [
+        {
+            "dataItemNo": global_config['bdwd_items']['cizhou'],
+            "dataDate": first_date,
+            "dataStatus": "add",
+            "dataValue": first_mean
+        },
+        {
+            "dataItemNo": global_config['bdwd_items']['gezhou'],
+            "dataDate": last_date,
+            "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。