diff --git a/config_jingbo.py b/config_jingbo.py index 9a36653..13a4349 100644 --- a/config_jingbo.py +++ b/config_jingbo.py @@ -92,63 +92,14 @@ ClassifyId = 1214 ### 报告上传配置 # 变量定义--线上环境 -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" -upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save" - -login_data = { - "data": { - "account": "api_dev", - "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", - "tenantHashCode": "8a4577dbd919675758d57999a1e891fe", - "terminal": "API" - }, - "funcModule": "API", - "funcOperation": "获取token" -} - - - -upload_data = { - "funcModule":'研究报告信息', - "funcOperation":'上传原油价格预测报告', - "data":{ - "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 - "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 - "reportDeptCode" :"002000621000", # 报告部门 - 002000621000 SH期货研究部 - "productGroupCode":"RAW_MATERIAL" # 商品分类 - } -} - -warning_data = { - "funcModule":'原油特征停更预警', - "funcOperation":'原油特征停更预警', - "data":{ - 'WARNING_TYPE_NAME':'特征数据停更预警', - 'WARNING_CONTENT':'', - 'WARNING_DATE':'' - } -} - - - -# # 变量定义--测试环境 -# login_pushreport_url = "http://192.168.100.53:8080/jingbo-dev/api/server/login" -# upload_url = "http://192.168.100.53:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave" -# # upload_url = "http://192.168.100.109:8080/jingbo/api/analysis/reportInfo/researchUploadReportSave" # zhaoqiwei -# upload_warning_url = "http://192.168.100.53:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save" - +# 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" +# upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save" # login_data = { # "data": { -# "account": "api_test", -# # "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456 -# "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456 +# "account": "api_dev", +# "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # "tenantHashCode": "8a4577dbd919675758d57999a1e891fe", # "terminal": "API" # }, @@ -156,18 +107,20 @@ warning_data = { # "funcOperation": "获取token" # } + + # upload_data = { # "funcModule":'研究报告信息', # "funcOperation":'上传原油价格预测报告', # "data":{ -# "ownerAccount":'arui', #报告所属用户账号 +# "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": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', #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":"E40116", # 报告人 -# "reportDeptCode" :"D0044" ,# 报告部门 +# "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 # "productGroupCode":"RAW_MATERIAL" # 商品分类 # } # } @@ -182,6 +135,53 @@ warning_data = { # } # } + + +# # 变量定义--测试环境 +login_pushreport_url = "http://192.168.100.53:8080/jingbo-dev/api/server/login" +upload_url = "http://192.168.100.53:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave" +# upload_url = "http://192.168.100.109:8080/jingbo/api/analysis/reportInfo/researchUploadReportSave" # zhaoqiwei +upload_warning_url = "http://192.168.100.53:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save" + + +login_data = { + "data": { + "account": "api_test", + # "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456 + "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456 + "tenantHashCode": "8a4577dbd919675758d57999a1e891fe", + "terminal": "API" + }, + "funcModule": "API", + "funcOperation": "获取token" +} + +upload_data = { + "funcModule":'研究报告信息', + "funcOperation":'上传原油价格预测报告', + "data":{ + "ownerAccount":'arui', #报告所属用户账号 + "reportType":'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST + "fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', #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":"E40116", # 报告人 + "reportDeptCode" :"D0044" ,# 报告部门 + "productGroupCode":"RAW_MATERIAL" # 商品分类 + } +} + +warning_data = { + "funcModule":'原油特征停更预警', + "funcOperation":'原油特征停更预警', + "data":{ + 'WARNING_TYPE_NAME':'特征数据停更预警', + 'WARNING_CONTENT':'', + 'WARNING_DATE':'' + } +} + ### 线上开关 # is_train = True # 是否训练 # is_debug = False # 是否调试 @@ -198,14 +198,14 @@ warning_data = { ### 开关 is_train = True # 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_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_edbcode = True # 特征使用edbcoding列表中的 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_warning_data = True # 是否上传预警数据 ### 北京环境数据库jbsh_test # url: jdbc:mysql://192.168.101.27:3306/jingbo_test?autoReconnect=true&useUnicode=true&characterEncoding=utf8&zeroDateTimeBehavior=convertToNull&transformedBitIsBoolean=true&useSSL=false&serverTimezone=GMT%2B8&nullCatalogMeansCurrent=true diff --git a/lib/dataread.py b/lib/dataread.py index 5733db9..b1ad11e 100644 --- a/lib/dataread.py +++ b/lib/dataread.py @@ -134,25 +134,38 @@ def upload_warning_data(warning_data): return None -def upload_warning_info(last_update_times_df,y_last_update_time): +# def upload_warning_info(last_update_times_df,y_last_update_time): +# logger.info(f'上传预警信息') +# try: +# warning_data_df = last_update_times_df[last_update_times_df['warning_date'] 0: +# content = '原油特征指标预警信息：\n\n' +# warning_data_df = warning_data_df.sort_values(by='停更周期',ascending=False) +# fixed_length = 20 +# warning_data_df['特征名称'] = warning_data_df['特征名称'].str.replace(" ", "") +# content = warning_data_df.to_string(index=False, col_space=fixed_length) + +# else: +# logger.info(f'没有需要上传的预警信息') +# content = '没有需要维护的特征指标' +# warning_date = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') +# warning_data['data']['WARNING_DATE'] = warning_date +# warning_data['data']['WARNING_CONTENT'] = content + +# upload_warning_data(warning_data) +# logger.info(f'上传预警信息成功') +# except Exception as e: +# logger.error(f'上传预警信息失败：{e}') + + +def upload_warning_info(df_count): logger.info(f'上传预警信息') try: - warning_data_df = last_update_times_df[last_update_times_df['warning_date'] 0: - content = '原油特征指标预警信息：\n\n' - warning_data_df = warning_data_df.sort_values(by='停更周期',ascending=False) - fixed_length = 20 - warning_data_df['特征名称'] = warning_data_df['特征名称'].str.replace(" ", "") - content = warning_data_df.to_string(index=False, col_space=fixed_length) - - else: - logger.info(f'没有需要上传的预警信息') - content = '没有需要维护的特征指标' - warning_date = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + warning_date = datetime.datetime.now().strftime('%Y-%m-%d') + content = f'{warning_date}有{df_count}个停更' warning_data['data']['WARNING_DATE'] = warning_date warning_data['data']['WARNING_CONTENT'] = content - upload_warning_data(warning_data) logger.info(f'上传预警信息成功') except Exception as e: @@ -571,13 +584,13 @@ def datachuli(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time='',y='y' # 获取start_year年到end_time的数据 df = df[df['ds'].dt.year >= start_year] df = df[df['ds'] <= end_time] - last_update_times_df,y_last_update_time = create_feature_last_update_time(df) - logger.info(f'删除预警的特征前数据量：{df.shape}') - columns_to_drop = last_update_times_df[last_update_times_df['warning_date'] < y_last_update_time ]['feature'].values.tolist() - df = df.drop(columns = columns_to_drop) - logger.info(f'删除预警的特征后数据量：{df.shape}') - if is_update_warning_data: - upload_warning_info(last_update_times_df,y_last_update_time) + # last_update_times_df,y_last_update_time = create_feature_last_update_time(df) + # logger.info(f'删除预警的特征前数据量：{df.shape}') + # columns_to_drop = last_update_times_df[last_update_times_df['warning_date'] < y_last_update_time ]['feature'].values.tolist() + # df = df.drop(columns = columns_to_drop) + # logger.info(f'删除预警的特征后数据量：{df.shape}') + # if is_update_warning_data: + # upload_warning_info(last_update_times_df,y_last_update_time) # 去掉近最后数据对应的日期在六月以前的列，删除近2月的数据是常熟的列 current_date = datetime.datetime.now() two_months_ago = current_date - timedelta(days=180) diff --git a/main_yuanyou.py b/main_yuanyou.py index 1daf154..ab32603 100644 --- a/main_yuanyou.py +++ b/main_yuanyou.py @@ -139,8 +139,9 @@ 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: - if is_weekday: - logger.info('今天是周一，更新预测模型') + # if is_weekday: + if True: + logger.info('今天是周一，发送特征预警') # 上传预警信息到数据库 warning_data_df = df_zhibiaoliebiao.copy() 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']] @@ -153,11 +154,13 @@ def predict_main(): # 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) if not existing_data.empty: - max_id = existing_data['id'].max() - warning_data_df['id'] = range(max_id + 1, max_id + 1 + len(warning_data_df)) + max_id = existing_data['ID'].astype(int).max() + warning_data_df['ID'] = range(max_id + 1, max_id + 1 + len(warning_data_df)) 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(table_name, con=engine, if_exists='append', index=False) + if is_update_warning_data: + upload_warning_info(len(warning_data_df)) except: logger.info('上传预警信息到数据库失败') @@ -170,25 +173,25 @@ def predict_main(): row, col = df.shape now = datetime.datetime.now().strftime('%Y%m%d%H%M%S') - ex_Model(df, - horizon=horizon, - input_size=input_size, - train_steps=train_steps, - val_check_steps=val_check_steps, - early_stop_patience_steps=early_stop_patience_steps, - is_debug=is_debug, - dataset=dataset, - is_train=is_train, - is_fivemodels=is_fivemodels, - val_size=val_size, - test_size=test_size, - settings=settings, - now=now, - etadata=etadata, - modelsindex=modelsindex, - data=data, - is_eta=is_eta, - ) + # ex_Model(df, + # horizon=horizon, + # input_size=input_size, + # train_steps=train_steps, + # val_check_steps=val_check_steps, + # early_stop_patience_steps=early_stop_patience_steps, + # is_debug=is_debug, + # dataset=dataset, + # is_train=is_train, + # is_fivemodels=is_fivemodels, + # val_size=val_size, + # test_size=test_size, + # settings=settings, + # now=now, + # etadata=etadata, + # modelsindex=modelsindex, + # data=data, + # is_eta=is_eta, + # ) logger.info('模型训练完成') diff --git a/测试环境登录接口调试.ipynb b/测试环境登录接口调试.ipynb index aee5a4c..15440a6 100644 --- a/测试环境登录接口调试.ipynb +++ b/测试环境登录接口调试.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 17, + "execution_count": 1, "id": "31c0e11d-c87a-4e95-92a0-d1d09625e255", "metadata": {}, "outputs": [], @@ -15,17 +15,17 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 2, "id": "83c81b9e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'http://10.200.32.39/jingbo-api/api/server/login'" + "'http://192.168.100.53:8080/jingbo-dev/api/server/login'" ] }, - "execution_count": 18, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -44,14 +44,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 3, "id": "2b330ee3-c006-4ab1-8558-59c51ac8d86f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'data': {'account': 'api_dev',\n", + "{'data': {'account': 'api_test',\n", " 'password': 'ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=',\n", " 'tenantHashCode': '8a4577dbd919675758d57999a1e891fe',\n", " 'terminal': 'API'},\n", @@ -59,7 +59,7 @@ " 'funcOperation': '获取token'}" ] }, - "execution_count": 19, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 4, "id": "dcb6100a-ed2b-4077-a1a9-361c6cb565f9", "metadata": {}, "outputs": [], @@ -87,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 5, "id": "22c0c7c4", "metadata": {}, "outputs": [ @@ -95,7 +95,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'confirmFlg': False, 'data': {'accessToken': 'eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTczMjI3NzQzMiwianRpIjoiZDdkMTkzMzNkYzA1NDM4Y2IzOWFiM2RiYWQ5MzcwMTIifQ.dPUUcEzNwmQ5j7lug6G0dSnuSdNpDzN3RvVElierjig', 'md5Token': '669b6aa68ada10fb812612a742a1e10a'}, 'status': True}\n" + "{'confirmFlg': False, 'data': {'accessToken': 'eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfdGVzdCIsInRoIjoiOGE0NTc3ZGJkOTE5Njc1NzU4ZDU3OTk5YTFlODkxZmUiLCJsdCI6ImFwaSIsImlzcyI6IiIsInRtIjoiUEMiLCJleHAiOjE3MzM0MjMwMjQsImp0aSI6IjhjZWE4YWQ4YWU3YTQyMmY4ODkxYWY4N2RhNmFmNGI5In0.Doq76Zh4PWFr6U0ICJsWpcpFX7tALvIadgXKkt_IHTc', 'md5Token': '091cf636ce5a735ef287a312b1c5d410'}, 'status': True}\n" ] } ], @@ -105,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 6, "id": "12077ead", "metadata": {}, "outputs": [], @@ -115,89 +115,67 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 7, "id": "a7ae21d1", "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "last_update_times_df = pd.read_csv(os.path.join(dataset,'last_update_times.csv'))\n", - "\n", - "y_last_update_time = '2024-11-18'\n", - "warning_data_df = last_update_times_df[last_update_times_df['warning_date'] 0:\n", - " content = '原油特征指标预警信息：\\n\\n'\n", - " warning_data_df = warning_data_df.sort_values(by='停更周期',ascending=False)\n", - " fixed_length = 30\n", - " warning_data_df['特征名称'] = warning_data_df['特征名称'].str.replace(\" \", \"\")\n", - " content = warning_data_df.to_string(index=False, col_space=fixed_length)\n", - " \n", - "else:\n", - " logger.info(f'没有需要上传的预警信息')\n", - " content = '没有需要维护的特征指标'\n", - "warning_date = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n", - "warning_data['data']['WARNING_DATE'] = warning_date\n", - "warning_data['data']['WARNING_CONTENT'] = content" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b8009a67", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "3e1a9e67", - "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ + "上传预警信息\n", + "INFO:my_logger:上传预警信息\n", "预警上传中...\n", - "token:eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTczMjI3NzQzMiwianRpIjoiZDdkMTkzMzNkYzA1NDM4Y2IzOWFiM2RiYWQ5MzcwMTIifQ.dPUUcEzNwmQ5j7lug6G0dSnuSdNpDzN3RvVElierjig\n", - "warning_data:{'funcModule': '原油特征停更预警', 'funcOperation': '原油特征停更预警', 'data': {'WARNING_TYPE_NAME': '特征数据停更预警', 'WARNING_CONTENT': ' 停更周期 预警日期 最后更新时间 更新周期 特征名称\\n 2221 2018-10-27 2018-10-24 1.0 西北欧即期Brent211利润\\n 1026 2022-02-03 2022-01-31 1.0 B-W活跃合约价差\\n 1026 2022-02-03 2022-01-31 1.0 W-B活跃合约价差\\n 737 2022-11-19 2022-11-16 1.0 Brent/迪拜原油升贴水\\n 689 2023-01-06 2023-01-03 1.0 PVMDubaiEFS/SwapvsICEFirstMonthBrent\\n 449 2023-09-03 2023-08-31 1.0 美国RBOB期货2309月份合约价格\\n 296 2024-02-03 2024-01-31 1.0 美国墨西哥湾87#汽油现货价格\\n 268 2024-03-02 2024-02-28 1.0 Ebob连1合约\\n 226 2024-04-13 2024-04-10 1.0 中国航班执行数\\n 94 2024-08-23 2024-08-20 1.0 Kpler-海运在途原油\\n 86 2023-04-15 2023-03-31 7.0 原油：开工率：东北地区：独立炼厂（周）\\n 68 2023-08-19 2023-08-04 7.0 原油：港口库存：中国（周）\\n 30 2024-05-13 2024-04-28 7.0 中东-巴林周度原油海运出口\\n 26 2024-06-08 2024-05-24 7.0 原油：山东港口：库存：新口径（周）超季节性/3年\\n 26 2024-06-08 2024-05-24 7.0 原油：山东港口：库存：新口径（周）\\n 23 2024-11-02 2024-10-30 1.0 Brentc102:30收盘价滞后1天\\n 23 2024-11-02 2024-10-30 1.0 Brentc202:30收盘价滞后1天\\n 23 2024-11-02 2024-10-30 1.0 EFSc102:30收盘价滞后1天\\n 23 2024-11-02 2024-10-30 1.0 EFSc202:30收盘价滞后1天\\n 23 2024-06-29 2024-06-14 7.0 美国:东海岸地区:炼油厂的投入与使用情况:开工率:当周值\\n 23 2024-06-29 2024-06-14 7.0 美国:炼油厂的投入与使用情况:开工率:当周值\\n 23 2024-06-29 2024-06-14 7.0 美国:中西部地区:炼油厂的投入与使用情况:开工率:当周值\\n 23 2024-06-29 2024-06-14 7.0 美国:西海岸地区:炼油厂的投入与使用情况:开工率:当周值\\n 23 2024-06-29 2024-06-14 7.0 美国:墨西哥湾沿岸:炼油厂的投入与使用情况:开工率:当周值\\n 23 2024-06-29 2024-06-14 7.0 美国:洛基山地区:炼油厂的投入与使用情况:开工率:当周值\\n 22 2024-11-03 2024-10-31 1.0 WTIc2-c3(结算B)\\n 22 2023-10-09 2023-08-27 21.0 中东-也门周度原油海运出口\\n 22 2024-11-03 2024-10-31 1.0 Brentc2-c3(结算B)\\n 18 2024-08-05 2024-07-21 7.0 全球原油库存（剔除富查伊拉）\\n 13 2024-09-09 2024-08-25 7.0 Kpler-美国原油净进口（周度）\\n 8 2024-10-12 2024-09-27 7.0 美国墨西哥湾原油钻机数\\n 7 2024-10-19 2024-10-04 7.0 美国柴油产量占比\\n 7 2024-10-19 2024-10-04 7.0 美国汽油产量占比', 'WARNING_DATE': '2024-11-22 10:10:32'}}\n" + "INFO:my_logger:预警上传中...\n", + "token:eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfdGVzdCIsInRoIjoiOGE0NTc3ZGJkOTE5Njc1NzU4ZDU3OTk5YTFlODkxZmUiLCJsdCI6ImFwaSIsImlzcyI6IiIsInRtIjoiUEMiLCJleHAiOjE3MzM0MjMwMjQsImp0aSI6IjdiNTdhNDUxNWUwOTQzYWZhNWEwYTUxNzllM2Y0MDQ1In0.7KTHvBMEpsRPM9esVdp3MPLz_5WCjuK1vZvwkhbhfy0\n", + "INFO:my_logger:token:eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfdGVzdCIsInRoIjoiOGE0NTc3ZGJkOTE5Njc1NzU4ZDU3OTk5YTFlODkxZmUiLCJsdCI6ImFwaSIsImlzcyI6IiIsInRtIjoiUEMiLCJleHAiOjE3MzM0MjMwMjQsImp0aSI6IjdiNTdhNDUxNWUwOTQzYWZhNWEwYTUxNzllM2Y0MDQ1In0.7KTHvBMEpsRPM9esVdp3MPLz_5WCjuK1vZvwkhbhfy0\n", + "warning_data:{'funcModule': '原油特征停更预警', 'funcOperation': '原油特征停更预警', 'data': {'WARNING_TYPE_NAME': '特征数据停更预警', 'WARNING_CONTENT': '2024-12-05有34个停更', 'WARNING_DATE': '2024-12-05'}}\n", + "INFO:my_logger:warning_data:{'funcModule': '原油特征停更预警', 'funcOperation': '原油特征停更预警', 'data': {'WARNING_TYPE_NAME': '特征数据停更预警', 'WARNING_CONTENT': '2024-12-05有34个停更', 'WARNING_DATE': '2024-12-05'}}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "{\"confirmFlg\":false,\"data\":true,\"status\":true}\n" + "{'confirmFlg': False, 'data': {'accessToken': 'eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfdGVzdCIsInRoIjoiOGE0NTc3ZGJkOTE5Njc1NzU4ZDU3OTk5YTFlODkxZmUiLCJsdCI6ImFwaSIsImlzcyI6IiIsInRtIjoiUEMiLCJleHAiOjE3MzM0MjMwMjQsImp0aSI6IjdiNTdhNDUxNWUwOTQzYWZhNWEwYTUxNzllM2Y0MDQ1In0.7KTHvBMEpsRPM9esVdp3MPLz_5WCjuK1vZvwkhbhfy0', 'md5Token': '33e47710d77c32c7f3db2c83cd2bd621'}, 'status': True}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "上传预警信息成功\n", + "INFO:my_logger:上传预警信息成功\n" ] } ], "source": [ - "# def upload_warning_data(token, warning_data):\n", - "warning_data = warning_data\n", - "headers = {\"Authorization\": token}\n", - "logger.info(\"预警上传中...\")\n", - "logger.info(f\"token:{token}\")\n", - "logger.info(f\"warning_data:{warning_data}\" )\n", - "upload_res = requests.post(url=upload_warning_url, headers=headers, json=warning_data, timeout=(3, 15))\n", - "print(upload_res.text)" + "def upload_warning_data(warning_data):\n", + " token = get_head_auth_report()\n", + " warning_data = warning_data\n", + " headers = {\"Authorization\": token}\n", + " logger.info(\"预警上传中...\")\n", + " logger.info(f\"token:{token}\")\n", + " logger.info(f\"warning_data:{warning_data}\" )\n", + " upload_res = requests.post(url=upload_warning_url, headers=headers, json=warning_data, timeout=(3, 15))\n", + " if upload_res:\n", + " return upload_res\n", + " else:\n", + " logger.info(\"预警上传失败\")\n", + " return None\n", + "\n", + "\n", + "logger.info(f'上传预警信息')\n", + "try:\n", + " warning_date = datetime.datetime.now().strftime('%Y-%m-%d')\n", + " content = f'{warning_date}有34个停更'\n", + " warning_data['data']['WARNING_DATE'] = warning_date\n", + " warning_data['data']['WARNING_CONTENT'] = content\n", + " upload_warning_data(warning_data)\n", + " logger.info(f'上传预警信息成功')\n", + "except Exception as e:\n", + " logger.error(f'上传预警信息失败：{e}')" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1d47fe3c", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0baedf8f-3f6a-47c6-808d-c3363662f90f", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": {