聚烯烃配置更新
This commit is contained in:
workpc
parent
68ec1c8ffb
commit
830f369dc6
@ -1,3 +1,4 @@
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from decimal import Decimal
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import logging
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import os
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import logging.handlers
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@ -86,7 +87,6 @@ bdwdname = [
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]
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modelsindex = [{
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'NHITS': 'SELF0000077',
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'Informer': 'SELF0000078',
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@ -137,140 +137,138 @@ data = {
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ClassifyId = 1161
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# 变量定义--线上环境
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server_host = '10.200.32.39'
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login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
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upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
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upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
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query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
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# 上传数据项值
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push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
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上传停更数据到市场信息平台
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push_waring_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/crudeSaveOrupdate"
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获取预警数据中取消订阅指标ID
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get_waring_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/dataList"
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# # 变量定义--线上环境
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# server_host = '10.200.32.39'
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# login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
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# upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
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# upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
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# query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
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# # 上传数据项值
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# push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
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# # 上传停更数据到市场信息平台
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# push_waring_data_value_list_url = f"http://{server_host}/jingbo-api/api/basicBuiness/crudeOilWarning/crudeSaveOrupdate"
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# # 获取预警数据中取消订阅指标ID
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# get_waring_data_value_list_url = f"http://{server_host}/jingbo-api/api/basicBuiness/crudeOilWarning/dataList"
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login_data = {
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"data": {
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"account": "api_dev",
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"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
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"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
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"terminal": "API"
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},
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"funcModule": "API",
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"funcOperation": "获取token"
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}
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# login_data = {
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# "data": {
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# "account": "api_dev",
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# "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
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# "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
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# "terminal": "API"
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# },
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# "funcModule": "API",
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# "funcOperation": "获取token"
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# }
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upload_data = {
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"funcModule":'研究报告信息',
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"funcOperation":'上传聚烯烃PP价格预测报告',
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"data":{
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"groupNo":'000211' # 用户组编号
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"ownerAccount":'36541', #报告所属用户账号 36541 - 贾青雪
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"reportType":'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
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"fileName": '', #文件名称
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"fileBase64": '' ,#文件内容base64
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"categoryNo":'jxtjgycbg', # 研究报告分类编码
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"smartBusinessClassCode":'JXTJGYCBG', #分析报告分类编码
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"reportEmployeeCode":"E40482" ,# 报告人 E40482 - 管理员 0000027663 - 刘小朋
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"reportDeptCode" :"JXTJGYCBG", # 报告部门 - 002000621000 SH期货研究部
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"productGroupCode":"RAW_MATERIAL" # 商品分类
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}
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}
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# upload_data = {
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# "funcModule": '研究报告信息',
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# "funcOperation": '上传聚烯烃PP价格预测报告',
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# "data": {
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# "groupNo": '000211' # 用户组编号
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# "ownerAccount": '36541', # 报告所属用户账号 36541 - 贾青雪
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# "reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
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# "fileName": '', # 文件名称
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# "fileBase64": '', # 文件内容base64
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# "categoryNo": 'jxtjgycbg', # 研究报告分类编码
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# "smartBusinessClassCode": 'JXTJGYCBG', # 分析报告分类编码
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# "reportEmployeeCode": "E40482", # 报告人 E40482 - 管理员 0000027663 - 刘小朋
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# "reportDeptCode": "JXTJGYCBG", # 报告部门 - 002000621000 SH期货研究部
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# "productGroupCode": "RAW_MATERIAL" # 商品分类
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# }
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# }
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warning_data = {
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"funcModule": '原油特征停更预警',
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"funcOperation": '原油特征停更预警',
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"data": {
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"groupNo": "000211",
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'WARNING_TYPE_NAME': '特征数据停更预警',
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'WARNING_CONTENT': '',
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'WARNING_DATE': ''
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}
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}
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# warning_data = {
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# "funcModule": '原油特征停更预警',
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# "funcOperation": '原油特征停更预警',
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# "data": {
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# "groupNo": "000211",
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# 'WARNING_TYPE_NAME': '特征数据停更预警',
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# 'WARNING_CONTENT': '',
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# 'WARNING_DATE': ''
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# }
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# }
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query_data_list_item_nos_data = {
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"funcModule": "数据项",
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"funcOperation": "查询",
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"data": {
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"dateStart":"20200101",
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"dateEnd":"20241231",
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"dataItemNoList":["Brentzdj","Brentzgj"] # 数据项编码,代表 brent最低价和最高价
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}
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}
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# query_data_list_item_nos_data = {
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# "funcModule": "数据项",
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# "funcOperation": "查询",
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# "data": {
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# "dateStart": "20200101",
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# "dateEnd": "20241231",
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# "dataItemNoList": ["Brentzdj", "Brentzgj"] # 数据项编码,代表 brent最低价和最高价
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# }
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# }
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push_data_value_list_data = {
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"funcModule": "数据表信息列表",
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"funcOperation": "新增",
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"data": [
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{"dataItemNo": "91230600716676129",
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"dataDate": "20230113",
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"dataStatus": "add",
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"dataValue": 100.11
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},
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{"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
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"dataDate": "20230113",
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"dataStatus": "add",
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"dataValue": 100.55
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},
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{"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
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"dataDate": "20230113",
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"dataStatus": "add",
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"dataValue": 100.55
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}
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]
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}
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# push_data_value_list_data = {
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# "funcModule": "数据表信息列表",
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# "funcOperation": "新增",
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# "data": [
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# {"dataItemNo": "91230600716676129",
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# "dataDate": "20230113",
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# "dataStatus": "add",
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# "dataValue": 100.11
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# },
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# {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
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# "dataDate": "20230113",
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# "dataStatus": "add",
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# "dataValue": 100.55
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# },
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# {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
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# "dataDate": "20230113",
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# "dataStatus": "add",
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# "dataValue": 100.55
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# }
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# ]
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# }
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push_waring_data_value_list_data = {
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"data": {
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"crudeOilWarningDtoList": [
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{
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"lastUpdateDate": "20240501",
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"updateSuspensionCycle": 1,
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"dataSource": "9",
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"frequency": "1",
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"indicatorName": "美元指数",
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"indicatorId": "myzs001",
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"warningDate": "2024-05-13"
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}
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],
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"dataSource": "9"
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},
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"funcModule": "商品数据同步",
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"funcOperation": "同步"
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}
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# push_waring_data_value_list_data = {
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# "data": {
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# "crudeOilWarningDtoList": [
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# {
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# "lastUpdateDate": "20240501",
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# "updateSuspensionCycle": 1,
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# "dataSource": "9",
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# "frequency": "1",
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# "indicatorName": "美元指数",
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# "indicatorId": "myzs001",
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# "warningDate": "2024-05-13"
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# }
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# ],
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# "dataSource": "9"
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# },
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# "funcModule": "商品数据同步",
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# "funcOperation": "同步"
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# }
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get_waring_data_value_list_data = {
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"data": "9", "funcModule": "商品数据同步", "funcOperation": "同步"}
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# get_waring_data_value_list_data = {
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# "data": "9", "funcModule": "商品数据同步", "funcOperation": "同步"}
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# 八大维度数据项编码
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bdwd_items = {
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'ciri': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE',
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'benzhou': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE01',
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'cizhou': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE02',
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'gezhou': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE03',
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'ciyue': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE04',
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'cieryue': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE05',
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'cisanyue': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE06',
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'cisiyue': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE07',
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}
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# # 八大维度数据项编码
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# bdwd_items = {
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# 'ciri': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE',
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# 'benzhou': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE01',
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# 'cizhou': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE02',
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# 'gezhou': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE03',
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# 'ciyue': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE04',
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# 'cieryue': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE05',
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# 'cisanyue': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE06',
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# 'cisiyue': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE07',
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# }
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# 生产环境数据库
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host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
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port = 3306
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dbusername ='jingbo'
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password = 'shihua@123'
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dbname = 'jingbo'
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table_name = 'v_tbl_crude_oil_warning'
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# # 生产环境数据库
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# host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
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# port = 3306
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# dbusername = 'jingbo'
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# password = 'shihua@123'
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# dbname = 'jingbo'
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# table_name = 'v_tbl_crude_oil_warning'
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# 变量定义--测试环境
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@ -408,6 +406,20 @@ password = '123456'
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dbname = 'jingbo_test'
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table_name = 'v_tbl_crude_oil_warning'
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DEFAULT_CONFIG = {
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'feature_factor_frequency': 'D',
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'strategy_id': 2,
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'model_evaluation_id': 1,
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'tenant_code': '',
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'version_num': Decimal(1),
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'delete_flag': '0',
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'create_user': 'admin',
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'create_date': datetime.datetime.now(),
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'update_user': 'admin',
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'update_date': datetime.datetime.now(),
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'oil_code': 'PP',
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'oil_name': 'PP期货',
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}
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# 开关
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is_train = True # 是否训练
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@ -419,10 +431,12 @@ is_fivemodels = False # 是否使用之前保存的最佳的5个模型
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is_edbcode = False # 特征使用edbcoding列表中的
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is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
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is_update_eta = True # 预测结果上传到eta
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is_update_report = True # 是否上传报告
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is_update_report = False # 是否上传报告
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is_update_warning_data = True # 是否上传预警数据
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is_update_predict_value = True # 是否上传预测值到市场信息平台
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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
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is_del_tow_month = True # 是否删除两个月不更新的特征
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is_bdwd = False # 是否使用八大维度
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# 连接到数据库
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112
main_juxiting.py
112
main_juxiting.py
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from lib.dataread import *
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from config_juxiting import *
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from lib.tools import SendMail, exception_logger
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from lib.tools import SendMail, exception_logger, convert_df_to_pydantic, exception_logger, get_modelsname
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from models.nerulforcastmodels import ex_Model_Juxiting, model_losss_juxiting, pp_export_pdf
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import datetime
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import torch
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@ -23,6 +23,8 @@ global_config.update({
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'is_update_report': is_update_report,
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'settings': settings,
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'bdwdname': bdwdname,
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# 模型参数
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'data_set': data_set,
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@ -35,19 +37,18 @@ global_config.update({
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'modelsindex': modelsindex,
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'rote': rote,
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'bdwd_items': bdwd_items,
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'bdwdname':bdwdname,
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# 特征工程开关
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'is_del_corr': is_del_corr,
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'is_del_tow_month': is_del_tow_month,
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'is_eta': is_eta,
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'is_update_eta': is_update_eta,
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'is_fivemodels': is_fivemodels,
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'is_update_predict_value': is_update_predict_value,
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'early_stop_patience_steps': early_stop_patience_steps,
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# 时间参数
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'start_year': start_year,
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'end_time': end_time,
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'end_time': end_time or datetime.datetime.now().strftime("%Y-%m-%d"),
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'freq': freq, # 保持列表结构
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# 接口配置
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@ -90,6 +91,10 @@ global_config.update({
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# 数据库配置
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'sqlitedb': sqlitedb,
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'bdwd_items': bdwd_items,
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'is_bdwd': is_bdwd,
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'db_mysql': db_mysql,
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'DEFAULT_CONFIG': DEFAULT_CONFIG,
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})
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@ -150,6 +155,91 @@ def push_market_value():
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config.logger.error(f"推送数据失败: {e}")
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def sql_inset_predict(global_config):
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df = pd.read_csv(os.path.join(config.dataset, 'predict.csv'))
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df['created_dt'] = pd.to_datetime(df['created_dt'])
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df['ds'] = pd.to_datetime(df['ds'])
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# 获取次日预测结果
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next_day_df = df[df['ds'] == df['ds'].min()]
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# 获取本周预测结果
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this_week_df = df[df['ds'] == df['ds'].max()]
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wd = ['day_price', 'week_price']
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model_name_list, model_id_name_dict = get_modelsname(df, global_config)
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PRICE_COLUMNS = [
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'day_price', 'week_price', 'second_week_price', 'next_week_price',
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'next_month_price', 'next_february_price', 'next_march_price', 'next_april_price'
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]
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params_list = []
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for df, price_type in zip([next_day_df, this_week_df], wd):
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update_columns = [
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"feature_factor_frequency = VALUES(feature_factor_frequency)",
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||||
"oil_code = VALUES(oil_code)",
|
||||
"oil_name = VALUES(oil_name)",
|
||||
"data_date = VALUES(data_date)",
|
||||
"market_price = VALUES(market_price)",
|
||||
f"{price_type} = VALUES({price_type})",
|
||||
"model_evaluation_id = VALUES(model_evaluation_id)",
|
||||
"tenant_code = VALUES(tenant_code)",
|
||||
"version_num = VALUES(version_num)",
|
||||
"delete_flag = VALUES(delete_flag)",
|
||||
"update_user = VALUES(update_user)",
|
||||
"update_date = VALUES(update_date)"
|
||||
]
|
||||
|
||||
insert_query = f"""
|
||||
INSERT INTO v_tbl_predict_prediction_results (
|
||||
feature_factor_frequency, strategy_id, oil_code, oil_name, data_date,
|
||||
market_price, day_price, week_price, second_week_price, next_week_price,
|
||||
next_month_price, next_february_price, next_march_price, next_april_price,
|
||||
model_evaluation_id, model_id, tenant_code, version_num, delete_flag,
|
||||
create_user, create_date, update_user, update_date
|
||||
) VALUES (
|
||||
%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s
|
||||
)
|
||||
ON DUPLICATE KEY UPDATE
|
||||
{', '.join(update_columns)}
|
||||
"""
|
||||
|
||||
next_day_df = df[['ds', 'created_dt'] + model_name_list]
|
||||
|
||||
pydantic_results = convert_df_to_pydantic(
|
||||
next_day_df, model_id_name_dict, global_config)
|
||||
if pydantic_results:
|
||||
|
||||
for result in pydantic_results:
|
||||
price_values = [None] * len(PRICE_COLUMNS)
|
||||
price_index = PRICE_COLUMNS.index(price_type)
|
||||
price_values[price_index] = next_day_df[model_id_name_dict[result.model_id]].values[0]
|
||||
|
||||
params = (
|
||||
result.feature_factor_frequency,
|
||||
result.strategy_id,
|
||||
global_config['DEFAULT_CONFIG']['oil_code'],
|
||||
global_config['DEFAULT_CONFIG']['oil_name'],
|
||||
next_day_df['created_dt'].values[0],
|
||||
result.market_price,
|
||||
*price_values,
|
||||
result.model_evaluation_id,
|
||||
result.model_id,
|
||||
result.tenant_code,
|
||||
1,
|
||||
'0',
|
||||
result.create_user,
|
||||
result.create_date,
|
||||
result.update_user,
|
||||
result.update_date
|
||||
)
|
||||
params_list.append(params)
|
||||
affected_rows = config.db_mysql.execute_batch_insert(
|
||||
insert_query, params_list)
|
||||
config.logger.info(f"成功插入或更新 {affected_rows} 条记录")
|
||||
config.db_mysql.close()
|
||||
|
||||
|
||||
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。
|
||||
@ -358,13 +448,13 @@ def predict_main():
|
||||
'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'})
|
||||
'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'})
|
||||
|
||||
warning_data_df['warningDate'] = datetime.date.today().strftime(
|
||||
"%Y-%m-%d %H:%M:%S")
|
||||
warning_data_df['dataSource'] = 9
|
||||
|
||||
if len(quxiaodingyueidlist)>0:
|
||||
|
||||
if len(quxiaodingyueidlist) > 0:
|
||||
# 去掉取消订阅的指标
|
||||
print(warning_data_df.shape)
|
||||
warning_data_df = warning_data_df[~warning_data_df['indicatorId'].isin(
|
||||
@ -376,7 +466,8 @@ def predict_main():
|
||||
warning_data = warning_data.replace('周度', '2')
|
||||
warning_data = warning_data.replace('月度', '3')
|
||||
warning_data = json.loads(warning_data)
|
||||
push_waring_market_data(warning_data,dataSource=warning_data_df['dataSource'].values[0])
|
||||
push_waring_market_data(
|
||||
warning_data, dataSource=warning_data_df['dataSource'].values[0])
|
||||
# if is_update_warning_data:
|
||||
# upload_warning_info(len(warning_data_df))
|
||||
# except:
|
||||
@ -430,6 +521,7 @@ def predict_main():
|
||||
logger.info('模型训练完成')
|
||||
|
||||
push_market_value()
|
||||
sql_inset_predict(global_config)
|
||||
|
||||
# # LSTM 单变量模型
|
||||
# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
|
||||
@ -464,6 +556,8 @@ if __name__ == '__main__':
|
||||
# logger.info(f'预测失败：{e}')
|
||||
# continue
|
||||
|
||||
predict_main()
|
||||
# predict_main()
|
||||
|
||||
# push_market_value()
|
||||
|
||||
sql_inset_predict(global_config)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user