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聚烯烃配置更新

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workpc 2025-07-07 10:37:23 +08:00
parent 68ec1c8ffb
commit 830f369dc6
2 changed files with 237 additions and 129 deletions
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254
config_juxiting.py
View File

@ -1,3 +1,4 @@
from decimal import Decimal
import logging import logging
import os import os
import logging.handlers import logging.handlers
@ -86,7 +87,6 @@ bdwdname = [
] ]
modelsindex = [{ modelsindex = [{
'NHITS': 'SELF0000077', 'NHITS': 'SELF0000077',
'Informer': 'SELF0000078', 'Informer': 'SELF0000078',
@ -137,140 +137,138 @@ data = {
ClassifyId = 1161 ClassifyId = 1161
# # 变量定义--线上环境
# 变量定义--线上环境 # server_host = '10.200.32.39'
server_host = '10.200.32.39' # login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
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_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"
upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save" # query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos" # # 上传数据项值
# 上传数据项值 # push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList" # # 上传停更数据到市场信息平台
上传停更数据到市场信息平台 # push_waring_data_value_list_url = f"http://{server_host}/jingbo-api/api/basicBuiness/crudeOilWarning/crudeSaveOrupdate"
push_waring_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/crudeSaveOrupdate" # # 获取预警数据中取消订阅指标ID
获取预警数据中取消订阅指标ID # get_waring_data_value_list_url = f"http://{server_host}/jingbo-api/api/basicBuiness/crudeOilWarning/dataList"
get_waring_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/dataList"
login_data = { # login_data = {
"data": { # "data": {
"account": "api_dev", # "account": "api_dev",
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
"tenantHashCode": "8a4577dbd919675758d57999a1e891fe", # "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
"terminal": "API" # "terminal": "API"
}, # },
"funcModule": "API", # "funcModule": "API",
"funcOperation": "获取token" # "funcOperation": "获取token"
} # }
upload_data = { # upload_data = {
"funcModule":'研究报告信息', # "funcModule": '研究报告信息',
"funcOperation":'上传聚烯烃PP价格预测报告', # "funcOperation": '上传聚烯烃PP价格预测报告',
"data":{ # "data": {
    "groupNo":'000211' # 用户组编号 # "groupNo": '000211' # 用户组编号
        "ownerAccount":'36541', #报告所属用户账号  36541 - 贾青雪 # "ownerAccount": '36541', # 报告所属用户账号  36541 - 贾青雪
        "reportType":'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST # "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 # "fileBase64": '', # 文件内容base64
        "categoryNo":'jxtjgycbg', # 研究报告分类编码 # "categoryNo": 'jxtjgycbg', # 研究报告分类编码
        "smartBusinessClassCode":'JXTJGYCBG', #分析报告分类编码 # "smartBusinessClassCode": 'JXTJGYCBG', # 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" :"JXTJGYCBG", # 报告部门 - 002000621000  SH期货研究部   # "reportDeptCode": "JXTJGYCBG", # 报告部门 - 002000621000  SH期货研究部  
        "productGroupCode":"RAW_MATERIAL"  # 商品分类 # "productGroupCode": "RAW_MATERIAL"   # 商品分类
  } # }
} # }
warning_data = { # warning_data = {
"funcModule": '原油特征停更预警', # "funcModule": '原油特征停更预警',
"funcOperation": '原油特征停更预警', # "funcOperation": '原油特征停更预警',
"data": { # "data": {
"groupNo": "000211", # "groupNo": "000211",
'WARNING_TYPE_NAME': '特征数据停更预警', # 'WARNING_TYPE_NAME': '特征数据停更预警',
'WARNING_CONTENT': '', # 'WARNING_CONTENT': '',
'WARNING_DATE': '' # 'WARNING_DATE': ''
} # }
} # }
query_data_list_item_nos_data = { # query_data_list_item_nos_data = {
"funcModule": "数据项", # "funcModule": "数据项",
"funcOperation": "查询", # "funcOperation": "查询",
"data": { # "data": {
"dateStart":"20200101", # "dateStart": "20200101",
"dateEnd":"20241231", # "dateEnd": "20241231",
"dataItemNoList":["Brentzdj","Brentzgj"] # 数据项编码,代表 brent最低价和最高价 # "dataItemNoList": ["Brentzdj", "Brentzgj"] # 数据项编码,代表 brent最低价和最高价
} # }
} # }
push_data_value_list_data = { # push_data_value_list_data = {
"funcModule": "数据表信息列表", # "funcModule": "数据表信息列表",
"funcOperation": "新增", # "funcOperation": "新增",
"data": [ # "data": [
{"dataItemNo": "91230600716676129", # {"dataItemNo": "91230600716676129",
"dataDate": "20230113", # "dataDate": "20230113",
"dataStatus": "add", # "dataStatus": "add",
"dataValue": 100.11 # "dataValue": 100.11
}, # },
{"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY", # {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
"dataDate": "20230113", # "dataDate": "20230113",
"dataStatus": "add", # "dataStatus": "add",
"dataValue": 100.55 # "dataValue": 100.55
}, # },
{"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY", # {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
"dataDate": "20230113", # "dataDate": "20230113",
"dataStatus": "add", # "dataStatus": "add",
"dataValue": 100.55 # "dataValue": 100.55
} # }
] # ]
} # }
push_waring_data_value_list_data = { # push_waring_data_value_list_data = {
"data": { # "data": {
"crudeOilWarningDtoList": [ # "crudeOilWarningDtoList": [
{ # {
"lastUpdateDate": "20240501", # "lastUpdateDate": "20240501",
"updateSuspensionCycle": 1, # "updateSuspensionCycle": 1,
"dataSource": "9", # "dataSource": "9",
"frequency": "1", # "frequency": "1",
"indicatorName": "美元指数", # "indicatorName": "美元指数",
"indicatorId": "myzs001", # "indicatorId": "myzs001",
"warningDate": "2024-05-13" # "warningDate": "2024-05-13"
} # }
], # ],
"dataSource": "9" # "dataSource": "9"
}, # },
"funcModule": "商品数据同步", # "funcModule": "商品数据同步",
"funcOperation": "同步" # "funcOperation": "同步"
} # }
get_waring_data_value_list_data = { # get_waring_data_value_list_data = {
"data": "9", "funcModule": "商品数据同步", "funcOperation": "同步"} # "data": "9", "funcModule": "商品数据同步", "funcOperation": "同步"}
# 八大维度数据项编码 # # 八大维度数据项编码
bdwd_items = { # bdwd_items = {
'ciri': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE', # 'ciri': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE',
'benzhou': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE01', # 'benzhou': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE01',
'cizhou': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE02', # 'cizhou': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE02',
'gezhou': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE03', # 'gezhou': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE03',
'ciyue': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE04', # 'ciyue': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE04',
'cieryue': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE05', # 'cieryue': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE05',
'cisanyue': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE06', # 'cisanyue': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE06',
'cisiyue': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE07', # 'cisiyue': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE07',
} # }
# 生产环境数据库 # # 生产环境数据库
host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com' # host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
port = 3306 # port = 3306
dbusername ='jingbo' # dbusername = 'jingbo'
password = 'shihua@123' # password = 'shihua@123'
dbname = 'jingbo' # dbname = 'jingbo'
table_name = 'v_tbl_crude_oil_warning' # table_name = 'v_tbl_crude_oil_warning'
# 变量定义--测试环境 # 变量定义--测试环境
@ -408,6 +406,20 @@ password = '123456'
dbname = 'jingbo_test' dbname = 'jingbo_test'
table_name = 'v_tbl_crude_oil_warning' table_name = 'v_tbl_crude_oil_warning'
DEFAULT_CONFIG = {
'feature_factor_frequency': 'D',
'strategy_id': 2,
'model_evaluation_id': 1,
'tenant_code': '',
'version_num': Decimal(1),
'delete_flag': '0',
'create_user': 'admin',
'create_date': datetime.datetime.now(),
'update_user': 'admin',
'update_date': datetime.datetime.now(),
'oil_code': 'PP',
'oil_name': 'PP期货',
}
# 开关 # 开关
is_train = True # 是否训练 is_train = True # 是否训练
@ -419,10 +431,12 @@ is_fivemodels = False # 是否使用之前保存的最佳的5个模型
is_edbcode = False # 特征使用edbcoding列表中的 is_edbcode = False # 特征使用edbcoding列表中的
is_edbnamelist = False # 自定义特征，对应上面的edbnamelist is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
is_update_eta = True # 预测结果上传到eta is_update_eta = True # 预测结果上传到eta
is_update_report = True # 是否上传报告 is_update_report = False # 是否上传报告
is_update_warning_data = True # 是否上传预警数据 is_update_warning_data = True # 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_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 is_del_tow_month = True # 是否删除两个月不更新的特征
is_bdwd = False # 是否使用八大维度
# 连接到数据库 # 连接到数据库

108
main_juxiting.py
View File

@ -2,7 +2,7 @@
from lib.dataread import * from lib.dataread import *
from config_juxiting import * from config_juxiting import *
from lib.tools import SendMail, exception_logger from lib.tools import SendMail, exception_logger, convert_df_to_pydantic, exception_logger, get_modelsname
from models.nerulforcastmodels import ex_Model_Juxiting, model_losss_juxiting, pp_export_pdf from models.nerulforcastmodels import ex_Model_Juxiting, model_losss_juxiting, pp_export_pdf
import datetime import datetime
import torch import torch
@ -23,6 +23,8 @@ global_config.update({
'is_update_report': is_update_report, 'is_update_report': is_update_report,
'settings': settings, 'settings': settings,
'bdwdname': bdwdname,
# 模型参数 # 模型参数
'data_set': data_set, 'data_set': data_set,
@ -35,19 +37,18 @@ global_config.update({
'modelsindex': modelsindex, 'modelsindex': modelsindex,
'rote': rote, 'rote': rote,
'bdwd_items': bdwd_items, 'bdwd_items': bdwd_items,
'bdwdname':bdwdname,
# 特征工程开关 # 特征工程开关
'is_del_corr': is_del_corr, 'is_del_corr': is_del_corr,
'is_del_tow_month': is_del_tow_month, 'is_del_tow_month': is_del_tow_month,
'is_eta': is_eta, 'is_eta': is_eta,
'is_update_eta': is_update_eta, 'is_update_eta': is_update_eta,
'is_fivemodels': is_fivemodels, 'is_fivemodels': is_fivemodels,
'is_update_predict_value': is_update_predict_value,
'early_stop_patience_steps': early_stop_patience_steps, 'early_stop_patience_steps': early_stop_patience_steps,
# 时间参数 # 时间参数
'start_year': start_year, 'start_year': start_year,
'end_time': end_time, 'end_time': end_time or datetime.datetime.now().strftime("%Y-%m-%d"),
'freq': freq, # 保持列表结构 'freq': freq, # 保持列表结构
# 接口配置 # 接口配置
@ -90,6 +91,10 @@ global_config.update({
# 数据库配置 # 数据库配置
'sqlitedb': sqlitedb, 'sqlitedb': sqlitedb,
'bdwd_items': bdwd_items,
'is_bdwd': is_bdwd,
'db_mysql': db_mysql,
'DEFAULT_CONFIG': DEFAULT_CONFIG,
}) })
@ -150,6 +155,91 @@ def push_market_value():
config.logger.error(f"推送数据失败: {e}") config.logger.error(f"推送数据失败: {e}")
def sql_inset_predict(global_config):
df = pd.read_csv(os.path.join(config.dataset, 'predict.csv'))
df['created_dt'] = pd.to_datetime(df['created_dt'])
df['ds'] = pd.to_datetime(df['ds'])
# 获取次日预测结果
next_day_df = df[df['ds'] == df['ds'].min()]
# 获取本周预测结果
this_week_df = df[df['ds'] == df['ds'].max()]
wd = ['day_price', 'week_price']
model_name_list, model_id_name_dict = get_modelsname(df, global_config)
PRICE_COLUMNS = [
'day_price', 'week_price', 'second_week_price', 'next_week_price',
'next_month_price', 'next_february_price', 'next_march_price', 'next_april_price'
]
params_list = []
for df, price_type in zip([next_day_df, this_week_df], wd):
update_columns = [
"feature_factor_frequency = VALUES(feature_factor_frequency)",
"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(): 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。 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。
@ -364,7 +454,7 @@ def predict_main():
"%Y-%m-%d %H:%M:%S") "%Y-%m-%d %H:%M:%S")
warning_data_df['dataSource'] = 9 warning_data_df['dataSource'] = 9
if len(quxiaodingyueidlist)>0: if len(quxiaodingyueidlist) > 0:
# 去掉取消订阅的指标 # 去掉取消订阅的指标
print(warning_data_df.shape) print(warning_data_df.shape)
warning_data_df = warning_data_df[~warning_data_df['indicatorId'].isin( 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('周度', '2')
warning_data = warning_data.replace('月度', '3') warning_data = warning_data.replace('月度', '3')
warning_data = json.loads(warning_data) 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: # if is_update_warning_data:
# upload_warning_info(len(warning_data_df)) # upload_warning_info(len(warning_data_df))
# except: # except:
@ -430,6 +521,7 @@ def predict_main():
logger.info('模型训练完成') logger.info('模型训练完成')
push_market_value() push_market_value()
sql_inset_predict(global_config)
# # LSTM 单变量模型 # # LSTM 单变量模型
# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset) # 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}') # logger.info(f'预测失败：{e}')
# continue # continue
predict_main() # predict_main()
# push_market_value() # push_market_value()
sql_inset_predict(global_config)