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27
config_shiyoujiao_lvyong.py
View File

@ -139,31 +139,8 @@ edbnamelist = ['ds', 'y']+[edbcodenamedict[edbcodename]
for edbcodename in edbcodelist]
# eta自有数据指标编码，石油焦铝用还没新增，暂且留空
modelsindex = [{
"NHITS":"SELF0000363",
"Informer":"SELF0000364",
"LSTM":"SELF0000365",
"iTransformer":"SELF0000366",
"TSMixer":"SELF0000367",
"TSMixerx":"SELF0000368",
"PatchTST":"SELF0000369",
"RNN":"SELF0000370",
"GRU":"SELF0000371",
"TCN":"SELF0000372",
"BiTCN":"SELF0000373",
"DilatedRNN":"SELF0000374",
"MLP":"SELF0000375",
"DLinear":"SELF0000376",
"NLinear":"SELF0000377",
"TFT":"SELF0000378",
"FEDformer":"SELF0000379",
"StemGNN":"SELF0000380",
"MLPMultivariate":"SELF0000381",
"TiDE":"SELF0000382",
"DeepNPTS":"SELF0000383",
"NBEATS":"SELF0000384",
}]
modelsindex = {
}
# 百川数据指标编码
baichuanidnamedict = {

617
config_shiyoujiao_lvyong_yuedu.py
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@ -1,617 +0,0 @@
import logging
import os
import logging.handlers
import datetime
from lib.tools import MySQLDB, SQLiteHandler
# eta 接口token
APPID = "XNLDvxZHHugj7wJ7"
SECRET = "iSeU4s6cKKBVbt94htVY1p0sqUMqb2xa"
# eta 接口url
sourcelisturl = 'http://10.189.2.78:8108/v1/edb/source/list'
classifylisturl = 'http://10.189.2.78:8108/v1/edb/classify/list?ClassifyType='
uniquecodedataurl = 'http://10.189.2.78:8108/v1/edb/data?UniqueCode=4991c37becba464609b409909fe4d992&StartDate=2024-02-01'
classifyidlisturl = 'http://10.189.2.78:8108/v1/edb/list?ClassifyId='
edbcodedataurl = 'http://10.189.2.78:8108/v1/edb/data?EdbCode='
edbdatapushurl = 'http://10.189.2.78:8108/v1/edb/push'
edbdeleteurl = 'http://10.189.2.78:8108/v1/edb/business/edb/del'
edbbusinessurl = 'http://10.189.2.78:8108/v1/edb/business/data/del'
edbcodenamedict = {
'C2403283369': '预赔阳极加工利润（高端）',
'C2403285560': '预培阳极加工利润（低端）',
'C2403288616': '低硫石油焦煅烧利润',
'S6949656': '平均价：氧化铝：一级：全国',
'S5807052': '氧化铝：一级：贵阳',
'S5443355': '市场价：煤沥青：河北地区',
'S5443357': '市场价：煤沥青：山西地区',
'W000294': '国内主要港口石油焦出货量（隆重）',
'W000293': '日照港库存（隆重）',
'W000292': '港口总库存（隆重）',
'W000283': '主营石油焦产量（隆重）',
'W000282': '地炼石油焦产量（隆重）',
'W000281': '中国石油焦产量（隆重）',
'W000280': '主营石油焦开工负荷率（隆重）',
'W000279': '地炼石油焦开工负荷率（隆重）',
'ID00150273': '石油焦：1 # ：市场低端价：东北地区（日）',
'ID00150281': '石油焦：1 # ：市场主流价：东北地区（日）',
'ID00150277': '石油焦：1 # ：市场高端价：东北地区（日）',
'ID00150289': '石油焦：2 # ：市场低端价：华东地区（日）',
'ID00150285': '石油焦：2 # ：市场低端价：西北地区（日）',
'ID00150313': '石油焦：2 # ：市场主流价：华东地区（日）',
'ID00150309': '石油焦：2 # ：市场主流价：西北地区（日）',
'ID00150301': '石油焦：2 # ：市场高端价：华东地区（日）',
'ID00150297': '石油焦：2 # ：市场高端价：西北地区（日）',
'ID00150321': '石油焦：2 # A：市场低端价：山东（日）',
'ID00150329': '石油焦：2 # A：市场主流价：山东（日）',
'ID00150325': '石油焦：2 # A：市场高端价：山东（日）',
'ID00150337': '石油焦：2 # B：市场低端价：华南地区（日）',
'ID00150341': '石油焦：2 # B：市场低端价：华中地区（日）',
'ID00150361': '石油焦：2 # B：市场主流价：华南地区（日）',
'ID00150365': '石油焦：2 # B：市场主流价：华中地区（日）',
'ID00150349': '石油焦：2 # B：市场高端价：华南地区（日）',
'ID00150353': '石油焦：2 # B：市场高端价：华中地区（日）',
'ID00150333': '石油焦：2 # B：市场低端价：山东（日）',
'ID00150357': '石油焦：2 # B：市场主流价：山东（日）',
'ID00150345': '石油焦：2 # B：市场高端价：山东（日）',
'ID00150369': '石油焦：3 # ：市场低端价：华中地区（日）',
'ID00150373': '石油焦：3 # ：市场高端价：华中地区（日）',
'ID00150385': '石油焦：3 # A：市场高端价：山东（日）',
'ID00150393': '石油焦：3 # B：市场低端价：华东地区（日）',
'ID00150409': '石油焦：3 # B：市场主流价：华东地区（日）',
'ID00150401': '石油焦：3 # B：市场高端价：华东地区（日）',
'ID00146589': '海绵焦：4 # ：出厂价：华中地区：洛阳石化（日）',
'ID01242846': '石油焦：4 # B：挂牌价：华北地区：中石化燕山（日）',
'ID01300358': '石油焦：3 # C：市场低端价：山东（日）',
'ID01300357': '石油焦：3 # C：市场高端价：山东（日）',
'ID00150377': '石油焦：3 # ：市场主流价：华中地区（日）',
'ID01387643': '煅烧焦：低硫：0.5 % S：市场价：东北地区（日）',
'ID01387646': '煅烧焦：低硫：3.5 % S：市场价：东北地区（日）',
'ID01387660': '煅烧焦：中硫：3 % S，400V：市场价：山东（日）',
'ID00150381': '石油焦：3 # A：市场低端价：山东（日）',
'ID00150397': '石油焦：3 # B：市场低端价：山东（日）',
'ID00150405': '石油焦：3 # B：市场高端价：山东（日）',
'ID00146545': '海绵焦：3B：出厂价：山东：山东东明（日）',
'B3e90b34e4b9e7a6ea3': '石油焦市场均价(元/吨)',
'B6b5c53b270a3af12ac': '石油焦1 # 市场均价(元/吨)',
'B10721189a11c209a20': '石油焦2 # 市场均价(元/吨)',
'B6accfa9d2bf4735a50': '石油焦3 # 市场均价(元/吨)',
'B8a0ab5357569c385a9': '石油焦海绵焦市场均价(元/吨)',
'B19dcf45e22fbfd3e43': '石油焦海绵焦东北1 # A焦（低端）(元/吨)（百川）',
'B5832a62d1e0fba50b6': '石油焦海绵焦东北1 # A焦（高端）(元/吨)（百川）',
'B1de4fba026d4609cc7': '石油焦海绵焦东北1 # B焦（低端）(元/吨)（百川）',
'B38f89180736172490d': '石油焦海绵焦东北1 # B焦（高端）(元/吨)（百川）',
'B4f847871674c3d77f2': '石油焦海绵焦山东地炼1 # -3#焦（低端）(元/吨)',
'B1aefb8a64a5200adbd': '石油焦海绵焦山东地炼1 # -3#焦（高端）(元/吨)',
'B1df7d0afbfedfb628a': '煅烧焦东北低硫(高端S < 0.5)(元/吨)',
'B5f8f9859635876da28': '煅烧焦东北低硫(低端S < 0.5)(元/吨)',
'B2342a8c5a39fa00348': '煅烧焦华北中硫(高端S < 3.0，钒 < 400)(元/吨)',
'B051f27900397c6a35f': '煅烧焦山东中硫(高端S < 3.0，钒 < 400)(元/吨)',
'Be2a8050a48e86cae1f': '煅烧焦华东中硫(高端S < 3.0，钒 < 400)(元/吨)',
'B4a1811938f85065f6a': '煅烧焦华中中硫(高端S < 3.0，钒 < 400)(元/吨)',
'Bc197d4834ef7fb98ec': '煅烧焦华东高硫(高端S < 3.5，钒 < 400)(元/吨)',
'B62be5dbdb8c6454530': '煅烧焦低硫参考价格(元/吨)（百川）',
'Bdd813140bffc4edfa6': '煅烧焦中硫微量市场均价(元/吨)（百川）',
'B185a597decfc71915a': '预焙阳极山东低端(元/吨)（百川）',
'B1bcde6130de031bd42': '山西 改质沥青(元/吨)',
'Bb9f4a1f6dd32b4ad8a': '山东 改质沥青(元/吨)',
'C2411261557491549': '石油焦市场均价(元/吨)/4DMA',
'C2411271143174617': '石油焦市场均价(元/吨)/9DMA',
'ID01387649': '煅烧焦：中硫：3 % S，350V：市场价：华东地区（日）',
'ID01387655': '煅烧焦：中硫：3 % S，350V：市场价：山东（日）',
'RE00010076': '煅烧焦：低硫：生产毛利：东北地区（周）',
'B9d1acaf80383683da3': '石油焦总产量（周）(吨)',
'Bdaa719a38936c8dd76': '石油焦开工率（周）( % )',
'B9459d549a332b200e7': '石油焦行业总库存（周）(吨)',
'Bce6e098b9518370cff': '石油焦工厂库存（周）(吨)',
'B577ce2809772779710': '石油焦市场库存（周）(吨)',
'B5d8c564c62f3e6b77f': '石油焦成本（周）(吨)',
'B43baa98bcaa06c11a5': '石油焦利润（周）(吨)',
'Bdd0c1361d94081211c': '煅烧石油焦总产量（周）(吨)',
'B65315111fa28951b1e': '煅烧石油焦开工率（周）( % )',
'B2aff5f2632a20027d0': '煅烧石油焦行业总库存（周）(吨)',
'B29fbd31128cd71b212': '煅烧石油焦工厂库存（周）(吨)',
'B7a88313a89d1261c53': '煅烧石油焦成本（周）(吨)',
'Bd4fa36b4decec0aafa': '煅烧石油焦利润（周）(吨)',
'B9bd80eac7df81ffbd4': '预焙阳极总产量（周）(吨)',
'B27074786605f4660d2': '预焙阳极开工率（周）( % )',
'Bdc2a5985ecb56b6a0c': '预焙阳极行业总库存（周）(吨)',
'Bce8511f899e487e5b6': '预焙阳极工厂库存（周）(吨)',
'B13ec89105bd866a2bd': '预焙阳极成本（周）(吨)',
'B66c3abcfa15a2e611c': '预焙阳极利润（周）(吨)',
'Bf7efe3200f9abc0453': '电解铝开工率（周）( % )',
'Be193166f347267b1a7': '电解铝行业总库存（周）(吨)',
'Baa744fc97769353175': '电解铝工厂库存（周）(吨)',
'Bf9654603913cfc5282': '电解铝市场库存（周）(吨)',
'Bef1535c96da0d70fbc': '电解铝利润（周）(吨)',
'B7d1d0b24316d49cbdc': '煤沥青总产量（周）(吨)',
'B4303fb002ea1c214da': '煤沥青开工率（周）( % )',
'Be9a470c97e9efe660c': '煤沥青行业总库存（周）(吨)',
'B50d4d87f6b78bca587': '煤沥青工厂库存（周）(吨)',
'B46cc7d0a90155b5bfd': '煅烧焦山东高硫(高端S < 3.5，普货)(元/吨)'
}
edbcodelist = edbcodenamedict.keys()
# 临时写死用指定的列,与上面的edbcode对应，后面更改
edbnamelist = ['ds', 'y']+[edbcodenamedict[edbcodename]
for edbcodename in edbcodelist]
# eta自有数据指标编码，石油焦铝用还没新增，暂且留空
# eta自有数据指标编码 次周,隔周
bdwdname = [
'次月',
'次二月',
'次三月',
'次四月',
]
modelsindex = [{"NHITS":"SELF0000451",
"Informer":"SELF0000452",
"LSTM":"SELF0000453",
"iTransformer":"SELF0000454",
"TSMixer":"SELF0000455",
"TSMixerx":"SELF0000456",
"PatchTST":"SELF0000457",
"RNN":"SELF0000458",
"GRU":"SELF0000459",
"TCN":"SELF0000460",
"BiTCN":"SELF0000461",
"DilatedRNN":"SELF0000462",
"MLP":"SELF0000463",
"DLinear":"SELF0000464",
"NLinear":"SELF0000465",
"TFT":"SELF0000466",
"FEDformer":"SELF0000467",
"StemGNN":"SELF0000468",
"MLPMultivariate":"SELF0000469",
"TiDE":"SELF0000470",
"DeepNPTS":"SELF0000471",
"NBEATS":"SELF0000472",
},{"NHITS":"SELF0000473",
"Informer":"SELF0000474",
"LSTM":"SELF0000475",
"iTransformer":"SELF0000476",
"TSMixer":"SELF0000477",
"TSMixerx":"SELF0000478",
"PatchTST":"SELF0000479",
"RNN":"SELF0000480",
"GRU":"SELF0000481",
"TCN":"SELF0000482",
"BiTCN":"SELF0000483",
"DilatedRNN":"SELF0000484",
"MLP":"SELF0000485",
"DLinear":"SELF0000486",
"NLinear":"SELF0000487",
"TFT":"SELF0000488",
"FEDformer":"SELF0000489",
"StemGNN":"SELF0000490",
"MLPMultivariate":"SELF0000491",
"TiDE":"SELF0000492",
"DeepNPTS":"SELF0000493",
"NBEATS":"SELF0000494",
},{"NHITS":"SELF0000495",
"Informer":"SELF0000496",
"LSTM":"SELF0000497",
"iTransformer":"SELF0000498",
"TSMixer":"SELF0000499",
"TSMixerx":"SELF0000500",
"PatchTST":"SELF0000501",
"RNN":"SELF0000502",
"GRU":"SELF0000503",
"TCN":"SELF0000504",
"BiTCN":"SELF0000505",
"DilatedRNN":"SELF0000506",
"MLP":"SELF0000507",
"DLinear":"SELF0000508",
"NLinear":"SELF0000509",
"TFT":"SELF0000510",
"FEDformer":"SELF0000511",
"StemGNN":"SELF0000512",
"MLPMultivariate":"SELF0000513",
"TiDE":"SELF0000514",
"DeepNPTS":"SELF0000515",
"NBEATS":"SELF0000516",
},{"NHITS":"SELF0000517",
"Informer":"SELF0000518",
"LSTM":"SELF0000519",
"iTransformer":"SELF0000520",
"TSMixer":"SELF0000521",
"TSMixerx":"SELF0000522",
"PatchTST":"SELF0000523",
"RNN":"SELF0000524",
"GRU":"SELF0000525",
"TCN":"SELF0000526",
"BiTCN":"SELF0000527",
"DilatedRNN":"SELF0000528",
"MLP":"SELF0000529",
"DLinear":"SELF0000530",
"NLinear":"SELF0000531",
"TFT":"SELF0000532",
"FEDformer":"SELF0000533",
"StemGNN":"SELF0000534",
"MLPMultivariate":"SELF0000535",
"TiDE":"SELF0000536",
"DeepNPTS":"SELF0000537",
"NBEATS":"SELF0000538",
}]
# 百川数据指标编码
baichuanidnamedict = {
'1588348470396480901': '石油焦滨州-友泰',
'1588348470396480903': '石油焦东营-海科瑞林',
'1588348470396480902': '石油焦东营-华联2',
'1588348470396481080': '石油焦东营-华联3',
'1588348470396480905': '石油焦东营-联合',
'1588348470396481081': '石油焦东营-联合3',
'1588348470396480915': '石油焦淄博-汇丰',
'1588348470396480888': '石油焦沧州-鑫海',
'1588348470396480917': '石油焦东营-万通',
'1588348470396480925': '石油焦东营-齐润',
'1588348470396481084': '石油焦东营-尚能4',
'1588348470396480930': '石油焦潍坊-寿光鲁清',
'1588348470396480929': '石油焦滨州-鑫岳'
}
# baichuanidnamedict = {'1588348470396475286': 'test1', '1666': 'test2'} # 北京环境测试用
# eta 上传预测结果的请求体，后面发起请求的时候更改 model datalist 数据
data = {
"IndexCode": "",
"IndexName": "石油焦铝用价格预测xx模型-yy",
"Unit": "无",
"Frequency": "日度",
"SourceName": f"价格预测",
"Remark": 'ddd',
"DataList": [
{
"Date": "2024-05-02",
"Value": 333444
}
]
}
# eta 分类
# level：3才可以获取到数据，所以需要人工把能源化工下所有的level3级都找到
# url = 'http://10.189.2.78:8108/v1/edb/list?ClassifyId=1214'
# ParentId ":1160, 能源化工
# ClassifyId ":1214,原油 3912 石油焦
# ParentId ":1214,"，就是原油下所有的数据。
ClassifyId = 3707
# 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"
# 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"
# # 上传数据项值
# push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
# 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":{
# "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
# "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 = {
# "groupNo":'', # 用户组id
# "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':''
# }
# }
# query_data_list_item_nos_data = {
# "funcModule": "数据项",
# "funcOperation": "查询",
# "data": {
# "dateStart":"20200101",
# "dateEnd":"20241231",
# "dataItemNoList":["Brentzdj","Brentzgj"] # 数据项编码,代表 brent最低价和最高价
# }
# }
# 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': '原油大数据预测|FORECAST|PRICE|T',
# 'benzhou': '原油大数据预测|FORECAST|PRICE|W',
# 'cizhou': '原油大数据预测|FORECAST|PRICE|W_1',
# 'gezhou': '原油大数据预测|FORECAST|PRICE|W_2',
# 'ciyue': '原油大数据预测|FORECAST|PRICE|M_1',
# 'cieryue': '原油大数据预测|FORECAST|PRICE|M_2',
# 'cisanyue': '原油大数据预测|FORECAST|PRICE|M_3',
# 'cisiyue': '原油大数据预测|FORECAST|PRICE|M_4',
# }
# # 生产环境数据库
# host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
# port = 3306
# dbusername ='jingbo'
# password = 'shihua@123'
# dbname = 'jingbo'
# table_name = 'v_tbl_crude_oil_warning'
# # 变量定义--测试环境
server_host = '192.168.100.53:8080' # 内网
# server_host = '183.242.74.28' # 外网
login_pushreport_url = f"http://{server_host}/jingbo-dev/api/server/login"
upload_url = f"http://{server_host}/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
upload_warning_url = f"http://{server_host}/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
query_data_list_item_nos_url = f"http://{server_host}/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
# 上传数据项值
push_data_value_list_url = f"http://{server_host}/jingbo-dev/api/dw/dataValue/pushDataValueList"
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 = {
"groupNo": '', # 用户组id
"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 = {
"groupNo": '', # 用户组id
"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': ''
}
}
query_data_list_item_nos_data = {
"funcModule": "数据项",
"funcOperation": "查询",
"data": {
"dateStart": "20200101",
"dateEnd": "20241231",
"dataItemNoList": ["Brentzdj", "Brentzgj"] # 数据项编码,代表 brent最低价和最高价
}
}
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': 'syjlyycbdwdcr',
'benzhou': 'syjlyycbdwdbz',
'cizhou': 'syjlyycbdwdcz',
'gezhou': 'syjlyycbdwdgz',
'ciyue': 'syjlyycbdwdcy',
'cieryue': 'syjlyycbdwdcey',
'cisanyue': 'syjlyycbdwdcsy',
'cisiyue': 'syjlyycbdwdcsiy',
}
# 报告中八大维度数据项重命名
columnsrename={'syjlyycbdwdbz': '本周', 'syjlyycbdwdcey': '次二月', 'syjlyycbdwdcr': '次日', 'syjlyycbdwdcsiy': '次四月',
'syjlyycbdwdcsy': '次三月', 'syjlyycbdwdcy': '次月', 'syjlyycbdwdcz': '次周', 'syjlyycbdwdgz': '隔周', }
# 北京环境数据库
host = '192.168.101.27'
port = 3306
dbusername = 'root'
password = '123456'
dbname = 'jingbo_test'
# 京博测试环境
# host = 'rm-2zehj3r1n60ttz9x5ko.mysql.rds.aliyuncs.com'
# port = 3306
# dbusername = 'jingbo'
# password = 'shihua@123'
# dbname = 'jingbo-test'
table_name = 'v_tbl_crude_oil_warning'
baichuan_table_name = 'V_TBL_BAICHUAN_YINGFU_VALUE'
# select BAICHUAN_ID, DATA_DATE, DATA_VALUE from V_TBL_BAICHUAN_YINGFU_VALUE where BAICHUAN_ID in ('1588348470396475286', '1666')
# 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_market = False # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
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 = 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_update_eta = True # 预测结果上传到eta
is_update_report = True # 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 = False # 是否删除两个月不更新的特征
is_bdwd = True # 是否使用八大维度
# 连接到数据库
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)
# 数据截取日期
start_year = 2000 # 数据开始年份
end_time = '' # 数据截取日期
freq = 'M' # 时间频率，"D": 天 "W": 周"M": 月"Q": 季度"A": 年 "H": 小时 "T": 分钟 "S": 秒 "B": 工作日 "WW" 自定义周
delweekenday = True if freq == 'B' else False # 是否删除周末数据
is_corr = False # 特征是否参与滞后领先提升相关系数
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
y = '石油焦：3 # B：市场低端价：山东（日）'
avg_cols = [
]
offsite = 80
offsite_col = []
horizon = 4 # 预测的步长
input_size = 16 # 输入序列长度
train_steps = 50 if is_debug else 1000 # 训练步数,用来限定epoch次数
val_check_steps = 30 # 评估频率
early_stop_patience_steps = 5 # 早停的耐心步数
# --- 交叉验证用的参数
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
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
weight_dict = [0.4, 0.15, 0.1, 0.1, 0.25] # 权重
# 文件
data_set = '石油焦铝用指标数据.xlsx' # 数据集文件
dataset = 'shiyoujiaolvyongyuedudataset' # 数据集文件夹
# 数据库名称
db_name = os.path.join(dataset, 'jbsh_shiyoujiao_lvyong_yuedu.db')
sqlitedb = SQLiteHandler(db_name)
sqlitedb.connect()
settings = f'{input_size}-{horizon}-{train_steps}--{k}-{data_set}'
# 获取日期时间
# now = datetime.datetime.now().strftime('%Y%m%d%H%M%S') # 获取当前日期时间
now = datetime.datetime.now().strftime('%Y-%m-%d') # 获取当前日期时间
reportname = f'石油焦铝用大模型月度预测报告--{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
username = '1321340118@qq.com'
passwd = 'wgczgyhtyyyyjghi'
# recv=['liurui_test@163.com','52585119@qq.com']
recv = ['liurui_test@163.com']
# recv=['liurui_test@163.com']
title = 'reportname'
content = y+'预测报告请看附件'
file = os.path.join(dataset, 'reportname')
# file=os.path.join(dataset,'14-7-50--100-原油指标数据.xlsx-Brent连1合约价格--20240731175936-预测报告.pdf')
ssl = True
# 日志配置
# 创建日志目录（如果不存在）
log_dir = 'logs'
if not os.path.exists(log_dir):
os.makedirs(log_dir)
# 配置日志记录器
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.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()
console_handler.setFormatter(logging.Formatter('%(message)s'))
# 将处理器添加到日志记录器
logger.addHandler(file_handler)
logger.addHandler(console_handler)
# logger.info('当前配置：'+settings)

69
config_shiyoujiao_lvyong_zhoudu.py
View File

@ -144,53 +144,8 @@ bdwdname = [
'次周',
'隔周',
]
modelsindex = [
{"NHITS":"SELF0000407",
"Informer":"SELF0000408",
"LSTM":"SELF0000409",
"iTransformer":"SELF0000410",
"TSMixer":"SELF0000411",
"TSMixerx":"SELF0000412",
"PatchTST":"SELF0000413",
"RNN":"SELF0000414",
"GRU":"SELF0000415",
"TCN":"SELF0000416",
"BiTCN":"SELF0000417",
"DilatedRNN":"SELF0000418",
"MLP":"SELF0000419",
"DLinear":"SELF0000420",
"NLinear":"SELF0000421",
"TFT":"SELF0000422",
"FEDformer":"SELF0000423",
"StemGNN":"SELF0000424",
"MLPMultivariate":"SELF0000425",
"TiDE":"SELF0000426",
"DeepNPTS":"SELF0000427",
"NBEATS":"SELF0000428",
},{"NHITS":"SELF0000429",
"Informer":"SELF0000430",
"LSTM":"SELF0000431",
"iTransformer":"SELF0000432",
"TSMixer":"SELF0000433",
"TSMixerx":"SELF0000434",
"PatchTST":"SELF0000435",
"RNN":"SELF0000436",
"GRU":"SELF0000437",
"TCN":"SELF0000438",
"BiTCN":"SELF0000439",
"DilatedRNN":"SELF0000440",
"MLP":"SELF0000441",
"DLinear":"SELF0000442",
"NLinear":"SELF0000443",
"TFT":"SELF0000444",
"FEDformer":"SELF0000445",
"StemGNN":"SELF0000446",
"MLPMultivariate":"SELF0000447",
"TiDE":"SELF0000448",
"DeepNPTS":"SELF0000449",
"NBEATS":"SELF0000450",}
]
modelsindex = {
}
# 百川数据指标编码
baichuanidnamedict = {
@ -335,18 +290,18 @@ bdwd_items = {
}
# 北京环境数据库
# host = '192.168.101.27'
# port = 3306
# dbusername = 'root'
# password = '123456'
# dbname = 'jingbo_test'
host = '192.168.101.27'
port = 3306
dbusername = 'root'
password = '123456'
dbname = 'jingbo_test'
# 京博测试环境
host = 'rm-2zehj3r1n60ttz9x5ko.mysql.rds.aliyuncs.com'
port = 3306
dbusername = 'jingbo'
password = 'shihua@123'
dbname = 'jingbo-test'
# host = 'rm-2zehj3r1n60ttz9x5ko.mysql.rds.aliyuncs.com'
# port = 3306
# dbusername = 'jingbo'
# password = 'shihua@123'
# dbname = 'jingbo-test'
table_name = 'v_tbl_crude_oil_warning'

2
main_shiyoujiao_lvyong_zhoudu.py
View File

@ -235,7 +235,7 @@ def predict_main():
df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
# 数据处理
df = zhoududatachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['y'], dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['y'], dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
end_time=end_time)
else: