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石油焦报告调整，配置调整

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This commit is contained in:
jingboyitiji 2025-03-25 15:46:30 +08:00
parent fa59c1cff4
commit 0abd38307d
4 changed files with 38 additions and 20 deletions
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36
config_shiyoujiao_lvyong.py
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@ -144,12 +144,12 @@ modelsindex = {
# 百川数据指标编码
baichuanidnamedict = {
'1588348470396480000': '石油焦滨州-友泰',
'1588348470396480000.00': '石油焦东营-海科瑞林',
'1588348470396480000.00': '石油焦东营-华联2',
'1588348470396480000.00': '石油焦东营-华联3',
'1588348470396480000.00': '石油焦东营-联合',
'1588348470396480000.00': '石油焦东营-联合3',
'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',
@ -157,6 +157,7 @@ baichuanidnamedict = {
'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',
'1588348470396480888': '江苏鑫海'
}
@ -285,16 +286,25 @@ bdwd_items = {
}
# 北京环境数据库
host = '192.168.101.27'
# 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 = 'root'
password = '123456'
dbname = 'jingbo_test'
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_train = False # 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
@ -361,11 +371,11 @@ db_name = os.path.join(dataset, 'jbsh_shiyoujiao_lvyong.db')
sqlitedb = SQLiteHandler(db_name)
sqlitedb.connect()
settings = f'{input_size}-{horizon}-{train_steps}--{k}-{data_set}-{y}'
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'石油焦铝用大模型预测报告--{end_time}.pdf' # 报告文件名
reportname = f'石油焦铝用大模型日度预测报告--{end_time}.pdf' # 报告文件名
reportname = reportname.replace(':', '-') # 替换冒号
if end_time == '':
end_time = now

8
lib/dataread.py
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@ -2222,7 +2222,7 @@ def addtimecharacteristics(df, dataset):
# 从数据库获取百川数据，接收一个百川id列表，返回df格式的数据
def get_baichuan_data(baichuanidnamedict):
baichuanidlist = list(baichuanidnamedict.keys())
baichuanidlist = [str(k) for k in baichuanidnamedict.keys()]
# 连接数据库
db = config.db_mysql
db.connect()
@ -2231,12 +2231,16 @@ def get_baichuan_data(baichuanidnamedict):
# 获取查询结果
results = db.execute_query(sql)
df = pd.DataFrame(results, columns=[
formatted_results = [tuple(item) for item in results]
df = pd.DataFrame(formatted_results, columns=[
'BAICHUAN_ID', 'DATA_DATE', 'DATA_VALUE'])
df['BAICHUAN_ID'] = df['BAICHUAN_ID'].astype('string')
df.to_csv('百川数据test.csv', index=False)
# 按BAICHUAN_ID 进行分组，然后按DATA_DATE合并
df1 = pd.DataFrame(columns=['DATA_DATE'])
for baichuan_id, group in df.groupby('BAICHUAN_ID'):
print(f"当前分组ID类型: {type(baichuan_id)}，值: {baichuan_id}") # 调试用
# group 删除BAICHUAN_ID列
group.drop(columns=['BAICHUAN_ID'], inplace=True)
# group DATA_value 转换为float类型,保留两位小数

10
main_shiyoujiao_lvyong.py
View File

@ -3,7 +3,7 @@
from lib.dataread import *
from config_shiyoujiao_lvyong import *
from lib.tools import SendMail, exception_logger
from models.nerulforcastmodels import ex_Model, model_losss, model_losss_juxiting, brent_export_pdf, tansuanli_export_pdf, pp_export_pdf, model_losss_juxiting
from models.nerulforcastmodels import ex_Model, model_losss,shiyoujiao_lvyong_export_pdf
import datetime
import torch
torch.set_float32_matmul_precision("high")
@ -219,7 +219,7 @@ def predict_main():
# 指标列表添加百川数据
df_baichuanliebiao = pd.DataFrame(
global_config['baichuanidnamedict'].items(), columns=['指标id', '指标名称'])
df_baichuanliebiao['指标分类'] = '百川'
df_baichuanliebiao['指标分类'] = '石油焦对标炼厂价格'
df_baichuanliebiao['频度'] = '其他'
df_zhibiaoliebiao = pd.concat(
[df_zhibiaoliebiao, df_baichuanliebiao], axis=0)
@ -421,15 +421,15 @@ def predict_main():
# 模型报告
logger.info('制作报告ing')
title = f'{settings}--{end_time}-预测报告' # 报告标题
reportname = f'Brent原油大模型日度预测--{end_time}.pdf' # 报告文件名
reportname = f'石油焦铝用大模型日度预测--{end_time}.pdf' # 报告文件名
reportname = reportname.replace(':', '-') # 替换冒号
brent_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
shiyoujiao_lvyong_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
reportname=reportname, sqlitedb=sqlitedb),
logger.info('制作报告end')
logger.info('模型训练完成')
push_market_value()
# push_market_value()
# # LSTM 单变量模型
# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)

4
models/nerulforcastmodels.py
View File

@ -2199,6 +2199,8 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
col = col.replace('*', '-')
col = col.replace(':', '-')
col = col.replace(r'/', '-')
col = col.replace(r'>', '-')
col = col.replace(r'<', '-')
plt.savefig(os.path.join(config.dataset, f'{col}与价格散点图.png'))
content.append(Graphs.draw_img(
os.path.join(config.dataset, f'{col}与价格散点图.png')))
@ -2517,6 +2519,8 @@ def shiyoujiao_lvyong_export_pdf(num_indicators=475, num_models=21, num_dayindic
col = col.replace('*', '-')
col = col.replace(':', '-')
col = col.replace(r'/', '-')
col = col.replace(r'>', '-')
col = col.replace(r'<', '-')
plt.savefig(os.path.join(config.dataset, f'{col}与价格散点图.png'))
content.append(Graphs.draw_img(
os.path.join(config.dataset, f'{col}与价格散点图.png')))