添加模型执行脚本
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@ -90,8 +90,8 @@ ClassifyId = 1214
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# 变量定义--测试环境
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# 变量定义--测试环境
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# server_host = '192.168.100.53' # 内网
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server_host = '192.168.100.53' # 内网
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server_host = '183.242.74.28 ' # 外网
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# server_host = '183.242.74.28' # 外网
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login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
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login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
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# 上传报告
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# 上传报告
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upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
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upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
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@ -197,7 +197,7 @@ table_name = 'v_tbl_crude_oil_warning'
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# 开关
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# 开关
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is_train = False # 是否训练
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is_train = True # 是否训练
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is_debug = True # 是否调试
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is_debug = True # 是否调试
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is_eta = True # 是否使用eta接口
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is_eta = True # 是否使用eta接口
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is_market = True # 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
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is_market = True # 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
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22
models/TEST.py
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models/TEST.py
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NHITS
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Informer
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LSTM
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iTransformer
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TSMixer
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TSMixerx
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PatchTST
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RNN
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GRU
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TCN
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BiTCN
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DilatedRNN
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MLP
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DLinear
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NLinear
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TFT
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FEDformer
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StemGNN
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MLPMultivariate
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TiDE
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DeepNPTS
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NBEATS
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@ -165,7 +165,7 @@ def ex_Model(df, horizon, input_size, train_steps, val_check_steps, early_stop_p
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# VanillaTransformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ), //报错了
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# VanillaTransformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ), //报错了
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# Autoformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ), //报错了
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# Autoformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ), //报错了
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# NBEATS(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ),
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NBEATS(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ),
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# NBEATSx (h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',activation='ReLU', ), //报错
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# NBEATSx (h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',activation='ReLU', ), //报错
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# HINT(h=horizon),
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# HINT(h=horizon),
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原油八大维度预测任务.py
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原油八大维度预测任务.py
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# 这是原油日度,周度,月度的预测,脚本
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# 遍历时间,跳过周六日,每日7点开始预测
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import datetime
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import subprocess
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import time
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def run_predictions(target_date):
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"""执行三个预测脚本"""
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scripts = [
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"main_yuanyou.py",
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"main_yuanyou_zhoudu.py",
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"main_yuanyou_yuedu.py"
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]
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# 合并命令为单个进程执行
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command = ["python"]
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command.extend(scripts)
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subprocess.run(command, check=True)
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def is_weekday(date):
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"""判断是否为工作日(周一到周五)"""
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return date.weekday() < 5 # 0-4 为周一到周五
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if __name__ == "__main__":
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start_date = datetime.date(2025, 3, 13)
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end_date = datetime.date(2100, 12, 31)
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current_date = start_date
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while current_date <= end_date:
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if is_weekday(current_date):
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# 等待到目标日期的7点
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target_time = datetime.datetime.combine(
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current_date, datetime.time(9, 40))
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while datetime.datetime.now() < target_time:
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time.sleep(60) # 每分钟检查一次
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print(f"等待到 {target_time} 开始执行任务")
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print(f"开始执行 {current_date} 的预测任务")
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run_predictions(current_date)
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current_date += datetime.timedelta(days=1)
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