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@@ -73,7 +73,7 @@ def forecast_data_distribution(pre_data,args):
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def model_prediction(df,args):
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- mongodb_connection,mongodb_database,mongodb_model_table,model_name,col_reserve,howlongago = "mongodb://root:sdhjfREWFWEF23e@192.168.1.43:30000/",args['mongodb_database'],args['mongodb_model_table'],args['model_name'],str_to_list(args['col_reserve']),int(args['howlongago'])
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+ mongodb_connection,mongodb_database,mongodb_model_table,model_name,howLongAgo,farm_id = "mongodb://root:sdhjfREWFWEF23e@192.168.1.43:30000/",args['mongodb_database'],args['mongodb_model_table'],args['model_name'],int(args['howLongAgo']),args['farm_id']
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client = MongoClient(mongodb_connection)
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db = client[mongodb_database]
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collection = db[mongodb_model_table]
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@@ -85,13 +85,14 @@ def model_prediction(df,args):
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model_binary = model_data['model'] # 确保这个字段是存储模型的二进制数据
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# 反序列化模型
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model = pickle.loads(model_binary)
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- df['predict'] = model.predict(df[model.feature_name()])
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- df.loc[df['predict']<0,'predict']=0
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+ df['power_forecast'] = model.predict(df[model.feature_name()])
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+ df.loc[df['power_forecast'] < 0, 'power_forecast'] = 0
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df['model'] = model_name
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- df['howlongago'] = howlongago
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+ df['howLongAgo'] = howLongAgo
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+ df['farm_id'] = farm_id
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print("model predict result successfully!")
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- features_reserve = col_reserve + ['model', 'predict', 'howlongago']
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- return df[list(set(features_reserve))]
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+
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+ return df[['dateTime','howLongAgo','model','farm_id','power_forecast','realPower']]
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@app.route('/model_prediction_lightgbm', methods=['POST'])
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