基于改进LightGBM的农机服务备件配置预测方法OA

In view of the current agricultural machinery service network resources distribution and the problem of spare parts waste of resources,this paper proposed a prediction method of spare parts allocation of agricultural machinery service based on improved LightGBM according to the operation of agricultural machinery in the service network.This pa-per first determines the agricultural machinery operation environment information,service information,and multiple characteristics of spare parts information in three dimensions,then based on PSO-LightGBM agricultural machinery spare parts service resources prediction model is established.To evaluate the effectiveness,we also compared our method with other machine learning methods such as(Logistic Regression,Random Forest,and XGBoost).LightGBM model has a better effect with RMSE value of 27.67.Moreover,The accuracy of LightGBM spare parts prediction is further im-proved by PSO super parameter tuning,and the RMSE value is 24.74,which can more accurately predict the spare parts demand of agricultural machinery service resources in service outlets.