农机化研究2024，Vol.46Issue(4)：7-14,8.
基于改进LightGBM的农机服务备件配置预测方法
Research on Agricultural Spare Parts Forecasting Based on Improved LightGBM
摘要
Abstract
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.关键词
农机服务/备件预测/LightGBM/机器学习Key words
agricultur machinery service/spare parts demand forecast/lightGBM/machine learning分类
农业科技引用本文复制引用
温彦博,王卓,白晓平..基于改进LightGBM的农机服务备件配置预测方法[J].农机化研究,2024,46(4):7-14,8.基金项目
国家重点研发计划项目(2020YFB1709603-1) （2020YFB1709603-1）
