论文检索
期刊
全部知识仓储预印本开放期刊机构
高级检索

基于改进LightGBM的农机服务备件配置预测方法OA

Research on Agricultural Spare Parts Forecasting Based on Improved LightGBM

中文摘要英文摘要

针对农机服务网点中服务备件配置预测不准确导致农机备件资源浪费的问题,根据农机在服务网点的作业情况,提出了一种基于改进 LightGBM 的农机服务备件配置预测方法.首先,确定了农机作业环境信息、服务点信息以及备件信息三大维度内的多个特征;然后,验证了影响农机服务资源备件量的主要影响因素;接着,基于 LightGBM 建立了农机服务资源备件预测模型;最后,为了提高模型的精度和速度,通过 PSO 优化算法对 Light-GBM 农机服务资源预测模型进行改进,达到了更好的预测结果.实验结果表明:与随机森林、XGBoost 等算法相比,LightGBM 模型有更好的效果,RMSE 值为 27.67;通过 PSO 的超参数调优,LightGBM 备件预测的精确性更进一步提高,RMSE 值为 24.74,能够较为准确地预测农机服务资源在服务网点的备件需求.

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.

温彦博;王卓;白晓平

中国科学院 沈阳自动化研究所, 沈阳 110000||中国科学院 机器人与智能制造创新研究院, 沈阳 110169||中国科学院大学, 北京 100049中国科学院 沈阳自动化研究所, 沈阳 110000||中国科学院 机器人与智能制造创新研究院, 沈阳 110169

农业工程

农机服务;备件预测;LightGBM;机器学习

agricultur machinery service;spare parts demand forecast;lightGBM;machine learning

《农机化研究》 2024 (004)

7-14 / 8

国家重点研发计划项目(2020YFB1709603-1)

评论

下载量:0
点击量:0