基于KNN神经网络的无人机作业操控系统研究  

Research on UAV Operation Control System Based on KNN Neural Network

作　　者：夏晶晶[1] Xia Jingjing(Henan University of Animal Husbandry and Economy,Zhengzhou 450046,China)

机构地区：[1]河南牧业经济学院,郑州450046

出　　处：《农机化研究》2024年第7期183-187,共5页Journal of Agricultural Mechanization Research

基　　金：河南省自然科学基金项目(202300410189)。

摘　　要：为进一步提升我国农业植保无人机的精准作业效率,针对其操控系统展开优化研究。选定KNN神经网络算法为执行理念,以无人机作业控制原理为基础,搭建正确的过程参数动态计算模型,进行操控系统的算法实现与调控配置,并展开基于KNN神经网络的无人机喷施作业试验。试验结果表明:KNN神经网络算法下的无人机操控系统运行稳定,过程参数的分类准确率相对提高了8.00%,目标喷施流量与试验喷施流量的偏差率相对降低了5.71%,农药喷施均匀度可提升至94.75%,整机作业综合效率明显提升。此设计理念以计算机智能数据处理为出发点,对无人机的高效率全面发挥有一定的推动作用,可用于类似智能农机装备的控制系统改进与开发,具有一定的参考价值。In order to further improve the precision operation efficiency of agricultural plant protection UAV in China,op-timization research was carried out on its control system.The KNN neural network algorithm was selected as the imple-mentation concept.Based on the UAV operation control principle,a correct dynamic calculation model of process parame-ters was established to realize the algorithm and control configuration of the control system.The UAV spray operation test based on the KNN neural network showed that the UAV control system under the KNN neural network algorithm operated stably,and the classification accuracy of process parameters was relatively improved by 8.0%,the deviation rate between the target spraying flow and the test spraying flow had been reduced by 5.71%,the uniformity of pesticide spraying could be increased to 94.75%,and the overall efficiency of the whole machine operation had been significantly improved.This design concept takes the computer intelligent data processing as the starting point,which has a certain role in promoting the high efficiency and full play of the UAV.It would be used for the improvement and development of the control system of similar intelligent agricultural machinery equipment,and be a strong practical reference significance.

关 键 词：无人机 操控系统 神经网络算法 分类准确率 喷施均匀度 

分 类 号：S252[农业科学—农业机械化工程]