基于机器学习的农业机械运行状态预测方法研究  

Research on the Prediction Method of Agricultural Machinery Operation State Based on Machine Learning

作　　者：傅东兴 FU Dongxing(Heilongjiang Academy of Agricultural Machinery Sciences,Harbin 150081,China)

机构地区：[1]黑龙江省农业机械工程科学研究院,哈尔滨150081

出　　处：《农机使用与维修》2024年第6期70-73,共4页Agricultural Machinery Using & Maintenance

摘　　要：随着农业机械的广泛应用,实时监测和预测其运行状态,对提高农业机械运行效率、降低故障率具有重要意义。该文基于双MapReduce框架,提出了一种基于机器学习的农业机械运行状态预测模型,通过传感器获取农业机械的各项运行数据,然后利用MapReduce技术对数据进行分布式处理,以历史运行数据作为输入,提取特征并进行数据预处理构建预测模型。仿真试验结果表明,该模型对农业机械运行状态预测结果较为准确,预测时效性较高,在数据量较大的情况下具有显著应用优势,可为农业生产提供实时监测和预警,提高了农业机械的使用效率和安全性。With the wide application of agricultural machinery,real-time monitoring and prediction of its operating status is of great significance to improve the operation efficiency of agricultural machinery and reduce the failure rate.Based on the framework of dual MapReduce,a machine learning-based prediction model for the running state of agricultural machinery is proposed,which obtains the operation data of agricultural machinery through sensors,and then uses MapReduce technology to distribute the data,and takes the historical operation data as input to extract features and preprocess the data to construct a prediction model.The simulation test results show that the model is more accurate in predicting the operation status of agricultural machinery,has high prediction timeliness,and has significant application advantages when the data volume is large,which can provide real-time monitoring and early warning for agricultural production,and improve the efficiency and safety of agricultural machinery.

关 键 词：机器学习 农业机械 运行状态预测 双MapReduce 分布式处理 特征提取 

分 类 号：S220[农业科学—农业机械化工程]