摘要
设备状态反映农机设备工作性能,由于当前检测方法无法描述农机设备状态的时变性,导致农机设备状态检测误差大、检测正确率低。为此,设计了基于计算机数据挖掘的农机设备状态智能检测方法。首先,分析了农机设备状态智能检测的研究现状,找到各种检测方法局限性;然后,采集农机设备状态数据,提取农机设备状态检测特征,并利用计算机数据挖掘建立农机设备状态自能检测模型;最后,进行了农机设备状态检测仿真测试。结果表明:该方法的农机设备状态检测正确率超过95%,且农机设备状态的误检率远低于当前其它农机设备状态检测方法,可以实现农机设备状态的实时检测,实际应用价值更高。
Equipment status reflects the working performance of agricultural machinery and equipment,which has strong time-varying characteristics.Current intelligent detection methods cannot describe the time-varying status of agricultural machinery and equipment,resulting in large error and low detection accuracy.In order to improve the detection effect of agricultural machinery and equipment status,a design based on meter is designed.Intelligent state detection method of agricultural machinery equipment is proposed based on computer data mining.Firstly,the research status of intelligent state detection of agricultural machinery equipment is analyzed,and the limitations of various detection methods are found.Then,the status data of agricultural machinery equipment are collected,the status detection characteristics of agricultural machinery equipment are extracted,and the self-examination of agricultural machinery equipment state is established by using computer data mining.At last,the simulation test of the state detection of agricultural machinery equipment is carried out.The correct rate of the state detection of agricultural machinery equipment in this method is more than 95%,and the false detection rate of the state of agricultural machinery equipment is much lower than that of other current methods of state detection of agricultural machinery equipment.The time of state detection of agricultural machinery equipment is shorter and the real-time state of agricultural machinery equipment can be realized.Detection is of higher practical application value.
作者
陈翔
Chen Xiang(Chengdu Agricultural College,Wenjiang 611130,China)
出处
《农机化研究》
北大核心
2020年第4期18-22,共5页
Journal of Agricultural Mechanization Research
基金
四川省教育厅科学技术研究项目(ZCKL14785)
关键词
农机设备状态
智能检测
数据挖掘
计算机技术
误检率
state of agricultural machinery equipment
intelligent detection
data mining
computer technology
false detection rate
