本文選題：農(nóng)業(yè)機(jī)械 + 作業(yè)軌跡��； 參考：《農(nóng)機(jī)化研究》2017年04期

【摘要】：農(nóng)業(yè)機(jī)械在田間作業(yè)過程中,時(shí)間和空間維度上產(chǎn)生大量的作業(yè)數(shù)據(jù),對(duì)農(nóng)業(yè)機(jī)械作業(yè)軌跡數(shù)據(jù)進(jìn)行聚類分析在農(nóng)機(jī)作業(yè)狀態(tài)分析和效率研究中具有重要意義。為此,應(yīng)用DBSCAN(Density-Based Spatial Clustering of Applications with Noise)算法對(duì)模擬農(nóng)業(yè)機(jī)械作業(yè)軌跡進(jìn)行分析,設(shè)計(jì)了基于密度聚類的農(nóng)機(jī)作業(yè)狀態(tài)分類算法。對(duì)模擬數(shù)據(jù)的聚類結(jié)果表明:該方法正確分類農(nóng)機(jī)作業(yè)班次內(nèi)的有效作業(yè)軌跡、空行轉(zhuǎn)移軌跡和停歇軌跡的精度達(dá)到98.33%、70%和100%。聚類作業(yè)軌跡反映的農(nóng)機(jī)利用率為95.35%,為農(nóng)機(jī)田間作業(yè)軌跡研究提供了依據(jù)。
[Abstract]:A large number of operational data are produced in the time and space dimensions of agricultural machinery in the process of field operation. Cluster analysis of agricultural machinery track data is of great significance in the analysis of agricultural machinery operation state and efficiency. Therefore, the DBSCAN(Density-Based Spatial Clustering of Applications with Noise algorithm is used to analyze the track of simulated agricultural machinery operation, and the classification algorithm of agricultural machinery operation status based on density clustering is designed. The clustering results of the simulated data show that this method correctly classifies the effective working trajectories in the shifts of agricultural machinery operations, and the accuracy of the empty transfer trajectory and the rest track reaches 98.3370% and 100% respectively. The utilization ratio of farm machinery reflected by cluster operation track was 95.35, which provided the basis for the research of farm machinery track.
【作者單位】： 新疆農(nóng)業(yè)大學(xué)機(jī)械交通學(xué)院;
【基金】：國家自然科學(xué)基金項(xiàng)目(51465057)
【分類號(hào)】：S22;TP311.13
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本文編號(hào)：1845625