Reasonable analysis of grain temperature statistical parameters can assist grain depot inspectors to analyze and detect conditions of historical grain reserves. This paper presents a method of using grain temperature statistical parameters to detect grain inventory modes (empty and aeration) based on DBSCAN (Density-based spatial clustering of applications with noise) algorithm. Statistical parameters of grain temperature during normal storage about one year from 27 grain warehouses in China were calculated and clustered with the DBSCAN algorithm. According to the clustering results, parameters analyzing and grain inventory modes detection experiments were conducted. The results of parameters analyzing showed that grain temperature differences between adjacent layers and aggregation ratios of four-layers grain temperatures could be used to detect empty warehouse, and that the recall rate and precision rate reached 100%. The results of parameters analyzing also showed that grain temperature change rate and standard deviation change rate could be used to detect aeration periods, and that the recall rate was about 85.4% and precision rate was about 97.4%.

对粮食温度统计参数进行合理分析，可以辅助粮库督查人员对历史粮食储备情况进行分析检测。本文提出了一种基于DBSCAN（基于密度的噪声应用空间聚类）算法，利用粮食温度统计参数来检测粮食库存模式（空和通气）的方法。采用DBSCAN算法对全国27个粮食仓库正常储存一年左右的粮食温度统计参数进行计算聚类。根据聚类结果，进行了参数分析和粮食库存模式检测实验。参数分析结果表明，相邻层间粮温差和四层粮温聚集率可用于空仓检测，召回率和准确率均达到100%。参数分析结果还表明，可以利用谷物温度变化率和标准偏差变化率来检测曝气期，召回率约为85.4%，准确率约为97.4%。