摘要
Abstract
The traditional clustering anomaly data detection algorithm has the problem of poor clustering effect and low detection efficiency when dealing with the environmental parameters of electromechanical equipment with high dimension,large data amount and chaotic distribution of outliers.Therefore,on the basis of the traditional anomaly detection algorithm,a prior clustering based environmental parameter anomaly detection algorithm of electromechanical equipment is proposed.In this algorithm,the historical data is used to construct prior clustering to ensure that the cluster construction cannot be affected by too many abnormal environmental parameters.The concept of density is introduced to ensure the reliability of cluster centers when selecting cluster centers,and the data points around the selected cluster centers are removed in the process of selecting cluster centers to prevent the selected cluster centers from being concentrated in a certain area,so as to improve the clustering effect.In the process of anomaly detection,the data to be detected are put into the prior clustering for matching.Once the testing data cannot match any of the known clusters,it is marked as abnormal data.The experimental results show that the proposed algorithm has the characteristics of high detection rate and low false positive rate in the abnormal detection of electromechanical equipment environmental parameters.In the abnormal detection of 2 000 cases of data,the detection accuracy rate can reach 97.5%,which is better than 97%of DBSCAN algorithm and 86%of basic K-means algorithm.Its false detection rate is as low as 0.010 6,which is better than 0.023 9 of DBSCAN algorithm and 0.022 8 of basic K-means algorithm.In comparison with basic K-means algorithm and DBSCAN algorithm,the improved model has better detection effect in the environmental parameters anomaly detection of electromechanical equipment,and has good performance in the detection of environmental abnormal data of electromechanical equipment.关键词
机电设备/环境参数/异常数据检测/先验聚类/K-means算法/密集度/聚类匹配Key words
electromechanical equipment/environmental parameters/anomaly data detection/priori clustering/K-means algorithm/degree of density/cluster matching分类
信息技术与安全科学
