科技创新与应用2024，Vol.14Issue(10)：138-142,5.DOI:10.19981/j.CN23-1581/G3.2024.10.034
基于DBSCAN聚类算法的卫星数据分区异常检测
尚星宇1
作者信息
- 1. 防灾科技学院 信息工程学院,河北 廊坊 065000
- 折叠
摘要
Abstract
With the launch of China's first electromagnetic monitoring satellite and the continuous emergence of massive data detected by the satellite,exploring the changing characteristics of space load data has become a current research hotspot.In order to detect the anomaly of ZH-1 satellite LAP load data,the processed data are divided into three regions:from 50°south latitude to 20°south latitude,from 20°south latitude to 20°north latitude,and from 20°north latitude to 50°north latitude.The clustering anomaly detection is carried out by using DBSCAN density clustering algorithm in turn.The results show that this method can be used for anomaly detection of LAP data.DBSCAN density clustering algorithm can be used to detect satellite abnormal data,which provides a reference for detecting abnormal satellite data and studying the changing characteristics of spatial data.关键词
ZH-1卫星/原位电子密度观测数据/异常检测/DBSCAN/聚类算法Key words
ZH-1 satellite/in situ electron density observation data/anomaly detection/DBSCAN/clustering algorithm分类
天文与地球科学引用本文复制引用
尚星宇..基于DBSCAN聚类算法的卫星数据分区异常检测[J].科技创新与应用,2024,14(10):138-142,5.
