实现故障监控的智能预警  

The Realization of Intelligent Pre-warning of Exceptions

作　　者：李虹[1] 韩永佳[1] LI Hong;HAN Yong-jia(China Mobile Group Guangdong Co.,Ltd.Dongguan Branch,Guangdong Dongguan 523000,China)

机构地区：[1]中国移动通信集团广东有限公司东莞分公司,广东东莞523000

出　　处：《软件》2020年第6期237-241,共5页Software

摘　　要：本文提出了一种基于K-平均算法的智能监控预警算法。对于被监控的系统,该算法首先对所发生的故障进行分类,然后对故障类两两进行分析,建立故障实例之间的关联关系。在此基础上,该算法进一步搜索关联故障实例在故障类中的最佳分布并计算故障类之间的绝对和相对关联度。当关联度达到设定的阈值,我们则认为分组故障类存在关联关系,彼此之间存在相互触发动因。本文正是通过寻找这种关联关系来帮助实现故障监控的连带预警功能,实现潜在故障规避。为了对所提出的算法进行客观的评估,我们以企业内部的关键业务系统作为样本数据进行测试。测试结果表明,本算法能够高效并较准确地挖掘故障之间的关联性,对故障的智能监控预警具有实际意义。This paper presents an algorithm of intelligent pre-warning of exceptions based on K-means algorithm.For the system monitored,the algorithm firstly classifies the exceptions already occurred.Then,it works out the associate relationship of the exception instances for every two exception classifications and finds out the best distribution of the associated exception instances in the exception classifications.Thus,the algorithm calculates the absolute and relative association degree of every two exception classifications and chooses those exception classifications whose association degree meets the threshod request as the associated ones.Thus,the algorithm is able to pre-warn the exception of one classification possible to happen when it detects some exceptions of the associated classification already happened,and help to tell us to do something to avoid it.To evaluate the algorithm justly,we test it using some of our key business systems as data samples.The result shows that our algorithm can discover the relationship of exceptions efficiently and fairly exactly.It is quite valuable in the intelligent pre-warning of exceptions.

关 键 词：K-平均算法 分类 故障 关联度 监控 预警 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]