基于小波神经网络的告警信息相关性挖掘策略  

Alarm Information Relevance Mining Mechanism Based on Wavelet Neural Network

作　　者：吴大鹏[1] 赵莹[1] 熊余[1] 刘晓清[2] 王汝言[1] 

机构地区：[1]重庆邮电大学宽带泛在接入技术研究所,重庆400065 [2]中国电信股份有限公司潼南分公司,重庆402660

出　　处：《电子与信息学报》2014年第10期2379-2384,共6页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61001105;61102151;61271261;61371097);重庆市自然科学重点基金(CSTC2013JJB40001;CSTC2013JJB40006);重邮青年自然科学基金(A2012-93)资助课题

摘　　要：针对现有告警信息相关性分析方法没有客观全面考虑各告警的重要程度,无法体现告警之间个体差异性等问题,该文提出一种基于小波神经网络的加权关联规则告警挖掘算法。综合告警级别、告警类型以及告警设备类型3个主要告警属性,将其作为小波神经网络的输入,通过对历史样本数据的学习确定连接权值,合理地评估各个告警属性重要程度,利用所得权值向量进一步挖掘告警加权关联规则。结果表明所提算法在权值确定时能够综合考虑告警信息的多个属性及历史经验,得到的权值更能合理地反映告警重要度,所得关联规则能够更加准确地反映告警之间的相关性。The existing alarm information correlation analysis methods take no consideration of the alarm significance, so they are unable to reflect individual differences between the alarms. In order to solve the problem, a new alarm information relevance mining mechanism based on wavelet neural network is proposed in this paper. The three key attributes of alarm information, alarm level, alarm type and alarm equipment type are considered as the inputs of the wavelet neural network respectively. Further, the weight corresponds to the importance of individual attribute, which can be determined reasonably by training with history sample. Finally, the association rules can be mined accurately. Results show that the proposed algorithm can consider multiple influence factors and history sample comprehensively, and the obtained weight can scientifically reflect the importance of the alarms;moreover, the association rules can reflect the correlation between the alarms more accurately.

关 键 词：数据挖掘 故障管理 告警关联分析 加权关联规则 小波神经网络 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]