摘要
介绍了广东联通在AI创新领域进行的一项网络智能化运维实践,提出一种基于AI关联分析的网络设备配置异常监测方法,创造性地以AI关联分析中得到的弱关联规则构建异常特征模型,从海量配置数据中快速发现配置异常。该算法具备自学习、自挖掘能力,具备良好的泛化性和应用推广性,能有效应对5G时代大量网络新建和网络改造带来的配置稽核工作量爆发式增长。
It introduces a network intelligent operation and maintenance practice of Guangdong Unicom in the field of AI innovation,and proposes a network device configuration anomaly monitoring method based on AI association analysis,which creatively uses weak association rules obtained from AI association analysis to build anomaly feature model,and quickly finds configuration anomalies from massive configuration data.The algorithm has the ability of self-learning and self mining,has good generaliza-tion and application promotion,and can effectively cope with the explosive growth of configuration audit workload caused by a large number of network construction and network transformation in 5G era.
作者
刘惜吾
马丹丹
叶晓斌
李亚梦
Liu Xiwu;Ma Dandan;Ye Xiaobin;Li Yameng(China Unicom Guangdong Branch,Guangzhou 510627,China;China Unicom Research Institute,Beijing 100176,China)
出处
《邮电设计技术》
2021年第8期15-19,共5页
Designing Techniques of Posts and Telecommunications
关键词
AI
关联规则
异常特征模型
5G
配置稽核
AI
Association rules
Anomaly feature model
5G
Configuration audit