• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学 ›› 2024, Vol. 46 ›› Issue (03): 427-439.

• 计算机网络与信息安全 • 上一篇    下一篇

基于胶囊网络的异常多分类模型

阳予晋1,王堃2,陈志刚1,徐悦1,李斌2   

  1. (1.中南大学计算机学院,湖南 长沙 410083;2.国网宁夏电力有限公司信息通信公司,宁夏 银川 753000)

  • 收稿日期:2022-09-27 修回日期:2023-06-05 接受日期:2024-03-25 出版日期:2024-03-25 发布日期:2024-03-15
  • 基金资助:
    基于复合AI算法的智能运维系统AIOps关键技术研究(5229XT20003T);国家自然科学基金(71633006);“新一代人工智能”重大项目(2020AAA009605)

An anomaly multi-classification model based on capsule network

YANG Yu-jin1,WANG Kun2,CHEN Zhi-gang1,XU Yue1,LI Bin2   

  1. (1.School of Computer Science,Central South University,Changsha 410083;
    2.Information and Telecommunication Branch,State Grid Ningxia Electric Power Co.,Ltd.,Yinchuan 753000,China)
  • Received:2022-09-27 Revised:2023-06-05 Accepted:2024-03-25 Online:2024-03-25 Published:2024-03-15

摘要: 国网公司日益庞大的服务器集群产生的大量生产运行数据,以及实时分析各类设备、系统产生的海量监控数据成为电力IT运维工作的新挑战。异常检测技术作为智能电网信息运维工作的关键技术,可以有效检测运维故障并及时告警,避免损坏敏感设备。目前一些传统异常检测方法检测的异常种类少且精度低,导致故障发现不及时。为了应对这一挑战,提出了基于胶囊网络的多维时间序列异常多分类模型NNCapsNet。首先,应用无监督算法结合专家知识对电网营销业务应用服务器性能监控数据进行预处理和标注。其次,引入胶囊网络进行分类和异常检测。五折交叉验证的实验结果表明,NNCapsNet在包含15类异常的数据集上实现了91.21%的平均分类准确度。还在包含2万条监控数据的数据集上与4个基准模型进行了对比,NNCapsNet在关键评估指标上均取得了较好的结果。

关键词: 监测数据, 电力IT运维, 异常检测, 胶囊网络, 多维时间序列分析, 无监督算法

Abstract: The increasingly large server clusters of state grid corporation generate a large amount of production operation data, and real-time analysis of the massive monitoring data generated by various devices and systems has become a new challenge in power IT operation and maintenance work. As a key technology of intelligent grid information operation and maintenance work, anomaly detection technology can effectively detect operation and maintenance faults and provide timely alarms to avoid damage to sensitive equipment. Currently, some traditional anomaly detection methods have few types of anomalies and low precision, resulting in delayed fault detection. To address this challenge, this article proposes a multi-dimensional time series anomaly detection method based on capsule networks, NNCapsNet. Firstly, the unsupervised algorithm is applied in combination with expert knowledge to preprocess and label the performance monitoring data of grid marketing business application servers. Secondly, the capsule network is introduced for classification and anomaly detection. Experimental results obtained through five-fold cross-validation show that NNCapsNet achieves an average classification accuracy of 91.21% on a dataset containing 15 types of anomalies. At the same time, compared with four benchmark models on the  dataset containing 20 000 monitoring data, NNCapsNet achieves good results in key evaluation indicators. 

Key words: monitoring data, power IT operation and maintenance, abnormal detection, capsule network, multi-dimensional time series analysis, unsupervised algorithm