计算机科学

计算机科学 ›› 2026, Vol. 53 ›› Issue (6A): 250600105-10.doi: 10.11896/jsjkx.250600105

• 信息安全 • 上一篇    下一篇

基于多视角交叉过滤的鲁棒时序异常检测模型

张菊玲1,3, 赵以兵2, 王胜1,3, 郗宁4, 佘文魁5   

  1. 1 国网四川省电力公司电力科学研究院 成都 610043
    2 国网四川省电力公司 成都 610043
    3 新型电力系统安全与运行四川省重点实验室 成都 610043
    4 国网天府新区供电公司 成都 610200
    5 四川中电启明星信息技术有限公司 成都 610200
  • 出版日期:2026-06-16 发布日期:2026-06-12
  • 通讯作者: 佘文魁(shewenkui@126.com)
  • 作者简介:(zjl725@qq.com)
  • 基金资助:
    国网四川省电力公司科技项目(52199723002P)

Robust Time Series Anomaly Detection Model Based on Multi-view Cross Filtering

ZHANG Juling1,3, ZHAO Yibing2, WANG Sheng1,3, XI Ning4, SHE Wenkui5   

  1. 1 State Grid Sichuan Electric Power Research Institute, Chengdu 610043,China
    2 State Grid Sichuan Electric Power Company,Chengdu 610043,China
    3 Power System Security and Operation Key Laboratory of Sichuan Province,Chengdu 610043,China
    4 State Grid Tianfu New Area Power Supply Company,Chengdu 610200,China
    5 Aostar Information Technology Co.,Ltd.,Chengdu 610200,China
  • Published:2026-06-16 Online:2026-06-12
  • About author:ZHANG Juling,born in 1990,senior engineer.Her main research interests include grid network security attack and defense,data security,Internet of Things security and industrial control security.
    SHE Wenkui,born in 1982,master,se-nior engineer.His main research in-terests include cloud computing,power Internet of Things.
  • Supported by:
    Scientific Research Foundation of State Grid Sichuan Electric Power Company(52199723002P).

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摘要： 伴随着数字化转型和智能技术的高速发展,工业制造、金融交易与能源管理等领域愈发依赖大量的时序数据来支撑关键决策。异常事件的突发性不仅会对系统性能构成威胁,也严重影响整体安全性,因此如何高效地从大规模且结构复杂的数据中识别异常成为当前亟待解决的挑战。聚焦于时序异常检测问题,探讨工业数据集噪声存在对模型训练的干扰,并提出了一种改进策略。工业环境中采集的数据往往具有高维、多噪声等特点,当训练样本中混入噪声时,模型的学习过程易受扰动,导致鲁棒性降低。以往研究主要采用单一指标对噪声样本进行识别和过滤,这种方法在训练过程中可能引入累积误差,进而影响异常检测的准确性。为应对上述问题,提出了一种基于多视角交叉过滤的鲁棒时序异常检测模型(Robust Time Series Anomaly Detection Model Based on Multi-View Cross Filtering,MVCF-AD),该模型首先引入非邻域注意力概念,并结合重构误差构造了用于噪声判别的双指标体系。随后,通过构建多视角交叉过滤策略,并采用并行训练双网络的方式,利用损失排序实现对噪声样本进行动态识别与过滤。实验结果显示,MVCF-AD在不同噪声率下均表现出卓越的检测性能和鲁棒性,证明了该方法在应对数据集噪声问题上的有效性。

关键词: 时间序列, 异常检测, 数据噪声, 鲁棒学习

Abstract: Amid the rapid advancement of digital transformation and intelligent technologies,fields such as industrial manufactu-ring,financial transactions,and energy management increasingly rely on vast amounts of time series data to support critical decision-making.The sudden occurrence of anomalous events not only poses a threat to system performance but also severely affects overall security.Thus,efficiently identifying anomalies from large-scale,structurally complex data has become a pressing challenge.This paper focuses on the issue of time series anomaly detection by investigating the interference caused by noise in industrial datasets during model training and proposing an improved strategy.Data collected in industrial environments often exhibit characteristics such as high dimensionality and the presence of multiple noises.When noise is incorporated into the training samples,the learning process of the model is easily disrupted,leading to reduced robustness.Previous studies mainly adopted a single indicator to identify and filter noisy samples,a method that may introduce cumulative errors during training and consequently affect the accuracy of anomaly detection.To address the aforementioned issues,this paper proposes a robust time series anomaly detection model based on multi-view cross filtering(MVCF-AD).The model first introduces the concept of non-neighbor attention and combines it with reconstruction error to construct a dual-indicator system for noise discrimination.Subsequently,a multi-view cross filtering strategy is built,and a dual-network parallel training approach is employed.By utilizing loss ranking,the model dynamically identifies and filters noisy samples.Experimental results demonstrate that MVCF-AD exhibits excellent detection performance and robustness under various noise levels,thereby proving its effectiveness in addressing the noise issue in datasets.

Key words: Time series, Anomaly detection, Data noise, Robust learning

中图分类号: 

  • TP183
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