Research of alarm correlation technique for edge cloud computing in power communication network

• Published in: Diànzǐ jìshù yīngyòng Vol. 47; no. 4; pp. 17 - 23
• Main Authors: Li Jixuan, Wu Zichen, Guo Tao, Zhu Pengyu, Wu Jihua
• Format: Journal Article
• Language: Chinese
• Published: National Computer System Engineering Research Institute of China 01.04.2021
• Subjects: alarm correlation; dbscan; edge computing; unsupervised learning
• ISSN: 0258-7998
• DOI: 10.16157/j.issn.0258-7998.201269

The demand for electric power and the coverage of power communication network has been gradually expanding, which brings the challenges that need to be addressed in the operation and maintenance of the domain. The deployment of edge nodes provides the availability of data collection, information filtering and computational support, which can heavily alleviate the pressure of management. Alarm analysis is a key and difficult problem in operation and maintenance. The traditional alarm analysis first uses rules to merge alarms, so as to reduce the workload of subsequent processing. However, the completeness of rules requires a lot of expert knowledge and human resources investment, and there are limitations. This paper proposed a novel and lightweight algorithm. In this paper, Unsupervised clustering is introduced into the alarm merging process of power communication network edge cloud computing, and the density based clustering method is combined with the existing merging rules. The experimental results show th