Anomaly Detection and Optimization Strategy of Power Communication Network Service Quality Based on Machine Learning

The stability of power communication network is very important in modern power system. However, with the increase of network size and complexity, the detection and optimization of service quality anomaly has become a key challenge. This paper proposes a quality anomaly detection and optimization strategy based on machine learning for power communication networks, aiming to achieve accurate anomaly recognition through data acquisition and preprocessing, combined with supervised learning, unsupervised learning and deep learning methods. Furthermore, this paper discusses the optimization strategy of intelligent scheduling, self-healing mechanism, real-time monitoring and early warning mechanism, and deep learning and big data analysis.