Distribution Communication Configuration Audit Method Based on Deep Learning Technology

This paper presents a novel approach to automate and intelligentize the configuration comparison and anomaly detection of power distribution communication equipment using a GWO-LSTM model, which integrates the Grey Wolf Optimization (GWO) algorithm with Long Short-Term Memory (LSTM) networks. Traditional methods rely heavily on manual inspection of each device's configuration, which is both time-consuming and prone to errors. In contrast, our method leverages automated data collection, processing, and analysis to significantly enhance efficiency and accuracy. The GWO-LSTM model adaptively optimizes LSTM hyperparameters and effectively captures the temporal characteristics of configuration data, enabling high-precision anomaly detection and substantially improving system safety and stability. Furthermore, the proposed method supports real-time monitoring of equipment configurations, promptly identifying anomalies and generating detailed audit reports. These reports provide maintenance personnel with immediate feedback and optimization recommendations, ensuring the continuous stable operation of the system. By reducing dependency on manual labor, our approach lowers human resource costs and minimizes the risk of errors caused by human factors. The high degree of automation, intelligent analysis, and excellent scalability of the system make it suitable for various power distribution communication devices. The standard configuration library can be flexibly expanded to accommodate different device types and configuration requirements, adapting to future technological advancements and equipment updates. This work provides robust support for equipment management and maintenance decision-making.