Design of Real-time Monitoring and Management System for Digital Power Grid Equipment Based on Intelligent Analysis

In this paper, a design scheme of real-time monitoring and management system for digital power grid equipment based on intelligent analysis is proposed. The system aims to monitor the running state of power grid equipment in real time, and use intelligent analysis technology to deeply mine the data, so as to realize the prediction and early warning of equipment faults, thus providing a strong guarantee for the safe and stable operation of power grid. The core of the system design is to build an efficient and stable realtime monitoring and management framework for the running state of power grid equipment, including data acquisition layer, data transmission layer, data processing layer and application layer. In the aspect of intelligent analysis algorithm, the system adopts a mixed model, which combines the advantages of traditional statistical methods, machine learning and deep learning to improve the sensitivity and accuracy of anomaly detection. The performance test of the system shows that the system can stably receive and process the data transmission rate, and ensure the timely processing of real-time data. The average accuracy rate of the algorithm is over $\mathbf{9 5 \%}$, which shows high reliability. In addition, the system runs continuously for more than 100 hours, during which there is no crash or performance degradation, which proves the stability of the system. The security test results show that the system can effectively resist common network attacks and protect the integrity of data and services.