Real time monitoring system for electrical equipment status based on improved ant colony optimization algorithm

Electrical equipment is an indispensable part of the production process, and real-time monitoring of it is crucial to prevent equipment failures, improve production efficiency, and ensure production safety. In response to the current problems in electrical equipment status monitoring, this article adopts a real-time monitoring system for electrical equipment status based on Ant Colony Optimization (ACO) algorithm. The system fully explores the operational status and expert experience of electrical equipment during operation, and designs heuristic information to guide ants in finding more valuable solution space. Improvements can be made to the ACO algorithm to better identify potential anomalies. Research heuristic information guidance methods that integrate the characteristics of electrical equipment, fully tap into the operational status and expert experience of electrical equipment during operation, and design heuristic information to guide ants in finding more valuable solution space. On the test dataset, the recall rate of the original ACO algorithm is between 73% -76%, and the maximum recall rate of the improved ACO algorithm is 84%. The method described in this article helps to improve recall rates and fully realize real-time monitoring of electrical equipment status.