Building public electric equipment positioning and early warning system

By adjusting the electrical characteristics of equipment using an encoding-decoding model and particle swarm optimization algorithm, the problem of accuracy in monitoring faults in public electrical equipment in high-rise buildings was solved, achieving efficient and accurate fault location and early warning.

CN121542776AActive Publication Date: 2026-02-17DAOYUAN CONSTR GRP CO LTD
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Patent Information

Application Number
CN202610064189.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-19
Publication Date
2026-02-17
Estimated Expiration
2046-01-19

AI Technical Summary

Technical Problem

Existing technologies make it difficult to accurately monitor public electrical equipment in high-rise buildings, especially when there are few fault events and power signals are interfered with. It is difficult to extract the electrical characteristics of the equipment, resulting in poor accuracy in fault diagnosis.

Method used

An encoder-decoder model is used to learn the mapping relationship between line power characteristics and equipment power characteristics. The equipment power characteristics are adjusted by combining particle swarm optimization and simulated annealing algorithms. Faulty and abnormal equipment is located by using the difference index.

Benefits of technology

This significantly improves the accuracy of identifying anomalies in public electrical equipment without requiring individual monitoring of each device, while reducing monitoring costs and complexity.

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Abstract

The invention provides a building public electric equipment positioning and early warning system, and relates to the technical field of electric equipment positioning and early warning. A coding-decoding model for realizing line power feature-power feature-equipment power feature-line power feature conversion in a normal state and calculating a reconstruction error; when the reconstruction error is smaller than a threshold value, the feature adjustment module obtains the optimal adjustment value of the power feature of the equipment based on a particle swarm algorithm and a simulated annealing algorithm; and a positioning early warning module which is used for analyzing the difference degree between the power characteristics of the equipment and the optimal adjustment value so as to determine the abnormal public electric equipment. When the abnormity is judged through the reconstruction error, the closest optimal adjustment value of the power characteristics of the equipment is found based on the particle swarm optimization and the simulated annealing algorithm; and determining which public electric equipment is abnormal by analyzing the difference degree between the public electric equipment and the electric power characteristics of the equipment, so as to realize the positioning and early warning of the abnormal public electric equipment.
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