A fire detection method and system based on power data analysis

By generating power analysis maps and performing characterization information mining, the misjudgment and high cost problems of existing fire detection methods are solved, and accurate grouping and fire detection of power equipment are realized, which improves the safety and reliability of the power system.

CN119807977BActive Publication Date: 2025-07-29SHENZHEN FUHUA FIRE POWER SAFETY TECH CO LTD
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Patent Information

Application Number
CN202510287879.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-03-12
Publication Date
2025-07-29
Estimated Expiration
2045-03-12

AI Technical Summary

Technical Problem

The existing fire detection methods cannot warning of potential abnormalities in power equipment in advance, and it is difficult to conduct comprehensive and systematic analysis and evaluation of the entire power system, resulting in high cost of misjudgment and data analysis.

Method used

By obtaining the historical anomaly monitoring data set of power equipment, generating a power analysis map, performing characterization information mining, using power equipment characterization vectors for grouping, and determining the target power equipment group for fire detection.

Benefits of technology

It improves the pertinence and accuracy of fire protection inspections, reduces the cost of data analysis, promptly discovers and eliminates potential fire protection risks, and ensures the safety and reliability of power equipment.

✦ Generated by Eureka AI based on patent content.
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Abstract

The present invention provides a fire detection method and system based on power data analysis. After obtaining a set of power equipment to be analyzed and a historical abnormal monitoring data set of each power equipment to be analyzed in the set of power equipment to be analyzed, one or more abnormal operation events are determined in the historical abnormal monitoring data set. According to the historical abnormal monitoring data set, the power equipment to be analyzed and the abnormal operation events are used as graph vertices to generate a power analysis graph. The power analysis graph is mined for characterization information to obtain a power equipment characterization vector of the power equipment to be analyzed. Based on the power equipment characterization vector, the power equipment to be analyzed is grouped to obtain one or more pending power equipment groups, and a target power equipment group is determined in the pending power equipment groups. The present application can improve the accuracy of the determined power equipment groups, help focus on monitoring the equipment in the group during fire monitoring, and make the fire monitoring more targeted.
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