Disaster damage monitoring method and identification device for electric power facilities

By constructing a multi-dimensional feature profile and a decoupled architecture for disaster monitoring, and combining static thresholds with dynamic trend analysis, we have achieved efficient identification and early warning of power facility disasters. This has formed a closed-loop self-evolving system of cloud-edge collaboration, solving the problems of low resource utilization efficiency and poor real-time response in existing technologies.

CN121542837APending Publication Date: 2026-02-17DONGFANG ELECTRONICS CO LTD
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Patent Information

Application Number
CN202511680384.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-17
Publication Date
2026-02-17

AI Technical Summary

Technical Problem

Existing disaster loss monitoring technologies fail to effectively integrate multi-source heterogeneous data at the perception level, resulting in unclear understanding of the disaster loss evolution mechanism of mechanical-electrical coupling. At the analysis and decision-making level, the utilization efficiency of computing resources is low and the real-time response is poor. At the system evolution level, there is a lack of intelligent release and optimization mechanisms, making it difficult to achieve early warning.

Method used

By constructing multi-dimensional feature profiles, dynamic scheduling of model resources and parallel processing of decoupled architecture are achieved. Combining static thresholds and dynamic trend analysis, a cloud-edge collaborative intelligent canary release mechanism is adopted to form a closed-loop self-evolving system.

Benefits of technology

It significantly improved the accuracy of disaster damage identification and early warning capabilities, extended the emergency response window, enhanced the accuracy and predictability of the monitoring system, and achieved a leap from delayed alarms to early warnings.

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Abstract

The invention relates to an electric power facility disaster damage monitoring method and an identification device, and belongs to the technical field of electric power facility disaster damage monitoring. The method comprises the steps of constructing a multi-dimensional feature portrait of a disaster damage identification device by collecting multi-source data, and dynamically scheduling and matching a pre-layering model for concurrent tasks based on the multi-dimensional feature portrait; performing decoupling analysis on the multi-dimensional feature portrait through the pre-layering model, and completing classification and positioning in parallel; data uploaded by all disaster damage identification devices are aggregated to train and generate a new model, a multi-dimensional feature weight is set based on a release target to calculate a comprehensive score, and a gray release target is screened. According to the method, dynamic scheduling of model resources is realized through the multi-dimensional feature portraits, and the utilization efficiency and response real-time performance of edge-side resources are improved; classification and positioning tasks are processed in parallel by adopting a decoupling architecture, so that the recognition precision is improved; a closed-loop self-evolution system is constructed by means of an intelligent gray release mechanism based on cloud edge cooperation, and the monitoring accuracy, predictability and continuous optimization capability are comprehensively improved.
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