An unmanned aerial vehicle intelligent management system and method applied to photovoltaic power station inspection

By assigning identification tags and real-time monitoring status data to the drone swarm of photovoltaic power plants, generating takeover requests, screening candidate drones, and assessing fault risks, the problems of slow fault response and task continuity risks in the inspection operations of drone swarms in photovoltaic power plants are solved, achieving efficient and reliable fault handling and risk prediction.

CN122151949APending Publication Date: 2026-06-05UPER ENERGY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
UPER ENERGY
Filing Date
2026-01-12
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

In existing technologies, when the inspection of photovoltaic power plant drone swarms is interrupted due to faults, the response is slow, there is a lack of risk prediction and backup mechanisms, resulting in mission continuity risks, and the backup decision fails to comprehensively consider multiple constraints.

Method used

By assigning unique identifiers to drones, monitoring dynamic status data in real time, generating replacement requests, screening candidate drones, comprehensively calculating takeover costs, assessing failure risks, and designating reserve replacement drones in advance, an intelligent drone management system is formed.

Benefits of technology

It enables rapid and scientific fault handling, reduces mission downtime, ensures the efficiency and safety of drone replacement, reduces the probability of cascading failures, and improves mission reliability and operational flexibility.

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Abstract

The application discloses an unmanned aerial vehicle intelligent management system and method applied to photovoltaic power station inspection, relates to the technical field of inspection management, and comprises the following steps: constructing an inspection cluster and collecting dynamic state data in real time; when a fault occurs in an unmanned aerial vehicle in the cluster, generating a replacement request containing an uncompleted task according to the identity and task progress of the unmanned aerial vehicle; screening unmanned aerial vehicles having completed their own tasks as candidate machines; selecting an optimal replacement unmanned aerial vehicle to take over the fault task by calculating and comparing comprehensive takeover costs; finally, predicting high-risk unmanned aerial vehicles based on state data of the remaining unmanned aerial vehicles and specifying a backup replacement machine for the high-risk unmanned aerial vehicles in advance; the system comprises a cluster construction module, a fault processing module, a candidate screening module, an optimal replacement module and a risk prediction module; and the application realizes dynamic takeover and risk prevention of the inspection task, and effectively improves the autonomy and task reliability of the unmanned aerial vehicle cluster inspection of the photovoltaic power station.
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