A UAV-based power grid inspection system and method for UAV-based distribution network application.

By combining automated drone nesting power distribution application system with automatic drone nesting unit, drone swarm, edge computing unit, collaborative scheduling unit and intelligent application unit, the problems of low efficiency, poor coordination and insufficient data security in drone inspection are solved. It realizes the automation, intelligence and closed-loop management of power grid inspection, and improves inspection efficiency and defect identification accuracy.

CN122292671APending Publication Date: 2026-06-26SHENZHEN POWER SUPPLY BUREAU
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHENZHEN POWER SUPPLY BUREAU
Filing Date
2026-03-20
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Existing drone inspection technologies suffer from low efficiency, poor coordination, slow algorithm iteration, and insufficient data security. In particular, when multiple drones are coordinated and scheduled, spatial and temporal conflicts occur frequently, tasks are not allocated reasonably, information silos exist in the data interaction between multiple systems, and it is difficult to balance data transmission security and real-time performance.

Method used

The system employs a drone-based power grid inspection system, comprising an automatic drone nesting unit, a drone swarm, an edge computing unit, a collaborative scheduling unit, and an intelligent application unit. Through dual-link redundant transmission between the 5G network and the dedicated power grid, it achieves automatic take-off and landing, intelligent power replenishment, multi-modal data fusion, defect identification, and task scheduling. Combined with a digital twin model and intelligent optimization algorithms, it ensures the real-time performance and security of data transmission.

Benefits of technology

It has achieved automation, intelligence and closed-loop management of the entire power grid inspection process, which has significantly improved inspection efficiency, reduced operation and maintenance costs and the intensity of manual intervention, improved defect identification accuracy and data transmission security, shortened the model iteration cycle, and solved the problems of spatiotemporal conflicts and unreasonable task allocation in multi-machine collaborative operations.

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Abstract

This application relates to a drone-nested power distribution application system and method based on power grid drone inspection, comprising an automatic drone nesting unit, a drone swarm, an edge computing unit, a collaborative scheduling unit, and an intelligent application unit. Each unit is connected to the dedicated power grid via a dual-link redundant transmission through a 5G network. The automatic drone nesting unit provides the drone swarm with automatic take-off and landing, intelligent power replenishment, and environmental adaptive protection. The drone swarm is equipped with multimodal sensors to collect images of distribution network equipment and environmental data. The edge computing unit, integrated within the drone nesting unit, performs multimodal data fusion, defect identification, and online incremental algorithm updates. The collaborative scheduling unit, based on a digital twin model of the distribution network, achieves unified scheduling, spatiotemporal isolation, and emergency response for multiple drones and tasks. The intelligent application unit seamlessly integrates with the power grid management platform to achieve task distribution and closed-loop processing of work orders. This application significantly improves inspection efficiency and defect identification accuracy while reducing operation and maintenance costs.
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