Bird damage risk assessment method and system based on bird side activity traces

By combining multimodal data collected by UAVs using vision and acoustics, and utilizing multi-scale target detection and time-series prediction models, the problems of passive obstruction and uneven resource allocation in bird damage control have been solved. This has enabled dynamic quantitative assessment and differentiated control of bird damage risks, improving the pertinence of control measures and optimizing operation and maintenance resources.

CN121660465APending Publication Date: 2026-03-13STATE GRID FUJIAN ELECTRIC POWER RES INST +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-08
Publication Date
2026-03-13

AI Technical Summary

Technical Problem

Existing technologies for bird pest control suffer from passive obstruction, limitations of single-modal perception, and a lack of time-series analysis and prediction capabilities, leading to problems such as delayed operation and maintenance decisions and uneven resource allocation.

Method used

By combining drone visual inspection and pole acoustic data acquisition, and utilizing multi-scale target detection models, acoustic recognition models, and time-series prediction models, image data of bird side movement traces and environmental sounds are integrated to achieve dynamic quantitative assessment and differentiated prevention and control of bird damage risks.

Benefits of technology

It enables comprehensive perception and accurate assessment of bird damage risks, improves the pertinence of prevention and control measures and optimizes operation and maintenance resources, and enhances the flexibility of risk prediction and the effectiveness of prevention and control.

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Abstract

The invention relates to a bird damage risk assessment method and system based on bird side surface activity traces. The method comprises the following steps: acquiring image data, environment sound and historical bird-related faults of bird side surface activity traces of a power transmission line; using a pre-trained multi-scale target detection model to perform bird damage trace identification on the image data, and outputting visual features including bird nest density, bird droppings coverage and bird prevention facility integrity; performing acoustic recognition on the environment sound by using a pre-trained acoustic recognition model, and outputting acoustic features representing activity intensity of high-risk bird species; fusing the visual feature, the acoustic feature and the historical bird-related fault by using a preset weight to obtain a fused feature; inputting the fusion features into a pre-trained time sequence prediction model to obtain a bird damage risk level at the current moment and a bird damage risk development trend in a preset period; and generating a corresponding differential protection strategy based on the risk level at the current moment and the bird damage risk development trend.
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