Digital tower abnormity diagnosis method based on point cloud
The method leverages point cloud data processing and deep learning to efficiently detect structural anomalies in power line towers, addressing the inefficiencies of traditional inspection methods and enhancing power grid reliability.
CN119723557APending Publication Date: 2025-03-28STATE GRID HENAN ELECTRIC POWER ELECTRIC POWER SCI RES INST +3
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Patent Information
- Application Number
- CN202411771295.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-04
- Publication Date
- 2025-03-28
AI Technical Summary
Technical Problem
Current methods for diagnosing structural anomalies in power line towers are inefficient and labor-intensive, as they rely on individual device inspections, making it difficult to promptly identify and address potential safety hazards.
Method used
A method utilizing point cloud data processing, including preprocessing, classification, and 3D modeling, combined with deep learning algorithms for automatic classification and baseline point alignment, to quickly identify structural anomalies in power line towers.
Benefits of technology
Enables efficient and automated detection of structural anomalies in power line towers, improving the reliability of power grid operations by quickly identifying and addressing potential safety hazards.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention discloses a digital tower abnormity diagnosis method based on point cloud. According to the technical scheme, the method comprises the following steps of 1, extracting structure information of a tower from point cloud data, and generating a three-dimensional model; 2, rapidly identifying the tower with the abnormal structure according to the abnormal state of the tower structure; according to the pole tower three-dimensional model reconstruction method based on the point cloud classification technology, the point cloud data of the pole tower is acquired by utilizing the laser scanning equipment, and the point cloud data is classified and segmented by utilizing a computer in combination with a PointNet point cloud processing framework, so that the three-dimensional model of the pole tower is reconstructed. Three-dimensional laser point cloud automatic classification modeling and reference point rapid registration of the power transmission channel are realized, rapid identification of the abnormal state of the tower structure is proposed, and the hidden danger tower with the structure abnormality is efficiently checked.
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