Railway turnout curve-based inspection anomaly early warning method and system

By performing voxelization and feature extraction on the 3D point cloud data of railway turnout areas, and combining the attention mechanism and KD tree structure, the problem of insufficient evaluation of turnout curve morphology was solved, and efficient and accurate inspection anomaly early warning was achieved.

CN122336692BActive Publication Date: 2026-08-18SICHUAN WANGDA TECH CO LTD
2 Cites 0 Cited by

Patent Information

Application Number
CN202610795658.9
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-06-04
Publication Date
2026-08-18
Estimated Expiration
2046-06-04

AI Technical Summary

Technical Problem

Existing technologies lack a comprehensive assessment of turnout curve morphology during railway turnout inspections, resulting in inconsistent registration accuracy. Furthermore, they often overlook the unique characteristics of turnout curve regions, leading to mismatches or omissions.

Method used

By acquiring 3D point cloud data of railway turnout areas, voxelization and outlier filtering are performed. Convolution operations are used to extract track features of the turnout areas. Attention mechanisms and KD tree structures are combined for parallel filtering and matching to generate track centerlines. Turnout curve curvature analysis is then performed, and finally, abnormal risk indicators are generated for inspection anomaly warning.

Benefits of technology

It enables comprehensive assessment of turnout areas, improves registration accuracy, and allows for real-time monitoring of track change trends, achieving efficient and accurate railway turnout curve inspection.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The application discloses a railway turnout curve-based inspection abnormality early warning method and system, relates to the technical field of track detection and early warning, and comprises the following steps: acquiring three-dimensional point cloud data of a railway turnout region track, performing voxelization processing and outlier filtering on the three-dimensional point cloud data, and obtaining a screening set; based on the screening set, generating a neighborhood point set through a preset neighborhood radius, performing convolution operation, outputting a reference track point cloud feature matrix, acquiring a current track point cloud feature matrix, taking the reference and current track point cloud feature matrices as inputs, and performing a parallel screening mechanism to determine a final reference point; based on the final reference point, solving track geometric offset error, performing clustering, generating a track center line using a cubic B-spline curve, performing turnout curve curvature analysis, and constructing a turnout curve parameter vector; and the application can realize real-time monitoring of track change trends and efficient and accurate inspection of railway turnout curves.
Need to check novelty before this filing date? Find Prior Art