Method, device and program product for real-time monitoring of wind-induced vibration displacement of long-span cable net structure
CN122345374BActive Publication Date: 2026-08-28XIAMEN UNIV +1
Patent Information
- Application Number
- CN202610797646.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2026-06-04
- Publication Date
- 2026-08-28
- Estimated Expiration
- 2046-06-04
AI Technical Summary
Technical Problem
然而,受限于光照变化、遮挡、雨雾气象条件等外部环境因素,机器视觉技术在长期现场监测中的稳定性和可靠性仍存在一定不足,难以满足工程长期监测的需求
Benefits of technology
计算各监测通道采集的索力与三维空间位移数据之间的皮尔逊相关系数,作为该监测通道的第一相关性强度;
✦ Generated by Eureka AI based on patent content.
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Figure CN122345374B_ABST
Abstract
The present disclosure provides a large-span cable net structure wind-induced vibration displacement real-time monitoring method, device and program product, relating to the technical field of computers. The wind-induced vibration displacement real-time monitoring method of the present disclosure constructs a fiber optic cable force sensor by laying out a plurality of key monitoring points on the surface of the bearing cable and the surface of the cable, and rigidly fixes a machine vision target at the top of the mast. Real three-dimensional spatial displacement data of the top of the mast is obtained in a short period of time using the constructed binocular camera vision system, and a deep learning network is trained with cable force as input and three-dimensional spatial displacement data as output. The trained network can identify the three-dimensional spatial displacement of the top of the mast in real time based on the cable force, without the need to construct complex mechanical models and structural dynamics equations to accurately characterize the stress path of complex cable net structures, effectively solving the problem of difficulty in modeling by physical methods and difficulty in capturing nonlinear coupling relationships.
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Citation Information
Patent Citations
Structural body health state monitoring method based on multi-scale cascade graph neural network
CN119167088A
Full-automatic cable vibration high-precision measurement method based on machine vision technology
CN120369097A