一种用于沿空留巷的液压支架时空压力监测预警系统及方法
By combining wireless sensors and positioning tags with adaptive sampling frequency and dynamic graph neural networks, the problems of cable damage and early warning lag in the hydraulic support pressure monitoring system along the goaf were solved. This enabled spatiotemporal pressure monitoring and intelligent early warning of the hydraulic support, improving prediction accuracy and system stability, and reducing safety risks.
Patent Information
- Application Number
- CN202610431318.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-02
- Publication Date
- 2026-07-17
- Estimated Expiration
- 2046-04-02
AI Technical Summary
Existing hydraulic support pressure monitoring systems suffer from issues such as easily damaged cables, difficult maintenance, and high power consumption of wireless sensors in goaf-side roadway environments. Furthermore, they lack accurate perception of the spatial position of hydraulic supports and in-depth understanding of complex mine pressure patterns, resulting in low early warning accuracy and high false alarm rate, making them unsuitable for monitoring the dynamic movement of supports.
Wireless sensors and positioning tags are used to acquire the pressure and spatial position of the lower chamber of the hydraulic support column. Combined with adaptive sampling frequency control, multi-source data preprocessing, dynamic graph neural network model and reinforcement learning algorithm, spatiotemporal pressure monitoring and intelligent early warning are realized. Through spatiotemporal feature matrix construction, real-time positioning calibration, physical constraints and self-learning mechanism, the support relocation strategy is optimized.
It enables comprehensive monitoring and accurate early warning of spatial and temporal pressure changes in hydraulic supports, improves prediction accuracy and robustness, reduces safety risks, enhances operational efficiency and system stability, and adapts to pressure change characteristics under different working conditions.
Smart Images
Figure CN121993259B_ABST
Abstract
Citation Information
Patent Citations
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