基于煤矿井下皮带异物识别预测的设备控制方法及装置
By combining image processing and deep learning with inspection equipment and robots, the conveyor belt speed and broadcast alarms are dynamically adjusted, solving the problems of large computational load, low accuracy and insufficient adaptability in foreign object detection on underground coal mine conveyor belts, and achieving efficient and safe foreign object disposal.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HUANENG COAL TECH RES CO LTD
- Filing Date
- 2025-07-01
- Publication Date
- 2026-07-17
AI Technical Summary
Existing methods for detecting foreign objects on underground conveyor belts in coal mines involve large computational loads, low accuracy, and insufficient adaptability, leading to safety hazards, low transportation efficiency, and imperfect linkage control.
The system employs image processing and deep learning-based methods to identify foreign objects on conveyor belts. It combines inspection equipment and robots for confirmation and handling, dynamically adjusts the conveyor belt speed and broadcasts alarms, and optimizes image and deep learning algorithms to improve detection accuracy and adaptability.
It improves the reliability of foreign object detection and the efficiency of system response, reduces false alarms, enables timely handling of foreign objects, reduces safety hazards, and improves the safe operation efficiency of transport aircraft.
Smart Images
Figure CN120808232B_ABST