一种煤矿瓦斯浓度超前预测与自适应调速控制方法
By constructing a gas concentration prediction model that integrates spatiotemporal features and a dynamic cloud map, and combining it with a multi-objective optimization algorithm, the problems of low prediction accuracy and insufficient collaborative optimization in coal mine gas concentration were solved, and adaptive speed control of coal mining machines was realized, thereby improving the safety and production efficiency of coal mining.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- XIAN UNIV OF SCI & TECH
- Filing Date
- 2026-04-29
- Publication Date
- 2026-07-17
AI Technical Summary
In existing technologies, the accuracy of coal mine gas concentration prediction is not high, and there is a lack of collaborative optimization based on multi-dimensional information. This results in the coal mining machine speed operating independently from the extraction and ventilation systems, making it impossible to accurately predict risks and affecting production efficiency and safety.
By constructing a gas concentration prediction model that integrates spatiotemporal characteristics, combining dynamic gas concentration cloud maps and gas accumulation velocity vectors, risk zones are divided, segmented speed strategies are formulated, and a multi-objective optimization algorithm is used to generate a coal mining machine speed-position look-ahead control curve to achieve adaptive speed regulation control.
It has achieved dynamic quantification and spatial positioning of gas risk, improved the coordinated control of coal mining machine speed and gas migration, and significantly improved the safety and economic benefits of coal mining.
Smart Images
Figure CN122407285A_ABST