矿井巷道风速智能感知技术研究进展

Research Progress on Intelligent Perception Technology for Wind Speed in Mine Tunnels

矿井通风系统智能化是推进智能矿山建设、保障矿井安全生产的关键环节,通风参数作为基础数据来源,是矿井通风系统智能化建设的重要保障。而矿井巷道风速智能感知技术发展过程中存在传感器精度及可靠性优化、传感器测风误差修正、平均风速智能快速预测、传感器布设优化等关键科学技术问题有待改善。综述了传感器技术、高精度智能风速预测等研究成果,总结了各项技术的优劣及适用范围,提出了基于PSO−GRU神经网络构建的巷道断面平均风速智能预测模型,该模型能够有效提高矿井巷道平均风速测算的精度,可为通风参数智能感知技术的发展提供理论参考。

The intelligent mine ventilation systems is a key link of advancing intelligent mine construction and ensuring safe mine production.As the fundamental data source,ventilation parameters are essential for the intelligent construction of mine ventilation systems.However,during the development of intelligent wind speed sensing technology for mine tunnels,there are key scientific and technological issues that need to be addressed,such as optimizing sensor accuracy and reliability,correcting sensor wind measurement errors,intelligent and rapid prediction of average wind speed,and optimizing sensor layout.This paper studies the cutting−edge achievements in the field from aspects such as sensor technology and high−precision intelligent wind speed prediction,summarizes the advantages,disadvantages,and applicable scopes of various technologies,and proposes an intelligent prediction model for the average wind speed in tunnel sections based on the PSO−GRU neural network.This model can effectively improve the accuracy of calculating the average wind speed in mine tunnels and provide a theoretical reference for the development of intelligent sensing technology for ventilation parameters.