Research Progress on Intelligent Perception Technology for Wind Speed in Mine Tunnels
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摘要:
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。
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关键词:
- 矿井通风 /
- 巷道风流特性 /
- 风速传感器 /
- 巷道平均风速 /
- PSO−GRU神经网络
Abstract: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.
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表 1 各类型风速传感器对比
Table 1. Comparison of various types of wind speed sensors
机械式风速传感器 压差式风速传感器 超声波风速传感器 涡街式传感器 时差法传感器 
优点：结构简单，成本低，易于维护；
缺点：恶劣环境中损耗快，响应速度慢优点：结构简单，低速响应速度快；
缺点：皮托管易受粉尘影响优点：精度高，量程广，不易损耗；
缺点：影响风流流场，易受粉尘影响优点：测量范围广，精度高，无易损耗部件 
下载: 导出CSV
表 2 部分巷道断面风速数据集
Table 2. Partial wind speed dataset of tunnel cross−sections
数据
编号输入数据 输出数据：
平均风速
/(m·s−1)粗糙度
常数壁面
粗糙度/m测点
高度/m测点
横坐标/m测点风速
/(m·s−1)1 0.5 0.01 0.3 2.37 0.99 1 2 0.6 0.05 2.11 2.57 7.31 5 3 0.8 0.02 2.96 2.55 3.49 3 4 1 0.03 2.46 1.00 4.47 4 5 1 0.04 1.01 1.43 2.17 2 … … … … … … … 120 0.5 0.03 2.88 3.18 11.80 10
下载: 导出CSV
表 3 三种模型对比结果
Table 3. Comparison results of three models
模型 RMSE MAE R2 PSO−GRU 0.9899 0.7901 0.9309 GRU 1.5282 0.9691 0.8129 GA−BP 1.0551 0.6010 0.8707
下载: 导出CSV
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