摘要
井下巷道瓦斯浓度检测是煤矿安全生产不可或缺的重要环节。提出了一种基于改进决策树算法的自组网井下巷道瓦斯浓度检测方法。针对井下巷道复杂多变的环境,构建瓦斯浓度检测自组网。以此为基础,在传统决策树算法中嵌入瓦斯浓度数据缺失值处理环节和瓦斯浓度数据主要特征筛选环节,应用改进决策树算法构建瓦斯浓度预测模型,获取最终瓦斯浓度检测结果。选取适当的瓦斯传感器,并进行配置和部署;训练改进决策树算法以确定决策树数量最优取值,进行瓦斯浓度对比测试。测试结果表明,该方法瓦斯浓度数据传输速率最大达到了2.78 MB/s,瓦斯浓度检测结果与实际瓦斯浓度一致,充分证实了该方法的准确性。
The detection of gas concentration in underground roadway is an indispensable link in coal mine safety production.A detection method of gas concentration with ad hoc network in underground roadway based on improved decision tree algorithm was proposed.In view of the complex and changeable environment of underground roadway,an ad hoc network for gas concentration detection was constructed.On this basis,the missing value processing link and the main feature screening link of gas concentration data were embedded in the traditional decision tree algorithm,and the improved decision tree algorithm was applied to build a gas concentration prediction model to obtain the final gas concentration detection result.Selected appropriate gas sensors,configured and deployed them;trained the improved decision tree algorithm to determine the optimal value of decision tree number,and conducted gas concentration comparison test.The test results show that the maximum transmission rate of gas concentration data by this method reaches 2.78 MB/s,and the gas concentration detection results are consistent with the actual gas concentration,which fully proves that this method is accuracy.
作者
马雄
Ma Xiong(CHN Energy Shendong Coal Group Co.,Ltd.,Yulin 719300,China)
出处
《煤矿机械》
2025年第9期240-244,共5页
Coal Mine Machinery
关键词
自组网
瓦斯浓度检测
改进决策树算法
井下巷道
检测性能测试
算法参数寻优
ad hoc network
gas concentration detection
improved decision tree algorithm
underground roadway
testing performance test
optimization of algorithm parameter
