为了准确预测矿山独头巷道CO浓度，基于云南老厂锡矿1800运输巷甩车场独头巷道掘进工作面CO浓度监测数据，运用MATLAB曲线拟合工具箱对该独头巷道中CO浓度随时间的变化情况进行曲线拟合，建立了该矿山独头巷道中CO浓度随时间变化的数学模型。通过该模型得到该独头巷道中CO浓度值达到安全规程要求所需的时间。然后，运用卷积神经网络时间序列预测模型（CNN模型）和BP神经网络时间序列预测模型（BP模型）对独头巷道CO浓度进行预测，并比较评价指标R ²和RMSE。结果表明：BP神经网络时间序列预测模型对该独头巷道CO浓度的预测效果更好，为该矿山独头巷道CO浓度值的监测和控制提供了准确可靠的理论依据。

In order to realize the prediction of CO concentration in the single-head roadway of the mine，based on the monitoring data of CO concentration in the heading face of the single-head roadway in the 1800 transport lane of Laochang tin mine in Yunnan Province.Firstly，the MATLAB curve fitting toolbox was used to fit the curve of the change of CO concentration with time in the single-head roadway，and the mathematical model of the change of CO concentration with time in the single-head roadway of the mine was established.Through the model，the time required for the CO concentration value in the single-head roadway of the mine to reach the CO concentration value required by the safety regulations was obtained.Then，the convolutional neural network time series prediction model（CNN model） and the BP neural network time series prediction model（BP model） were used to predict the CO concentration，and the two evaluation indexes of R ² and RMSE were compared.The results show that the BP neural network time series prediction model has the better prediction effect on the CO concentration of the single-head roadway，which provides an accurate and reliable theoretical basis for the monitoring and control of the CO concentration value of the single-head roadway in the mine.

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http://www.goldsci.ac.cn/article/2024/1005-2518/1005-2518-2024-32-1-75.shtml