为实现应用炮掘工作面炮后瓦斯实时监测数据进行突出危险性动态预测的目标，提出了一种应用一阶差分、卷积运算和MATLAB中的findpeaks函数，自动识别瓦斯体积分数监测曲线中爆破事件的方法，应用提出的自动识别方法，对某炮掘工作面的爆破事件进行了自动识别与提取，并对识别结果进行了评价；基于爆破识别与提取的结果，分析了炮后瓦斯体积分数变化特征，计算了炮后瓦斯体积分数增长速率、峰值和衰减速率3个指标与K1值的相关性，据此建立了炮掘工作面突出危险性预测模型，并将模型应用于另一炮掘工作面，对工作面前方的突出危险性进行预测。研究结果表明：提出的自动识别方法实现了对爆破时刻、爆破时的瓦斯体积分数、炮后瓦斯体积分数峰值及其时刻的自动识别与提取，识别爆破事件的召回率平均为84.13%、精准率平均为77.21%，提取出的炮后瓦斯体积分数峰值和爆破时刻的平均绝对误差分别为0.018 9%和2.323 7 min；炮后瓦斯体积分数增长速率、峰值、衰减速率都与K1值强相关，相关系数分别为0.85、0.92、0.79；应用建立的突出危险性预测模型，成功地预测出了25次喷孔事件和1次小型压出事件，预报效能为60.48%。研究结果可作为现有突出预测方法的补充，辅助钻屑瓦斯解吸指标K1值进行突出危险性预测，并可为炮掘工作面瓦斯异常识别及突出危险性预测提供指导。

In order to predict the outburst risk dynamically by using gas real-time monitoring data after blasting in blasting driving face,this paper proposed a method to automatically identify blasting event in the gas concentration monitoring curve by applying the first-order difference,convolution operation and the findpeaks function in MATLAB. By using the auto-identification method,the blasting event in a blasting driving face is automatically identified and extracted. Then the recognition result is evaluated. Based on the results of blasting identification and extraction,analyzed the variation characteristics of the gas concentration after blasting,and calculated the correlations between the growth rate,peak value,decay rate of gas concentration after blasting and K1 value,and based on these three indexes,a prediction model of outburst risk for blasting driving face is established and applied to another face to predict the outburst risk in front of the working face. The results show that the proposed auto-identification method realized the automatic identification and extraction of the blasting moment,the gas concentration at the blasting,the peak value and the moment of the gas concentration after the blasting. The recall and precision of the proposed auto-identification method are 84.13% and 77.21% respectively,and the mean absolute errors of the extracted peak gas concentration after blasting and the blasting time are 0.018 9% and 2.323 7 min,respectively. The growth rate,peak value,decay rate of gas concentration after blasting are all strongly related to the K1 value,and the correlation coefficients between three indexes and K1 value are 0.85,0.92 and 0.79 respectively. Applying the established outburst risk prediction model,25 times jet hole and 1 time small outburst were successfully predicted,with prediction efficiency of 60.48%. The results can be used as a supplement to the existing outburst risk prediction methods,assist K1 value in prediction of outburst risk,and provide guidance for the identification of abnormal gas and the prediction of outburst risk in blasting driving face.