基于多源数据融合的煤矿工作面瓦斯浓度预测*

为了解决瓦斯浓度预测使用的单一数据在预测中影响还不够深入的问题，提出基于LSTM神经网络的多源数据融合瓦斯浓度预测模型。模型将上隅角瓦斯浓度、采煤机速度、工作面吨煤瓦斯涌出量等不同数据融合作为输入层参数，使用Adam优化算法更新LSTM网络层参数，利用Attention机制突出关键影响瓦斯浓度的因素，开展多源数据融合的瓦斯浓度预测，结合某矿1008工作面的实际数据，分析不同数据在瓦斯浓度预测中的作用。研究结果表明:单变量下的Attention-aLSTM预测效果相比LSTM提升14.2%；多源数据融合下的Attention-aLSTM相比自身提升了5%。

Prediction of gas concentration in coal mining face based on multi-source data fusion

In order to solve the problem that the single data used in gas concentration prediction has not had enough influence in the prediction,a prediction model of gas concentration with multi-source data fusion based on LSTM network was proposed.Different data such as gas concentration at upper corner,shearer speed and gas emission amount per ton of coal in the working face were fused as the input layer parameters,and the Adam optimization algorithm was used to update the LSTM network layer parameters.The Attention mechanism was applied to highlight the key factors affecting gas concentration,and the gas concentration prediction based on multi-source data fusion was carried out.The role of different data in gas concentration prediction was analyzed combined with the actual data of 1008 working face in a mine.The results showed that the prediction effect of Attention-aLSTM under single variable was 14.2% higher than that of LSTM,and the prediction effect of Attention-aLSTM under multi-source data fusion was 5% higher than itself.