Gas is an important factor affecting mine safety,but the existing gas prediction work ignores the hetero-geneity of multi-granularity data,resulting in low prediction accuracy.Single granularity data cannot fully express the characteristics of gas change,and the existing methods cannot fully mine the data characteristics under different gran-ularity.Based on the idea of multi-granularity,multi-granularity data is constructed by CNN aggregation,and with the feature extraction ability of LSTM and multi-head self-attention mechanism,a coal mine gas multi-granularity pre-diction model based on multi-head self-attention mechanism(MGPM)is proposed.The model can effectively meet the construction of different granularity data in gas prediction tasks,and realize the in-depth mining of coal mine gas data under different granularity characteristics.The experimental results show that the proposed model reduces the prediction error compared with the baseline model.
关键词
瓦斯预测/多粒度/特征提取/多头注意力
Key words
Gas prediction/Multi-granularity/Feature extraction/Multi-headattention
维普
万方数据