针对综采工作面周期来压预测的技术难题，研究了理论分析、数据采集与预处理、模型评估与优化等方法，提出了具有时空关联分析与POI(Point of Intersesting)数据的ConvLSTM模型，利用多源数据融合得到周期来压预测的最优解，实现工作面环境状态的实时感知和预测。试验结果表明:基于POI-ConvLSTM的工作面周期来压预测模型，均方误差为 0。159，R2 评价指标为 0。999，相比于Seq2Seq和ConvLSTM模型的均方误差分别降低了68。07%和4。22%。可见，融合了多元数据POI-ConvLSTM模型的预测精度更高，普适性更强，能够准确地提前预测周期来压问题。
Prediction of periodic weighting based on POI ConvLSTM model
Aiming at solving the technical difficulties in predicting the periodic weighting of fully mechanized mining face,theoretical analysis,data acquisition and pre-processing,model evaluation and optimization are studied,and the ConvLSTM model with spatio-temporal correlation analysis and POI(Point of Intersesting)data is proposed.Based on multi-source data consolidation,the optimal solution for periodic weighting prediction is obtained and real-time sensing and prediction of working face environment is realized.The test results show that the mean square error based on POI ConvLSTM model for working face periodic weighting is 0.159,and R2 evaluation index is 0.999.Compared with the Seq2Seq and ConvLSTM models,the mean square error is reduced by 68.07%and 4.22%,respectively.Therefore,the POI ConvLSTM model with multiple data has more precise prediction and universality,which will accurately predict periodic pressure in advance.
万方数据
维普
