Study on dynamic modification method of 3D model of coal seam in fully mechanized working face

The high-precision coal seam geographic information of fully mechanized working face is the key to realizing intelligent unmanned mining. However, the vertical precision of 3D model of coal seam constructed at this stage is low. The model cannot meet the actual needs of intelligent mining. In order to solve this problem, a dynamic modification method of 3D model of coal seam in fully mechanized working face is proposed. The static data of the initial coal seam 3D model and the dynamic data generated by the shearer cutting in the mining process are fused. The method is based on the prediction algorithms of long-short term memory (LSTM) network and its improved algorithm. The improved algorithms are based on the convolutional long-short term memory network (Conv LSTM) and encoder-decoder long-short term memory network (Encoder-Decoder LSTM). The coal seam floor curved surface and the coal seam thickness of the unmined area in the next stage are dynamically predicted according to the coal seam data of the previo