Volume 48 Issue 7
Aug.  2022
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LIANG Shua, WANG Shibo, GE Shirong, et al. Study on dynamic modification method of 3D model of coal seam in fully mechanized working face[J]. Journal of Mine Automation，2022，48(7)：58-65, 72. DOI: 10.13272/j.issn.1671-251x.17956
Citation: LIANG Shua, WANG Shibo, GE Shirong, et al. Study on dynamic modification method of 3D model of coal seam in fully mechanized working face[J]. Journal of Mine Automation，2022，48(7)：58-65, 72. DOI: 10.13272/j.issn.1671-251x.17956
shu

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

  • 1.

    School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China

  • 2.

    School of Mechanical Electrionic and Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China

  • 3.

    Xuzhou Huada Electromechanical Technology Co., Ltd., Xuzhou 221116, China

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  • Received Date: May 22, 2022
  • Revised Date: July 14, 2022
  • Available Online: August 08, 2022
    Graphical Abstract
    • Abstract
  • 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 previous mining stage. The parameters of the above three prediction algorithms are optimized by using the grid search method of double-layer loop nesting. The obtained high-precision vertical distribution data of the coal seam floor curved surface and the coal seam thickness of the unexploited area are taken as the coal seam 3D model correction value. The correction value is used to dynamically correct the coal seam 3D model of the unexploited area in the next stage. With the continuous mining of the working face, the newly obtained correction data is used to continuously and dynamically correct and update the initial coal seam 3D model, so as to improve the precision of the initial coal seam 3D model. Therefore, the dynamic modified coal seam 3D model can reflect the actual coal seam distribution of fully mechanized working face more accurately. Taking the coal seam 3D model of 18201 working face of a coal mine in Lvliang, Shanxi Province as an example, the proposed dynamic correction method is used to correct the coal seam 3D model. Within the range of 16-23.2 m in the advancing direction of the working face, the average error of the coal seam floor after the dynamic correction is 0.068 5 m. The average error of the coal seam roof is 0.076 m. Compared with the average floor error of 0.20 m and vertical average error of 0.40 m of the coal seam thickness before correction, the precision of the coal seam 3D model after dynamic correction is greatly improved. The results confirm the effectiveness of the correction method.
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