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Research on 3D Laser Scanning Monitoring Method for Mining Subsidence Based on the Auxiliary for Probability Integral Method

机译:基于概率积分法的辅助矿井矿区3D激光扫描监测方法研究

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摘要

When 3D laser scanning technology is used to monitor the surface deformation of mining subsidence in mining area, the surface of the working face is covered with a large number of vegetation, and the surface water accumulates above the working face, which makes the point cloud data obtained by the 3D laser scanning difficult to denoise, or even missing. At this time, the conventional 3D laser scanning technology can not obtain the surface deformation field of mining subsidence. Aiming at the above problems, the 3D laser scanning monitoring method for mining subsidence based on the auxiliary for PIM proposed (3DLS-PIM). Firstly, this paper introduces the PIM prediction model. Secondly, the mining subsidence observation equation based on 3DLS-PIM is constructed, and then the prediction parameters of PIM are solved based on quantum particle swarm optimization (QPSO). Finally, according to the PIM and its parameters, the mining subsidence surface deformation basin is predicted and obtain the surface deformation field of mining subsidence. Robust experiments show that QPSO has a certain ability to resist random errors and gross errors. The results of engineering application show that the mining area 3D deformation monitoring method proposed in this paper has certain engineering application value.
机译:当3D激光扫描技术用于监测采矿区采矿沉降的表面变形时,工作面的表面被大量植被覆盖,地面水积聚在工作面上,这使得点云数据由3D激光扫描获得难以去噪,甚至丢失。此时,传统的3D激光扫描技术无法获得采矿沉降的表面变形领域。针对上述问题,基于PIM的辅助挖掘3D激光扫描监测方法(3DLS-PIM)。首先,本文介绍了PIM预测模型。其次,构造了基于3DLS-PIM的挖掘沉降观察方程,然后基于量子粒子群优化(QPSO)来解决PIM的预测参数。最后,根据PIM及其参数,预测采矿沉降表面变形盆地并获得矿业沉降的表面变形领域。稳健的实验表明,QPSO具有一定的抵抗随机误差和粗略误差的能力。工程应用结果表明,本文提出的采矿区3D变形监测方法具有一定的工程应用价值。

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