Hao Xuedi, Zhang Jiajin, Wen Rusen, Gao Chuan, Xu Xianlei, Ge Shirong, Zhang Yiming, Wang Shuyang
School of Mechanical and Electronical Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
Key Laboratory of Coal Mine Intelligence and Robot Innovation Application Emergency Management Department, China University of Mining and Technology (Beijing), Beijing 100083, China.
Sensors (Basel). 2024 Sep 5;24(17):5766. doi: 10.3390/s24175766.
Accurately obtaining the geological characteristic digital model of a coal seam and surrounding rock in front of a fully mechanized mining face is one of the key technologies for automatic and continuous coal mining operation to realize an intelligent unmanned working face. The research on how to establish accurate and reliable coal seam digital models is a hot topic and technical bottleneck in the field of intelligent coal mining. This paper puts forward a construction method and dynamic update mechanism for a digital model of coal seam autonomous cutting by a coal mining machine, and verifies its effectiveness in experiments. Based on the interpolation model of drilling data, a fine coal seam digital model was established according to the results of geological statistical inversion, which overcomes the shortcomings of an insufficient lateral resolution of lithology and physical properties in a traditional geological model and can accurately depict the distribution trend of coal seams. By utilizing the numerical derivation of surrounding rock mining and geological SLAM advanced exploration, the coal seam digital model was modified to achieve a dynamic updating and optimization of the model, providing an accurate geological information guarantee for intelligent unmanned coal mining. Based on the model, it is possible to obtain the boundary and inclination information of the coal seam profile, and provide strategies for adjusting the height of the coal mining machine drum at the current position, achieving precise control of the automatic height adjustment of the coal mining machine.
准确获取综采工作面煤壁前方煤层及围岩地质特征数字模型,是实现智能无人工作面煤炭自动化连续开采作业的关键技术之一。如何建立准确可靠的煤层数字模型的研究,是智能采煤领域的热点和技术瓶颈。本文提出了一种采煤机自主截割煤层数字模型的构建方法及动态更新机制,并通过实验验证了其有效性。基于钻孔数据插值模型,根据地质统计反演结果建立了精细煤层数字模型,克服了传统地质模型横向岩性和物性分辨率不足的缺点,能准确描绘煤层分布趋势。利用围岩开采数值推导和地质SLAM先进探测技术,对煤层数字模型进行修正,实现模型动态更新与优化,为智能无人采煤提供准确地质信息保障。基于该模型,可获取煤层剖面边界及倾角信息,为当前位置采煤机滚筒调高提供调整策略,实现采煤机自动调高精确控制。