The topic of ground movements in Germany has been studied extensively in the past,especially in the field of active mines.The active hard coal mines in Germany were finally shut down in 2018 and lignite mining is expe...The topic of ground movements in Germany has been studied extensively in the past,especially in the field of active mines.The active hard coal mines in Germany were finally shut down in 2018 and lignite mining is expected to take place only until 2038.The so-called long-term liabilities of the mine operators in Germany include,among other things,the long-term guarantee of stability and thus the monitoring of ground motion.So far,the economic use of underground mining in Germany was mainly the supply of raw materials.In the future,the underground storage of compressed air,methane or hydrogen will play an important role in renewable energy supply and climate change.Therefore,the underground storage space will become more important and the spatial planning is essential to ensure availability of safe underground openings for the various options of environmentally friendly energy storage.However,this renewed usage of underground openings may also bring new and sometimes unknown challenges of geomechanical influence.The aftermath of hard coal and lignite mining will be an increasing challenge in mining subsidence engineering.On the other hand,new possibilities due to underground spatial planning may lead to subsidence and/or heaving of the upper surface.展开更多
Due to the rapid growth of the mining sector of Mongolia, the need for preparing mining surveying specialists is increasing significantly. The history of preparing highly educated mining surveying specialists and putt...Due to the rapid growth of the mining sector of Mongolia, the need for preparing mining surveying specialists is increasing significantly. The history of preparing highly educated mining surveying specialists and putting their education into practice in our country is an interesting one. The main center to prepare mining surveying specialists is the School of Mining Engineering of the Mongolian State University of Science and Technology. This paper introduces the work that is being done today to prepare mining surveying specialists in Mongolia and its future purposes.展开更多
This study aims to predict the migration time of toxic fumes induced by excavation blasting in underground mines.To reduce numerical simulation time and optimize ventilation design,several back propagation neural netw...This study aims to predict the migration time of toxic fumes induced by excavation blasting in underground mines.To reduce numerical simulation time and optimize ventilation design,several back propagation neural network(BPNN)models optimized by honey badger algorithm(HBA)with four chaos mapping(CM)functions(i.e.,Chebyshev(Che)map,Circle(Cir)map,Logistic(Log)map,and Piecewise(Pie)map)are developed to predict the migration time.125 simulations by the computational fluid dynamics(CFD)method are used to train and test the developed models.The determination coefficient(R2),the variance accounted for(VAF),the Willmott’s index(WI),the root mean square error(RMSE),the mean absolute percentage error(MAPE),and the sum of squares error(SSE)are utilized to evaluate the model performance.The evaluation results indicate that the CirHBA-BPNN model has achieved the most satisfactory performance by reaching the highest values of R2(0.9945),WI(0.9986),VAF(99.4811%),and the lowest values of RMSE(15.7600),MAPE(0.0343)and SSE(6209.4),respectively.The wind velocity in roadway(Wv)is the most important feature for predicting the migration time of toxic fumes.Furthermore,the intrinsic response characteristic of the optimal model is implemented to enhance the model interpretability and provide reference for the relationship between features and migration time of toxic fumes in ventilation design.展开更多
矿井作业环境复杂,各类地质灾害以及水害极易影响井下安全生产,因而预先对灾害发生时的人员逃生路径进行规划很有必要。为获取矿井最短逃生路线,提出了一种改进灰狼优化算法的路径规划方法。该方法针对灰狼优化算法(Grey Wolf Optimizat...矿井作业环境复杂,各类地质灾害以及水害极易影响井下安全生产,因而预先对灾害发生时的人员逃生路径进行规划很有必要。为获取矿井最短逃生路线,提出了一种改进灰狼优化算法的路径规划方法。该方法针对灰狼优化算法(Grey Wolf Optimization,GWO)早熟收敛和易陷入局部最优解的不足,提出了一种基于Logistic映射和Tent映射组合的改进灰狼算法(LT-GWO),提高其全局搜索能力。结合矿井实际工作环境,将改进算法应用于井下逃生路径规划,并通过设定合理路径约束和限制条件,获得了较好的路径规划结果。研究表明:所提算法在平均路径长度、路径长度标准差、平均迭代次数和平均寻优耗时等指标上显著优于已有算法,并且具有较好的鲁棒性。所提算法对于矿井灾害等应急场景下的路径规划问题研究有一定的参考价值。展开更多
环境感知与地下空间导航是煤矿智能化信息领域的重要研究方向,对实现无人化、全自动化、智能化的煤矿生产作业至关重要。随着第五代移动通信技术(5th generation mobile networks,5G)和毫米波成像雷达软硬件日益紧密结合与成熟,毫米波...环境感知与地下空间导航是煤矿智能化信息领域的重要研究方向,对实现无人化、全自动化、智能化的煤矿生产作业至关重要。随着第五代移动通信技术(5th generation mobile networks,5G)和毫米波成像雷达软硬件日益紧密结合与成熟,毫米波探测与通讯应用到更多领域。5G通讯技术依托高速率、低延时、高带宽的特点给现有的无线电通讯技术带来巨大的变革;同时,毫米波雷达相比激光雷达,低成本、抗干扰、三维点云(3 dimension point cloud,3D)数量相对激光点云数量少1~2个数量级的特点,使得其在地下环境3D成像及同步定位与地图构建(Simultaneous Localization and Mapping,SLAM)领域得到越来越多的关注。基于5G通讯的V2X(Vehicle to Everything)技术结合毫米波SLAM导航,为煤矿机器人的自主导航提供新的解决方案。系统综述了当下煤矿机器人自主导航以及实现煤矿智能化所面临的问题;近期国内外毫米波成像最新进展;地下环境毫米波雷达模块组通讯与信号获取方法;高分辨率成像遇到的稀疏特征提取问题;稀疏点云的处理策略与算法评估;深度学习在毫米波稀疏点云处理中的研究现状与发展方向;SLAM算法应用于不同环境的研究现状及SLAM导航算法。归纳了煤矿地下环境中应用SLAM地图构建、路径规划及避障的困难和挑战,并对未来煤矿复杂环境下毫米波通讯与导航兼容并蓄的新应用提出了展望。展开更多
煤矿智能化是煤炭行业高质量发展的技术支撑,关键岗位的机器人替代是实现煤炭少人化、无人化的高效开采的发展趋势。即时定位与地图构建(Simultaneous Localization and Mapping,SLAM)是煤矿机器人自主移动与导航的关键技术之一。煤矿...煤矿智能化是煤炭行业高质量发展的技术支撑,关键岗位的机器人替代是实现煤炭少人化、无人化的高效开采的发展趋势。即时定位与地图构建(Simultaneous Localization and Mapping,SLAM)是煤矿机器人自主移动与导航的关键技术之一。煤矿井下为典型非结构化环境,空间狭长局促,结构复杂多变,照明情况不均匀,对煤矿井下SLAM提出了严峻挑战。总结了煤矿井下地图构建研究现状,针对LeGO-LOAM算法的回环检测仍存在的不足,利用SegMatch算法改进LeGO-LOAM的回环检测模块,且使用ICP算法进行全局图优化,提出了一种融合LeGO-LOAM和SegMatch的改进算法,阐述了该算法的原理和实现步骤;开展了煤矿井下模拟场景试验,对比分析改进前后SLAM算法的建图效果以及精度,试验结果表明改进算法构建的地图回环效果更好,估计轨迹更平滑、精确;结合导航需求研究了二维占据栅格地图的构建方法,试验验证了该方法所构建的栅格地图精度,结果表明有效滤除动态障碍物等离群噪点后的栅格地图具有0.01 m的建图精度,且所需存储空间较点云地图降低了3个数量级。研究成果有助于煤矿井下非结构环境下SLAM和煤矿机器人实时定位和自主移动。展开更多
文摘The topic of ground movements in Germany has been studied extensively in the past,especially in the field of active mines.The active hard coal mines in Germany were finally shut down in 2018 and lignite mining is expected to take place only until 2038.The so-called long-term liabilities of the mine operators in Germany include,among other things,the long-term guarantee of stability and thus the monitoring of ground motion.So far,the economic use of underground mining in Germany was mainly the supply of raw materials.In the future,the underground storage of compressed air,methane or hydrogen will play an important role in renewable energy supply and climate change.Therefore,the underground storage space will become more important and the spatial planning is essential to ensure availability of safe underground openings for the various options of environmentally friendly energy storage.However,this renewed usage of underground openings may also bring new and sometimes unknown challenges of geomechanical influence.The aftermath of hard coal and lignite mining will be an increasing challenge in mining subsidence engineering.On the other hand,new possibilities due to underground spatial planning may lead to subsidence and/or heaving of the upper surface.
文摘Due to the rapid growth of the mining sector of Mongolia, the need for preparing mining surveying specialists is increasing significantly. The history of preparing highly educated mining surveying specialists and putting their education into practice in our country is an interesting one. The main center to prepare mining surveying specialists is the School of Mining Engineering of the Mongolian State University of Science and Technology. This paper introduces the work that is being done today to prepare mining surveying specialists in Mongolia and its future purposes.
基金The authors were funded by China Scholarship Council(Grant Nos.202106370038,and 201906690049)National Key Research and Development Program of China(Grant No.2021YFC3001300).
文摘This study aims to predict the migration time of toxic fumes induced by excavation blasting in underground mines.To reduce numerical simulation time and optimize ventilation design,several back propagation neural network(BPNN)models optimized by honey badger algorithm(HBA)with four chaos mapping(CM)functions(i.e.,Chebyshev(Che)map,Circle(Cir)map,Logistic(Log)map,and Piecewise(Pie)map)are developed to predict the migration time.125 simulations by the computational fluid dynamics(CFD)method are used to train and test the developed models.The determination coefficient(R2),the variance accounted for(VAF),the Willmott’s index(WI),the root mean square error(RMSE),the mean absolute percentage error(MAPE),and the sum of squares error(SSE)are utilized to evaluate the model performance.The evaluation results indicate that the CirHBA-BPNN model has achieved the most satisfactory performance by reaching the highest values of R2(0.9945),WI(0.9986),VAF(99.4811%),and the lowest values of RMSE(15.7600),MAPE(0.0343)and SSE(6209.4),respectively.The wind velocity in roadway(Wv)is the most important feature for predicting the migration time of toxic fumes.Furthermore,the intrinsic response characteristic of the optimal model is implemented to enhance the model interpretability and provide reference for the relationship between features and migration time of toxic fumes in ventilation design.
文摘矿井作业环境复杂,各类地质灾害以及水害极易影响井下安全生产,因而预先对灾害发生时的人员逃生路径进行规划很有必要。为获取矿井最短逃生路线,提出了一种改进灰狼优化算法的路径规划方法。该方法针对灰狼优化算法(Grey Wolf Optimization,GWO)早熟收敛和易陷入局部最优解的不足,提出了一种基于Logistic映射和Tent映射组合的改进灰狼算法(LT-GWO),提高其全局搜索能力。结合矿井实际工作环境,将改进算法应用于井下逃生路径规划,并通过设定合理路径约束和限制条件,获得了较好的路径规划结果。研究表明:所提算法在平均路径长度、路径长度标准差、平均迭代次数和平均寻优耗时等指标上显著优于已有算法,并且具有较好的鲁棒性。所提算法对于矿井灾害等应急场景下的路径规划问题研究有一定的参考价值。
文摘环境感知与地下空间导航是煤矿智能化信息领域的重要研究方向,对实现无人化、全自动化、智能化的煤矿生产作业至关重要。随着第五代移动通信技术(5th generation mobile networks,5G)和毫米波成像雷达软硬件日益紧密结合与成熟,毫米波探测与通讯应用到更多领域。5G通讯技术依托高速率、低延时、高带宽的特点给现有的无线电通讯技术带来巨大的变革;同时,毫米波雷达相比激光雷达,低成本、抗干扰、三维点云(3 dimension point cloud,3D)数量相对激光点云数量少1~2个数量级的特点,使得其在地下环境3D成像及同步定位与地图构建(Simultaneous Localization and Mapping,SLAM)领域得到越来越多的关注。基于5G通讯的V2X(Vehicle to Everything)技术结合毫米波SLAM导航,为煤矿机器人的自主导航提供新的解决方案。系统综述了当下煤矿机器人自主导航以及实现煤矿智能化所面临的问题;近期国内外毫米波成像最新进展;地下环境毫米波雷达模块组通讯与信号获取方法;高分辨率成像遇到的稀疏特征提取问题;稀疏点云的处理策略与算法评估;深度学习在毫米波稀疏点云处理中的研究现状与发展方向;SLAM算法应用于不同环境的研究现状及SLAM导航算法。归纳了煤矿地下环境中应用SLAM地图构建、路径规划及避障的困难和挑战,并对未来煤矿复杂环境下毫米波通讯与导航兼容并蓄的新应用提出了展望。
文摘煤矿智能化是煤炭行业高质量发展的技术支撑,关键岗位的机器人替代是实现煤炭少人化、无人化的高效开采的发展趋势。即时定位与地图构建(Simultaneous Localization and Mapping,SLAM)是煤矿机器人自主移动与导航的关键技术之一。煤矿井下为典型非结构化环境,空间狭长局促,结构复杂多变,照明情况不均匀,对煤矿井下SLAM提出了严峻挑战。总结了煤矿井下地图构建研究现状,针对LeGO-LOAM算法的回环检测仍存在的不足,利用SegMatch算法改进LeGO-LOAM的回环检测模块,且使用ICP算法进行全局图优化,提出了一种融合LeGO-LOAM和SegMatch的改进算法,阐述了该算法的原理和实现步骤;开展了煤矿井下模拟场景试验,对比分析改进前后SLAM算法的建图效果以及精度,试验结果表明改进算法构建的地图回环效果更好,估计轨迹更平滑、精确;结合导航需求研究了二维占据栅格地图的构建方法,试验验证了该方法所构建的栅格地图精度,结果表明有效滤除动态障碍物等离群噪点后的栅格地图具有0.01 m的建图精度,且所需存储空间较点云地图降低了3个数量级。研究成果有助于煤矿井下非结构环境下SLAM和煤矿机器人实时定位和自主移动。
