Research on 3D modeling of fully mechanized mining face based on UPSO-Kriging
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摘要: 基于传统地质统计学的综采工作面煤层赋存形态三维建模的基础是Kriging插值算法,但其选择并拟合的变差函数模型并不能较好地反映实际的地质特征与空间数据的变化趋势。对此,本文提出UPSO-Kriging插值法:针对PSO算法存在的收敛速度慢、易陷入局部解等问题,优化算法,将优化的PSO算法(UPSO)引入Kriging插值中求解变差参数,拟合出变差函数模型,实现工作面煤层结构中各层的高程值预测;在此基础上,基于规则网格法建立DEM数字高程模型,运用Three.js实现了综采工作面煤层赋存形态三维可视化,可为煤炭企业的透明、智能、分质开采提供科学依据。Abstract: Kriging interpolation algorithm is the basis of 3D modeling of coal seam occurrence form in fully mechanized mining face based on traditional geostatistics. However, the variation function model selected and fitted by Kriging interpolation algorithm cannot reflect the actual geological characteristics and the variation trend of spatial data. In this paper, UPSO-Kriging interpolation method is thus proposed: PSO algorithm is optimized to solve the problems of slow convergence and easily falling into local solution, and the optimized algorithm UPSO is then introduced into Kriging interpolation to solve the variation parameters and fit the variation function model, thus realizing the height prediction of each layer in the coal seam structure of working face. In addition, with DEM model established on regular grid method, the three-dimensional visualization of the occurrence form of coal seam in fully mechanized mining face is realized by using Three.js, which provides scientific basis for transparent mining, intelligent mining and quality mining of coal enterprises.
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Key words:
- Kriging interpolation /
- variation function /
- PSO /
- regular grid /
- 3D visualization
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[1] 范文遥, 曹梦雪, 路来君. 基于GOCAD软件的三维地质建模可视化过程[J]. 科学技术与工程, 2020,20(24):9771-9778.
[2] Fan W Y, Cao M X, Lu L J. Visualization process of 3D geological modeling based on GOCAD software[J]. Science Technology and Engineering, 2020,20(24):9771-9778.
[3] 尚福华, 杨彦彬, 杜睿山. 基于TIN-Octree的三维地质模型构建方法研究[J]. 计算技术与自动化, 2019,38(4):121-125.
[4] Shang F H, Yang Y B, Du R S. Research on construction method of 3D geological model based on TIN-Octree[J]. Computer Technology and Automation, 2019,38(4):121-125.
[5] 宋越, 高振记. 煤系地层三维地质模型精细化表达研究[J]. 中国矿业杂志, 2020,29(9):147-151, 159.
[6] Song Y, Gao Z J. Research on expression of three-dimensional geological model of coal measure strata[J]. China Mining Magazine, 2020,29(9):147-151, 159.
[7] 高辰飞. 基于WebGL的海洋样品三维可视化的研究[D]. 青岛:中国海洋大学, 2014.
[8] Gao C F. Research of ocean sample 3D visualization based on WebGL[D]. Qingdao:Ocean University of China, 2014.
[9] 牛志宏. 基于Kriging估计的GPS高程拟合对比研究[J]. 浙江水利科技, 2014,42(1):52-55.
[10] Niu Z H. Comparative study on GPS height fitting models based on kriging estimation[J]. Zhejiang Hydrotechnics, 2014,42(1):52-55.
[11] 王炯辉, 李毅, 黄冬梅, 等. 基于普通克里格法的泥河铁矿床资源储量估算研究[J]. 地质与勘探, 2013,49(6):1108-1113.
[12] Wang J H, Li Y, Huang D M, et al. Reserves estimation of the Nihe iron deposit in Anhui based on ordinary kriging[J]. Geology and Exploration, 2013,49(6):1108-1113.
[13] 刘夏, 莫树培. 改进克里金插值算法的井下无线定位指纹库构建方法[J]. 传感技术学报, 2019,32(7):1100-1106.
[14] Liu X, Mo S P. Construction of underground wireless position fingerprint database based on improved kriging interpolation algorithms[J]. Chinese Journal of Sensors and Actuators, 2019,32(7):1100-1106.
[15] 朱军, 王跃, 卓杰. 基于粒子群算法的钢板倒垛优化[J]. 自动化与仪表, 2017,32(6):20-22,35.
[16] Zhu J, Wang Y, Zhuo J. Particle swarm optimization for steel plate dumping[J]. Automation & Instrumentation, 2017,32(6):20-22,35.
[17] 刘玉敏, 高松岩. 一种改进的粒子群优化算法及其算法测试[J]. 数学的实践与认识, 2019,49(9):237-247.
[18] Liu Y M, Gao S Y. An improved particle swarm optimization algorithm and algorithm testing[J]. Journal of Mathematics in Practice and Theory, 2019,49(9):237-247.
[19] 侯丽娟, 李蜀瑜. 基于混合PSO算法的语义Web服务发现[J]. 计算机工程, 2011,37(3):195-197.
[20] Hou L J, Li S Y. Semantic web service discovery based on hybrid PSO algorithm[J]. Computer Engineering, 2011,37(3):195-197.
[21] 宋文文, 王珺, 杜晔, 等. 基于粒子群优化的数据中心负载均衡机制[J]. 南京邮电大学学报:自然科学版, 2019,39(5):81-88.
[22] Song W W, Wang J, Du Y, et al. Load balancing technology for data center based on particle swarm optimization[J]. Journal of Nanjing University of Posts and Telecommunications:Natural Science Edition, 2019,39(5):81-88.
[23] 舒宗玉. 基于多目标混合粒子群算法的无人船全局路径规划[D]. 武汉:武汉理工大学, 2017.
[24] Shu Z Y. Global path planning of unmanned surface vessel based on multi-objective hybrid particle swarm algorithm[D]. Wuhan:Wuhan University of Technology, 2017.
[25] 王育平, 亓呈明. 改进的蚁群算法求解连续性空间优化问题[J]. 辽宁工程技术大学学报:自然科学版, 2010,29(5):903-906.
[26] Wang Y P, Qi C M. A randomized ant colony algorithm for continuous function optimization[J]. Journal of Liaoning Technical University Natural Science:Natural Science Edition, 2010,29(5):903-906.
[27] 张小艳, 许慧. 基于改进粒子群算法的气化配煤模型求解[J]. 煤炭技术, 2021,40(2):196-199.
[28] Zhang X Y, Xu H. The solution of gasification coal blending model based on improved particle swarm optimization algorithm[J]. Coal Technology, 2021,40(2):196-199.
[29] 颜学峰, 余娟, 钱锋, 等. 基改进差分进化算法的超临界水氧化动力学参数估计[J]. 华东理工大学学报:自然科学版, 2006,32(1):94-97,124.
[30] Yan X F, Yu J, Qian F, et al. Kinetic parameter estimation of oxidation in supercritical water based on modified different evolution[J]. Journal of East China University of Science and Technology:Natural Science Edition, 2006,32(1):94-97,124.
[31] 贾雨, 邓世武, 姚兴苗, 等. 基于约束粒子群优化的克里金插值算法[J]. 成都理工大学学报:自然科学版, 2015,42(1):104-109.
[32] Jia Y, Deng S W, Yao X M, et al. Kriging interpolation algorithm based on constraint particle swarm optimization[J]. Journal of Chengdu University of Technology:Natural Science Edition, 2015,42(1):104-109.
[33] George Y L, David W W. An adaptive inverse-distance weighting spatial interpolation technique[J]. Computers and Geosciences, 2007,34(9):1044-1055.
[34] 陆潮. 基于three.js的在线3D室内设计系统的设计与实现[D]. 武汉:华中科技大学, 2018.
[35] Lu C. Design and implementation of online 3D interior design system based on three.js[D]. Wuhan:Huazhong University of Science and Technology, 2018.
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