Research and practice on key technologies for intelligentization of coal mine

Authors

WANG Haijun, CCTEG Chinese Institute of Coal Science, Beijing 100013, China; CCTEG Xi’an Research Institute (Group) Co., Ltd., Xi’an 710077, ChinaFollow
CAO Yun, CCTEG Chinese Institute of Coal Science, Beijing 100013, China; CCTEG Xi’an Research Institute (Group) Co., Ltd., Xi’an 710077, China
WANG Honglei, CCTEG Chinese Institute of Coal Science, Beijing 100013, China; CCTEG Xi’an Research Institute (Group) Co., Ltd., Xi’an 710077, ChinaFollow

Abstract

Intelligentization of coal mine is an important way for coal industry to achieve sustainable development. It provides effective guarantee for coal mine enterprises to reduce workers, increase efficiency and improve the production safety. The new generation of information technologies, such as big data, robotics and artificial intelligence, strongly support the intelligent construction of coal mines. Firstly, the design ideas of intelligent mines are introduced in this paper, and the overall technical system of intelligent construction are developed. Secondly, the development status and trend of key technologies for the intelligentization of coal mine are analyzed, including the general heterogeneous control and data processing platform, mining inspection robot technology, artificial intelligence technology, fault diagnosis technology and intelligent wearable technology. Meanwhile, problems in the process of intelligent construction and application were combed through. In addition, solutions for the bottleneck problems and future development trend are pointed out. Besides, general heterogeneous control and data processing platform based on modular are developed, realizing the reconfigurability of underlying terminals and the unification of basic communication protocols. Thus, the problem of data islands is solved. Key technologies of the monitoring, motion control and precise positioning of inspection robot drive module are also studied to realize the precise positioning of robot based on the multi-sensor fusion technology. Problems in the application of artificial intelligence technologies in coal mine scenarios, such as lack of typical data and combination with scene knowledge, are also studied. It is shown that the few-shot learning technology is expected to promote further implementation and application of artificial intelligence technologies in the intelligent field of coal mine. The fault diagnosis technology of coal mine equipment is studied, and it is proposed that construction of equipment fault diagnosis model based on data and knowledge hybrid drive can effectively solve the problem of over-maintenance and under-maintenance. Moreover, intelligent wearable technology is the key to solve the problem of protecting workers in key underground positions, and the design scheme is also provided for the intelligent wearable system composed of respiratory system, sensor monitoring system, human-computer interaction system and voice display system. Finally, practical experience and phased achievements of intelligent construction of Huoluowan Coal Mine and Wujiata Open-pit Coal Mine of Shendong Tianlong Group are introduced, providing reference for intelligent construction of different types of coal mines.

Keywords

data processing, coal mine robot, artificial intelligence, intelligentization of coal mine, intelligent coal mine

DOI

10.12363/issn.1001-1986.22.12.0992

Recommended Citation

WANG Haijun, CAO Yun, WANG Honglei, et al. (2023) "Research and practice on key technologies for intelligentization of coal mine," Coal Geology & Exploration: Vol. 51: Iss. 1, Article 10.
DOI: 10.12363/issn.1001-1986.22.12.0992
Available at: https://cge.researchcommons.org/journal/vol51/iss1/10

Reference

[1] 康红普,王国法,王双明,等. 煤炭行业高质量发展研究[J]. 中国工程科学,2021,23(5):130−138.

KANG Hongpu,WANG Guofa,WANG Shuangming,et al. High–quality development of China’s coal industry[J]. Strategic Study of Chinese Academy of Engineering,2021,23(5):130−138.

[2] 张幼振,刘焱杰,钟自成. 预钻式原位岩体剪切测量系统研制与试验分析[J]. 煤田地质与勘探,2022,50(2):1−7.

ZHANG Youzhen,LIU Yanjie,ZHONG Zicheng. Development and test analysis of borehole in-situ rock mass shear measurement system[J]. Coal Geology & Exploration,2022,50(2):1−7.

[3] 王国法,庞义辉,任怀伟. 智慧矿山技术体系研究与发展路径[J]. 金属矿山,2022(5):1−9.

WANG Guofa,PANG Yihui,REN Huaiwei. Research and development path of smart mine technology system[J]. Metal Mine,2022(5):1−9.

[4] 王国法. 加快煤矿智能化建设 推进煤炭行业高质量发展[J]. 中国煤炭,2021,47(1):2−10.

WANG Guofa. Speeding up intelligent construction of coal mine and promoting high−quality development of coal industry[J]. China Coal,2021,47(1):2−10.

[5] 李泉新,刘飞,方俊,等. 我国煤矿井下智能化钻探技术装备发展与展望[J]. 煤田地质与勘探,2021,49(6):265−272.

LI Quanxin,LIU Fei,FANG Jun,et al. Development and prospect of intelligent drilling technology and equipment for underground coal mines in China[J]. Coal Geology & Exploration,2021,49(6):265−272.

[6] 张建明,曹文君,王景阳,等. 智能化煤矿信息基础设施标准体系研究[J]. 中国煤炭,2021,47(11):1−6.

ZHANG Jianming,CAO Wenjun,WANG Jingyang,et al. Research on information infrastructure standard system for intelligent coal mine[J]. China Coal,2021,47(11):1−6.

[7] 范京道,封华,宋朝阳,等. 可可盖煤矿全矿井机械破岩智能化建井关键技术与装备[J]. 煤炭学报,2022,47(1):499−514.

FAN Jingdao,FENG Hua,SONG Zhaoyang,et al. Key technology and equipment for intelligent mine construction of whole mine mechanical rock breaking in Kekegai Coal Mine[J]. Journal of China Coal Society,2022,47(1):499−514.

[8] 刘永宏，冀浩楠. 锻造精细化管理引擎 推动安全高效发展：陕西小保当矿业有限公司安全生产工作侧记[N]. 中国煤炭报，2022-12-03(004).

[9] 王雷,朱玉芹,张维娜,等. 煤矿大直径救援钻孔顶管钻进装备关键技术研究[J]. 煤田地质与勘探,2022,50(11):58−66.

WANG Lei,ZHU Yuqin,ZHANG Weina,et al. Research on key technology of pipe jacking drilling equipment for large–diameter rescue borehole of coal mine[J]. Coal Geology & Exploration,2022,50(11):58−66.

[10] 张良，王进军，张龙涛，等. 数字化新型管理助推企业高质量发展[C]//中国企业改革发展优秀成果2021(第五届)下卷，2020：1127–1139.

[11] 王国法,庞义辉,李爽,等. 基于煤矿时空多源信息感知的智能安控闭环体系[J]. 矿业安全与环保,2022,49(4):1−11.

WANG Guofa,PANG Yihui,LI Shuang,et al. Intelligent safety closed−loop management and control system based on multi−source information perception in coal mine[J]. Mining Safety & Environmental Protection,2022,49(4):1−11.

[12] 王苏君,平一帆,文伟,等. 宽频段一体化硬件处理平台设计[J]. 空间电子技术,2022,19(2):73−77.

WANG Sujun,PING Yifan,WEN Wei,et al. Design of broadband integrated hardware processing platform[J]. Space Electronic Technology,2022,19(2):73−77.

[13] MOHAMED K，MOHAMED O. Parallel computing in heterogeneous machines based on the CPU donation approach[C]//2017 First International Conference on Embedded & Distributed Systems (EDiS)，2017：1–6.

[14] KHOKHAR A A,PRASANNA V K,SHAABAN M E,et al. Heterogeneous computing:Challenges and opportunities[J]. IEEE Computer,1993,26(6):18−27.

[15] 仵金刚. 多接口通用协议解析方法研究与实现[D]. 西安：西安科技大学，2019.

WU Jingang. Research and implementation of multi–interface universal protocol resolution method[D]. Xi’ an：Xi’ an University of Science and Technology，2019.

[16] 葛世荣,胡而已,裴文良. 煤矿机器人体系及关键技术[J]. 煤炭学报,2020,45(1):455−463.

GE Shirong,HU Eryi,PEI Wenliang. Classification system and key technology of coal mine robot[J]. Journal of China Coal Society,2020,45(1):455−463.

[17] KATZ B G. A low cost modular actuator for dynamic robots[D]. Boston：Massachusetts Institute of Technology，2018.

[18] HWANGBO J,LEE J,DOSOVITSKIY A,et al. Learning agile and dynamic motor skills for legged robots[J]. Science Robotics,2019,4(26):eaau5872.

[19] 闫曈,许威,苏波. 基于ZMP的四足仿生机器人反应式行为控制策略研究[J]. 车辆与动力技术,2021(1):1−7.

YAN Tong,XU Wei,SU Bo. Research on reactive behavior control strategy of quadruped bionic robot based on ZMP[J]. Vehicle & Power Technology,2021(1):1−7.

[20] 魏扬帆,周川,郭健,等. 基于CPG的四足机器人坡面稳定行走控制研究[J]. 控制工程,2021,28(6):1055−1060.

WEI Yangfan,ZHOU Chuan,GUO Jian,et al. CPG–based stable walking control of the quadruped robot on the slope[J]. Control Engineering of China,2021,28(6):1055−1060.

[21] CARLO J D，WENSING P，KATZ B，et al. Dynamic locomotion in the MIT cheetah 3 through convex model–predictive control[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)，2018：1–9.

[22] 刘伟龙,李彬,侯兰东,等. 基于深度强化学习的四足机器人研究综述[J]. 齐鲁工业大学学报,2022,36(2):67−74.

LIU Weilong,LI Bin,HOU Landong,et al. Review of quadruped robot research based on deep reinforcement learning[J]. Journal of Qilu University of Technology,2022,36(2):67−74.

[23] 张征. 基于多传感器数据融合的煤矿井下移动机器人精确定位技术研究[D]. 徐州：中国矿业大学，2021.

ZHANG Zheng. Research on accurate positioning technology of mobile robot in coal mine based on multi–sensor data fusion[D]. Xuzhou：China University of Mining and Technology，2021.

[24] 张钹,朱军,苏航. 迈向第三代人工智能[J]. 中国科学:信息科学,2020,50(9):1281−1302.

ZHANG Bo,ZHU Jun,SU Hang. Toward the third generation of artificial intelligence[J]. Scientia Sinica Informationis,2020,50(9):1281−1302.

[25] 王国法,任怀伟,赵国瑞,等. 智能化煤矿数据模型及复杂巨系统耦合技术体系[J]. 煤炭学报,2022,47(1):61−74.

WANG Guofa,REN Huaiwei,ZHAO Guorui,et al. Digital model and giant system coupling technology system of smart coal mine[J]. Journal of China Coal Society,2022,47(1):61−74.

[26] 王斌,梁馨月. 某煤矿井下作业面职业病危害因素与防护设施[J]. 中国卫生工程学,2022,21(1):35−36.

WANG Bin,LIANG Xinyue. Detection of occupational hazards and analysis of protective facilities in a coal mine[J]. Chinese Journal of Public Health Engineering,2022,21(1):35−36.

[27] 陈晓川,卞显力,韩森. 碳纤维经编针织复合材料钻孔过程有限元建模与实验研究[J]. 机械设计与制造,2021(9):247−250.

CHEN Xiaochuan,BIAN Xianli,HAN Sen. Finite element modeling and experimental study on drilling process of carbon fiber warp knitted composites[J]. Machinery Design & Manufacture,2021(9):247−250.

[28] 邱靖斯,葛烨倩. 三大高性能纤维纺织品民用化推广的研究进展[J]. 现代纺织技术,2021,29(6):1−6.

QIU Jingsi,GE Yeqian. Research progress of top three high–performing fibers and textiles for civil use[J]. Advanced Textile Technology,2021,29(6):1−6.

[29] 罗朋,王晓波,巩春志,等. 磁控溅射制备高熵合金薄膜研究进展[J]. 中国表面工程,2021,34(5):53−66.

LUO Peng,WANG Xiaobo,GONG Chunzhi,et al. Research progress of high entropy alloy thin films prepared by magnetron sputtering[J]. China Surface Engineering,2021,34(5):53−66.

[30] 夏珊. 导电高分子水凝胶的制备及其在可穿戴传感器的应用研究[D]. 长春：长春工业大学，2020.

XIA Shan. Preparation of conductive polymer hydrogels and their application research in wearable sensors[D]. Changchun：Changchun University of Technology，2020.

[31] 韩昌报,王嫚琪,黄建华,等. 摩擦纳米发电技术研究进展及其潜在应用[J]. 北京工业大学学报,2020,46(10):1103−1127.

HAN Changbao,WANG Manqi,HUANG Jianhua,et al. Research progress of triboelectric generator and its potential application[J]. Journal of Beijing University of Technology,2020,46(10):1103−1127.

[32] 马金林,巩元文,马自萍,等. 唇语识别的视觉特征提取方法综述[J]. 计算机科学与探索,2021,15(12):2256−2275.

MA Jinlin,GONG Yuanwen,MA Ziping,et al. Review of extracting methods for lip visual features[J]. Journal of Frontiers of Computer Science and Technology,2021,15(12):2256−2275.