平行矿山: 从数字孪生到矿山智能

陈龙; 王晓; 杨健健; 艾云峰; 田滨; 李宇宸; 滕思宇; 王健; 曹东璞; 葛世荣; 王飞跃

doi:10.16383/j.aas.2021.y000001

平行矿山: 从数字孪生到矿山智能

doi: 10.16383/j.aas.2021.y000001

陈龙^{1, 2, 3,},
王晓^{1, 3, 4,},
杨健健^5,,
艾云峰^{6, 7,},
田滨^{1, 3,},
李宇宸^3,,
滕思宇^3,,
王健^{3, 7,},
曹东璞^{3, 8,},
葛世荣^5,,
王飞跃^{1, 4,}

1.
中国科学院自动化研究所复杂系统管理与控制国家重点实验室北京 100190 中国
2.
中山大学计算机学院广州 510006 中国
3.
青岛慧拓智能机器有限公司青岛 266109 中国
4.
青岛智能产业技术研究院青岛 266109 中国
5.
中国矿业大学(北京) 北京 100083 中国
6.
中国科学院大学人工智能学院北京 100049 中国
7.
吉林大学计算机科学与技术学院长春 130012 中国
8.
滑铁卢大学机械与机电工程系滑铁卢 ON N2L 3G1 加拿大

基金项目: 广东省重点领域研发计划(2020B090921003), 英特尔智能网联汽车大学合作研究中心项目(“ICRI-IACV”)资助

详细信息

作者简介:
陈龙：中山大学计算机学院副教授. 2013年获得武汉大学博士学位. 主要研究方向为自动驾驶, 机器人. E-mail: chenl46@mail.sysu.edu.cn

王晓：中国科学院自动化研究所复杂系统管理与控制国家重点实验室副研究员, 青岛智能产业技术研究院院长. 2016 年获得中国科学院大学社会计算博士学位. 主要研究方向为社会交通, 动态网群组织, 平行智能和社交网络分析. E-mail: x.wang@ia.ac.cn

杨健健：中国矿业大学(北京)机电与信息工程学院副教授. 2013年获得中国矿业大学(北京)博士学位. 主要研究方向为煤矿机器人与智能装备, 智能监测与控制, 无线传感器及机器智能, 物联网技术及数据挖掘. E-mail: yangjiannedved@163.com

艾云峰：中国科学院大学副教授. 2006年获得中国科学院自动化研究所博士学位. 主要研究方向为视觉感知, 智能驾驶, 平行驾驶. E-mail: aiyunfeng@ucas.ac.cn

田滨：中国科学院自动化研究所复杂系统管理与控制国家重点实验室副研究员. 2014年获得中国科学院大学博士学位. 主要研究方向为自动驾驶, 视觉感知, 机器学习. E-mail: bin.tian@ia.ac.cn

李宇宸：2020年获得北京航空航天大学硕士学位. 主要研究方向为计算机视觉, 3D目标检测, 同步定位与地图构建, 无人驾驶. E-mail: liyuchen@buaa.edu.cn

滕思宇：2021年获得吉林大学硕士学位. 主要研究方向为路径规划, 模仿学习, 目标检测. E-mail: tengsyslash@gmail.com

王健：吉林大学教授. 2011年获得吉林大学博士学位. 主要研究方向为车联网, 虚拟仿真测试, 车路协同. E-mail: wangjian591@jlu.edu.cn

曹东璞：滑铁卢大学滑铁卢认知自动驾驶(CogDrive)实验室副教授和主任. 2008年获得康考迪亚大学博士学位. 主要研究方向为驾驶员认知, 自动驾驶和认知自动驾驶. E-mail: oscar_cao2016@163.com

葛世荣：中国矿业大学(北京)校长, 教授. 主要研究方向为智能采矿装备, 摩擦可靠性研究. 本文通信作者. E-mail: gesr@cumtb.edu.cn

王飞跃：中国科学院自动化研究所研究员, 复杂系统管理与控制国家重点实验室主任, 中国科学院大学中国经济与社会安全研究中心主任. 主要研究方向为平行系统的方法与应用, 社会计算, 平行智能以及知识自动化. E-mail: feiyue.wang@ia.ac.cn

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出版历程
- 收稿日期: 2020-11-08
- 录用日期: 2021-06-04
- 网络出版日期: 2021-06-17
- 刊出日期: 2021-07-27

Parallel Mining Operating Systems: From Digital Twins to Mining Intelligence

CHEN Long^{1, 2, 3
,},
WANG Xiao^{1, 3, 4
,},
YANG Jian-Jian^5
,,
AI Yun-Feng^{6, 7
,},
TIAN Bin^{1, 3
,},
LI Yu-Chen^3
,,
TENG Si-Yu^3
,,
WANG Jian^{3, 7
,},
CAO Dong-Pu^{3, 8
,},
Ge Shi-Rong^5
,,
WANG Fei-Yue^{1, 4
,}

1.
State Key Laboratory for Management and Control of Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.
School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China
3.
Vehicle Intelligence Pioneers, Inc., Qingdao 266109, China
4.
Qingdao Academy of Intelligent Industries, Qingdao 266109, China
5.
China University of Mining and Technology−Beijing, Beijing 100083, China
6.
School of Artificial Intelligence, University of Chinese Academy of Sciences Beijing 100049, China
7.
College of Computer Science and Technology, Jilin University, Changchun 130012, China
8.
Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo ON N2L 3G1, Canada

Funds: Supported by Key-Area Research and Development Program of Guangdong Province (2020B090921003) and Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (“ICRI-IACV”)

More Information

Author Bio:
CHEN Long　Associate professor at the School of Computer Science and Engineering, Sun Yat-sen University. He received his Ph.D. degree from Wuhan University in 2013. His research interest covers autonomous cars and robotics

WANG Xiao　Associate professor at the State Key Laboratory for Management and Control of Systems, Institute of Automation, Chinese Academy of Sciences, president of Qingdao Academy of Intelligent Industries. She received her Ph.D. degree in social computing from University of Chinese Academy of Sciences in 2016. Her research interest covers social transportation, cyber movement organizations, parallel intelligence, and social network analysis

YANG Jian-Jian　Associate professor at China University of Mining and Technology−Beijing. He received his Ph.D. degree from China University of Mining and Technology−Beijing in 2013. His research interest covers coal mine robot and intelligent equipment, intelligent monitoring and control, wireless sensor and machine intelligence, and internet of things technology and data mining

AI Yun-Feng　Associate professor at University of Chinese Academy of Sciences. He received his Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences. His current research interest covers computer vision, machine learning, robots, and automated driving

TIAN Bin　Associate professor at the State Key Laboratory for Management and Control of Systems, Institute of Automation, Chinese Academy of Sciences. He received his Ph.D. degree from University of Chinese Academy of Sciences in 2014. His research interest covers automated driving, vision sensing and perception, and machine learning

LI Yu-Chen　Received his master degree from Beihang University in 2020. His research interest covers computer vision, 3D object detection, simultaneous localization and mapping (SLAM), and autonomous driving

TENG Si-Yu　Received his master degree from Jilin University in 2021. His research interest covers path planning, imitation learning, and target detection

WANG Jian　Professor at Jilin University. He received his Ph.D. degree from Jilin University in 2011. His research interest covers internet of vehicles, simulation testing, and vehicle-road cooperation

CAO Dong-Pu　Associate professor and director of Waterloo Cognitive Autonomous Driving (CogDrive) Laboratory at University of Waterloo, Canada. He received his Ph.D. degree from Concordia University, Canada, in 2008. His research interest covers driver cognition, automated driving, and cognitive autonomous driving

GE Shi-Rong　President and professor at China University of Mining and Technology−Beijing. His research interest covers intelligent mining equipment and friction reliability. Corresponding author of this paper

WANG Fei-Yue　Professor at the Institute of Automation, Chinese Academy of Sciences, director of the State Key Laboratory for Management and Control of Systems, director of China Economic and Social Security Research Center at University of Chinese Academy of Sciences. His research interest covers theories, methods, and applications for robotics, parallel systems, social computing, parallel intelligence, and knowledge automation

收稿日期 2020-11-08 录用日期 2021-03-12 Manuscript received November 8, 2020; accepted March 12, 2021 广东省重点领域研发计划 (2020B090921003), 英特尔智能网联汽车大学合作研究中心项目 ( “ICRI-IACV”)资助 Supported by Key-Area Research and Development Program of Guangdong Province (2020B090921003) and Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles ( “ICRI-IACV”) 本文责任编委孙长银 Recommended by Associate Editor SUN Chang-Yin 1. 中国科学院自动化研究所复杂系统管理与控制国家重点实验室北京 100190 中国 2. 中山大学计算机学院广州 510006 中国3. 青岛慧拓智能机器有限公司青岛 266109 中国 4. 青岛智能产业技术研究院青岛 266109 中国 5. 中国矿业大学 (北京) 北京 100083 中国 6. 中国科学院大学人工智能学院北京 100049 中

国 7. 吉林大学计算机科学与技术学院长春 130012 中国 8. 滑铁卢大学机械与机电工程系滑铁卢 ON N2L 3G1 加拿大 1. State Key Laboratory for Management and Control of Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China 2. School of Data and Computer Science,Sun Yat-sen University, Guangzhou 510006, China 3. Vehicle Intelligence Pioneers, Inc., Qingdao 266109, China 4. Qingdao Academy of Intelligent Industries, Qingdao 266109, China 5. Chi- na University of Mining and Technology−Beijing, Beijing 100083, China 6. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China 7. College of Com-puter Science and Technology, Jilin University, Changchun 130012, China 8. Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo ON N2L 3G1, Canada

摘要

摘要:
针对新时代下我国矿区智能化发展诉求与矿山无人化进程中遇到的复现难、协同难的技术问题, 本文融合智慧矿山理念、ACP (Artificial societies + computational experiments + parallel execution)平行智能理论和新一代智能技术, 设计并实现了智慧矿山操作系统 (Intelligent mine operation system, IMOS), 为平行矿山智能管理与控制一体化提出了解决方案. 本文首先分析露天煤矿产业发展趋势; 国内外露天矿山智能化发展情况; 面向露天矿山无人化与智能化需求, 深度融合数字四胞胎理论, 设计了虚实融合的IMOS架构; 详细阐述了IMOS子系统架构与功能, 包括: 单车作业系统、多车协同系统、车路协同系统、无人驾驶智能系统、调度管理系统、平行系统、监管系统、远程接管系统和通信系统; 并探讨了IMOS关键技术, 即平行矿山仿真建模技术、无人驾驶技术、矿区通信技术和协同作业技术. 该操作系统是国内首套露天矿山无人化与智能化的一体化解决方案, 并能够迁移到不同矿区不同作业场景, 推动矿区智能化无人化发展, 减少人工干预从而降低安全风险, 大幅度降低人工成本, 提高生产作业效率, 并可结合社会发展要素为实现绿色可持续发展矿区提供支撑.
- 无人矿山 /
- 平行矿山 /
- 平行智慧矿山框架 /
- 数字孪生 /
- 数字四胞胎 /
- 矿山智能
Abstract:
In view of the development of coal mine industries in China, the requests to unmanned mines are urgently and immediately. In this paper, the parallel management and control of mining operating infrastructure that integrates the smart mine theories, the ACP (artificial societies + computational experiments + parallel execution) based parallel intelligence approaches and the new generation of artificial intelligence (AI) technologies (including data fusion, knowledge graph, edge computing, etc.) is proposed. The intelligent mine operation system (IMOS) that realizes parallel mining is designed. This paper analyzes the development trends of open-pit coal mines industries, the stages of current researches on intelligentization of open-pit mines at home and abroad, and deeply integrates with digital quadruple theory to design the IMOS architecture. Besides, the IMOS subsystems are introduced in details, including: the single-vehicle operating subsystem, multi-vehicle collaboration subsystem, vehicle-road collaboration subsystem, unmanned intelligent subsystem, dispatch management subsystem, parallel management and control subsystem, supervisory subsystem, remote takeover subsystem and communication subsystem; and the key technologies in IMOS are discussed. The smart mine operating system presented in this paper is the first systemic integrative solution for unmanned and intelligent mine, which covered all scenarios in open-pit mine intelligence, and taking social development factors as the measurement for mining area sustainable development.
- Unmanned mines /
- parallel mining /
- parallel smart mine infrastructure /
- digital twins /
- digital quadruplets /
- mining intelligence
注释:

1) 收稿日期 2020-11-08 录用日期 2021-03-12 Manuscript received November 8, 2020; accepted March 12, 2021 广东省重点领域研发计划 (2020B090921003), 英特尔智能网联汽车大学合作研究中心项目 ( “ICRI-IACV”)资助 Supported by Key-Area Research and Development Program of Guangdong Province (2020B090921003) and Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles ( “ICRI-IACV”) 本文责任编委孙长银 Recommended by Associate Editor SUN Chang-Yin 1. 中国科学院自动化研究所复杂系统管理与控制国家重点实验室北京 100190 中国 2. 中山大学计算机学院广州 510006 中国3. 青岛慧拓智能机器有限公司青岛 266109 中国 4. 青岛智能产业技术研究院青岛 266109 中国 5. 中国矿业大学 (北京) 北京 100083 中国 6. 中国科学院大学人工智能学院北京 100049 中

2) 国 7. 吉林大学计算机科学与技术学院长春 130012 中国 8. 滑铁卢大学机械与机电工程系滑铁卢 ON N2L 3G1 加拿大 1. State Key Laboratory for Management and Control of Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China 2. School of Data and Computer Science,Sun Yat-sen University, Guangzhou 510006, China 3. Vehicle Intelligence Pioneers, Inc., Qingdao 266109, China 4. Qingdao Academy of Intelligent Industries, Qingdao 266109, China 5. Chi- na University of Mining and Technology−Beijing, Beijing 100083, China 6. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China 7. College of Com-puter Science and Technology, Jilin University, Changchun 130012, China 8. Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo ON N2L 3G1, Canada

HTML全文

图 1 平行矿山示意图

Fig. 1 The illustration of parallel mining

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图 2 智慧无人矿山操作系统功能示意图

Fig. 2 The function diagram of intelligent unmanned mine operation system

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图 3 无人驾驶智能系统功能示意图

Fig. 3 The function diagram of unmanned driving intelligent system

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图 4 调度管理系统功能示意图

Fig. 4 The function diagram of dispatch management system

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图 5 调度管理系统流程图

Fig. 5 The process diagram of dispatch management system

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图 6 单车作业系统框架图

Fig. 6 The architecture of single-vehicle operating system

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图 7 多车协同系统功能示意图

Fig. 7 The Function diagram of multi-vehicle collaboration system

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图 8 平行系统功能示意图

Fig. 8 The function diagram of parallel system

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图 9 监管系统功能示意图

Fig. 9 The function diagram of supervisory system

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图 10 远程接管系统功能示意图

Fig. 10 The function diagram of remote takeover system

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图 11 无人驾驶任务划分示意图

Fig. 11 The illustration of unmanned driving task partitioning

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