数字孪生与平行系统:发展现状、对比及展望

杨林瑶; 陈思远; 王晓; 张俊; 王成红

doi:10.16383/j.aas.2019.y000002

数字孪生与平行系统:发展现状、对比及展望

doi: 10.16383/j.aas.2019.y000002

杨林瑶^1,2,3,,
陈思远^4,,
王晓^1,2,5, ,,
张俊^1,2,4,,
王成红^6,

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
2.
青岛智能产业技术研究院青岛 266000
3.
中国科学院大学北京 100049
4.
武汉大学电气与自动化学院武汉 430072
5.
青岛慧拓智能机器有限公司青岛 266000
6.
国家自然科学基金委员会信息科学部北京 100085

基金项目:

国家自然科学基金 61533019

国家自然科学基金 61702519

国家自然科学基金 U1811463

中国科协青年人才托举工程 2017QNRC001

详细信息

作者简介:
杨林瑶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博士研究生.2017年获得山东大学物联网工程学士学位.主要研究方向为平行车联网、大数据分析、智能交通.E-mail:yanglinyao2017@ia.ac.cn

陈思远武汉大学电气与自动化学院博士研究生.2018年获得武汉大学电气工程学院硕士学位.主要研究方向为智能电网，电力市场.E-mail:wddqcsy@whu.edu.cn

张俊武汉大学电气与自动化学院教授.2003年和2005年分别获得华中科技大学电子信息与通信工程系学士与硕士学位.2008年获得亚利桑那州立大学电气工程博士学位.主要研究方向为智能系统，人工智能，知识自动化，及其在智能电力和能源系统中的应用.E-mail:jun.zhang@qaii.ac.cn

王成红国家自然科学基金委员会信息科学部研究员.1982年获得河北科技大学学士学位.1988年获得中国矿业大学(北京)硕士学位.1997年获得中国科学院自动化研究所博士学位.主要研究方向为控制理论和系统可靠性理论.E-mail:chenghwang@163.com

通讯作者:
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.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, 人工智能和社交网络分析.本文通信作者.E-mail:x.wang@ia.ac.cn

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出版历程
- 收稿日期: 2019-09-17
- 录用日期: 2019-10-17
- 刊出日期: 2019-11-20

Digital Twins and Parallel Systems: State of the Art, Comparisons and Prospect

YANG Lin-Yao^{1,2,3
,},
CHEN Si-Yuan^4
,,
WANG Xiao^{1,2,5
, ,},
ZHANG Jun^{1,2,4
,},
WANG Cheng-Hong^6
,

1.
State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190
2.
Qingdao Academy of Intelligent Industries, Qingdao 266000
3.
University of Chinese Academy of Sciences, Beijing 100049
4.
School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072
5.
Vehicle Intelligence Pioneers Inc., Qingdao 266000
6.
Department of Information Sciences, National Natural Science Foundation of China, Beijing 100085

Funds:

National Natural Science Foundation of China 61533019

National Natural Science Foundation of China 61702519

National Natural Science Foundation of China U1811463

the Young Elite Scientists Sponsorship Program of China Association of Science and Technology 2017QNRC001

More Information

Author Bio:
Ph.D. candidate at the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences. He received his bachelor degree in internet of things from Shandong University in 2017. His research interest covers parallel Internet of vehicles, big data analysis, intelligent transportation

Ph.D. candidate at the School of Electrical Engineering and Automation, Wuhan University. He received his master dagree from the School of Electrical Engineering, Wuhan University in 2018, His research interest covers smart grid, electricity market

Professor at the School of Electrical Engineering and Automation, Wuhan University. He received his bachelor and master degrees in electrical engineering from Huazhong University of Science and Technology, Wuhan, China, in 2003 and 2005, respectively, and his Ph.D. degree in electrical engineering from Arizona State University, USA, in 2008. His research interest covers intelligent systems, artificial intelligence, knowledge automation, and their applications in intelligent power and energy systems

Professor at the Department of Information Sciences, National Natural Science Foundation of China. He received his bachelor degree from Hebei University of Science and Technology in 1982, his master degree from China University of Mining and Technology (Beijing) in 1988, and his Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences in 1997. His current research interest covers control theory and system reliability theory

Corresponding author: WANG Xiao Associate professor at the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences. 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, artificial intelligence and social network analysis. Corresponding author of this paper

摘要

摘要: 随着物联网、大数据、人工智能（Artificial intelligence，AI）等技术的发展，针对促进新一代信息技术与制造业深度融合、实现制造物理世界与信息世界交互与共融的需要，数字孪生和平行系统技术成为智能制造和复杂系统管理与控制领域研究的热点.本文对数字孪生和平行系统技术的基本概念、技术内涵、相关应用等进行了研究与总结，对比了两者之间的异同，并分析了两者的发展趋势，预期能够给复杂系统管理与控制领域的研究人员提供一定的参考和借鉴.
- 数字孪生 /
- 平行系统 /
- 复杂系统管理与控制 /
- 人工智能 /
- 虚实交互
Abstract: With the development of information technologies such as the internet of things, big data, and artificial intelligence (AI), digital twins and parallel systems have become hot-spots in manufacturing and management and control of complex systems with the aim of promoting the deep integration of the new generation information technology and manufacturing and realizing the interaction and integration of the manufacturing physical world and the cyber world. In this paper, the basic concepts, technical connotations and applications of digital twins and parallel systems are investigated and summarized, the similarities and differences between them are compared, and their development trends are also analyzed. Researchers in the field of management and control of complex systems are expected to get some references from this paper.
- Digital twins /
- parallel systems /
- management and control of complex systems /
- artificial intelligence (AI) /
- virtual-reality interaction
Recommended by Associate Editor LIU De-Rong
注释:

1) 本文责任编委刘德荣

HTML全文

图 1 数字孪生概念模型

Fig. 1 The conceptual model of digital twin

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图 2 增强现实实现数字孪生可视化框架^[94]

Fig. 2 The framework of visualising the digital twin data by using AR^[94]

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图 3 数字孪生城市

Fig. 3 Smart city with digital twins

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图 4 平行系统的研究框架

Fig. 4 The research framework of parallel systems

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图 5 基于ACP的平行系统架构体系

Fig. 5 The framework of the ACP-based parallel systems

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图 6 平行感知框架

Fig. 6 The framework of parallel perception

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图 7 平行系统架构

Fig. 7 The architecture of parallel systems

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表 1 数字孪生模型对比

Table 1 Comparisons of digital twin models

模型名称	提出者	分类	用途	模型实现方法
镜像空间模型	Grieves^[2]	通用模型	描述数字孪生系统组成	概念
产品生命周期管理集成模型	Behrang等^[32]	专用模型	制造系统快速重配置	概念
3D打印数字孪生模型	Mukherjee等^[18]	专用模型	3D打印产品检验	CAD
微制造单元数字孪生模型	Lohtander等^[34]	专用模型	描述微制造单元行为	FlexSim
基于云的数字孪生参考模型	Kritzinger等^[35]	通用模型	描述基于云计算的数字孪生架构	Qfsm
装配流程数字孪生模型	Caputo等^[37]	专用模型	装配流程数字孪生设计	MATLAB
数字孪生应用模型	Zheng等^[27]	通用模型	数字孪生设计方法	概念
信息物理制造模型	Cai等^[36]	专用模型	制造系统数字孪生构建	SolidWorks
可重构数字孪生模型	Zhang等^[38]	专用模型	装配流程数字孪生设计	−
流水车间数字孪生模型	Liu等^[39]	专用模型	流水生产系统设计	概念
数控机床数字孪生模型	Luo等^[40]	专用模型	数控机床数字孪生建模	DTMT, MWorks
加工刀具状态预测数字孪生模型	Qiao等^[41]	专用模型	构建道具状态监测数字孪生	深度学习
数字孪生五维模型	陶飞等^[28]	通用模型	通用数字孪生模型架构	概念
数字孪生技术模型	刘大同等^[29]	通用模型	描述数字孪生模型技术体系	概念

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