Parallel Factories for Smart Industrial Operations: From Big AI Models to Field Foundational Models and Scenarios Engineering

Jingwei Lu; Xingxia Wang; Xiang Cheng; Jing Yang; Oliver Kwan; Xiao Wang

doi:10.1109/JAS.2022.106094

Volume 9 Issue 12

Dec. 2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2022 > 9(12): 2079-2086

J. W. Lu, X. X. Wang, X. Cheng, J. Yang, O. Kwan, and X. Wang, "Parallel factories for smart industrial operations: From big AI models to field foundational models and scenarios engineering, " IEEE/CAA J. Autom. Sinica, vol. 9, no. 12, pp.2079-2086, Dec. 2022. doi: 10.1109/JAS.2022.106094

Citation:

J. W. Lu, X. X. Wang, X. Cheng, J. Yang, O. Kwan, and X. Wang, "Parallel factories for smart industrial operations: From big AI models to field foundational models and scenarios engineering, " IEEE/CAA J. Autom. Sinica, vol. 9, no. 12, pp.2079-2086, Dec. 2022. doi: 10.1109/JAS.2022.106094

Citation:

J. W. Lu, X. X. Wang, X. Cheng, J. Yang, O. Kwan, and X. Wang, "Parallel factories for smart industrial operations: From big AI models to field foundational models and scenarios engineering, " IEEE/CAA J. Autom. Sinica, vol. 9, no. 12, pp.2079-2086, Dec. 2022. doi: 10.1109/JAS.2022.106094

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Parallel Factories for Smart Industrial Operations: From Big AI Models to Field Foundational Models and Scenarios Engineering

doi: 10.1109/JAS.2022.106094

Jingwei Lu^1
,,
Xingxia Wang^2
,,
Xiang Cheng^2
,,
Jing Yang^2
,,
Oliver Kwan^3
,,
Xiao Wang^{4
,
,}

1.
Qingdao Academy of Intelligent Industries, Qingdao 266114, and also with The State Key Laboratory for Management and Control of Complex Systems, Chinese Academy of Sciences, Beijing 100190, China
2.
The State Key Laboratory for Management and Control of Complex Systems, Chinese Academy of Sciences, Beijing 100190, and also with the School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
3.
Motion G, Inc., Singapore 068898, Singapore
4.
School of Artificial Intelligence, Anhui University, Hefei 230031, and also with the Qingdao Academy of Intelligent Industries, Qingdao 266114, China

More Information

Author Bio:
Jingwei Lu received the Ph.D. degree in computer application technology from University of Chinese Academy of Sciences, Beijing, China, in 2022. He was an Assistant Engineer with AVIC Manufacturing Technology Institute, Beijing, China, from 2017 to 2019. He is currently an assistant professor at the Qingdao Academy of Intelligent Industries, Qingdao, China. He has authored or co-authored over 20 journal and conference papers. His research interests include optimal control, parallel control, adaptive dynamic programming, reinforcement learning, and their industrial applications.e-mail: lujingweihh@gmail.com

Xingxia Wang received the master's degree in control theory and control engineering from Nankai University, Tianjin, China, in 2021. She is currently pursuing the Ph.D. degree in pattern recognition and intelligent system with University of Chinese Academy of Sciences, Beijing, China. Her research interests include parallel oil fields, artificial intelligence, and fault diagnosis of complex industrial processes.e-mail: wangxingxia2022@ia.ac.cn

Xiang Cheng received the master's degree in control science and engineering from the China University of Mining and Technology, Xuzhou, China, in 2019. He is currently working toward the Ph.D. degree in pattern recognition and intelligent system with University of Chinese Academy of Sciences, Beijing. His research interests include parallel oil fields, artificial intelligence, and modeling and optimization of complex industrial processes.e-mail: xiang.cheng@ia.ac.cn

Jing Yang received the bachelor degree in Automation from Beijing University of Chemical Technology in 2020. She is currently Ph.D. candidate at The State Key Laboratory for Management and Control of Complex Systems, Chinese Academy of Sciences, Beijing, China. Her research interests include parallel manufacturing, social manufacturing, cyber-physical-social systems, and artificial intelligence.e-mail: yangjing2020@ia.ac.cn

Oliver Kwan is the Founder and Chairman of Motion G, Inc. His research focuses on the relationships between statistics, inference, and knowledge transfer. He also works on industrial applications of digital twin, foundation model, and intelligent control.e-mail: oliver.kwan@motiong.com

Xiao Wang (Senior Member, IEEE) received her B.E degree in network engineering from the Dalian University of Technology, Dalian, China, in 2011, M.E and Ph.D. in social computing from the University of Chinese Academy of Sciences, Beijing, China, in 2016. She is president of Qingdao Academy of Intelligent Industries, and also a professor of School of Artificial Intelligence, Anhui University. Her research interests include social network analysis, social transportation, cybermovement organizations, and multi-agent modeling. She is an Associate Editor of IEEE TCSS, IEEE TIV, ITSM, Chinese Journal of Intelligent Science and Technology.e-mail: xiao.wang@ahu.edu.cn
Corresponding author: Xiao Wang, e-mail: xiao.wang@ahu.edu.cn

Abstract

Abstract

The rapid advancement of fundamental theories and computing capacity has brought artificial intelligence, internet of things, extended reality, and many other new intelligent technologies into our daily lives. Due to the lack of interpretability and reliability guarantees, it is extremely challenging to apply these technologies directly to real-world industrial systems. Here we present a new paradigm for establishing parallel factories in metaverses to accelerate the deployment of intelligent technologies in real-world industrial systems: QAII-1.0. Based on cyber-physical-social systems, QAII-1.0 incorporates complex social and human factors into the design and analysis of industrial operations and is capable of handling industrial operations involving complex social and human behaviors. In QAII-1.0, a field foundational model called EuArtisan combined with scenarios engineering is developed to improve the intelligence of industrial systems while ensuring industrial interpretability and reliability. Finally, parallel oil fields in metaverses are established to demonstrate the operating procedure of QAII-1.0.
- Cyber-physical-social system (CPSS),
- Industry 5.0,
- Metaverses,
- Parallel factories,
- Parallel intelligence

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References(37)

References

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Parallel Factories for Smart Industrial Operations: From Big AI Models to Field Foundational Models and Scenarios Engineering

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