2.765

2022影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

工业人工智能发展方向

柴天佑

柴天佑. 工业人工智能发展方向. 自动化学报, 2020, 46(10): 2005−2012 doi: 10.16383/j.aas.c200796
引用本文: 柴天佑. 工业人工智能发展方向. 自动化学报, 2020, 46(10): 2005−2012 doi: 10.16383/j.aas.c200796
Chai Tian-You. Development directions of industrial artificial intelligence. Acta Automatica Sinica, 2020, 46(10): 2005−2012 doi: 10.16383/j.aas.c200796
Citation: Chai Tian-You. Development directions of industrial artificial intelligence. Acta Automatica Sinica, 2020, 46(10): 2005−2012 doi: 10.16383/j.aas.c200796

工业人工智能发展方向

doi: 10.16383/j.aas.c200796
基金项目: 国家自然科学基金委重大项目(61991400, 61991404), 中国工程院咨询研究重大项目(2019-ZD-12), 2020年度辽宁省科技重大专项计划(2020JH1/10100008)资助
详细信息
    作者简介:

    柴天佑:中国工程院院士, 东北大学教授. IEEE Fellow, IFAC Fellow, 欧亚科学院院士. 主要研究方向为自适应控制, 智能解耦控制, 流程工业综合自动化理论、方法与技术. E-mail: tychai@mail.neu.edu.cn

Development Directions of Industrial Artificial Intelligence

Funds: Supported by National Natural Science Foundation of China (61991400, 61991404), China Institute of Engineering Consulting Research Project (2019-ZD-12), and Science and Technology Major Project 2020 of Liaoning Province (2020JH1/10100008)
  • 摘要: 本文结合工业自动化和信息技术在工业革命中的作用以及制造与生产全流程决策、控制以及运行管理的现状和智能化发展方向的分析, 提出了发展工业人工智能的必要性. 通过对人工智能技术的涵义、发展简史和发展方向的分析以及自动化与人工智能研究与应用的核心目标、实现方式、研究对象与研究方法等方面的对比分析, 提出了工业人工智能技术的涵义. 通过对工业人工智能和工业自动化的研究对象与研究目标对比分析, 提出了工业人工智能的研究方向和研究思路与方法.
  • 图  1  工业自动化与信息技术在工业革命中的作用

    Fig.  1  The role of industrial automation and information technology in the industrial revolution

    图  2  制造与生产全流程的决策、控制与运行管理的现状

    Fig.  2  Current situation of decision-making, control and operation management manufacturing and production process

    图  3  人参与的信息物理系统

    Fig.  3  Human participation in information physics systems

    图  4  制造与生产全流程智能化

    Fig.  4  Intelligent manufacturing and production process

    图  5  制造流程由三层结构变革为智能化两层结构

    Fig.  5  The manufacturing process changed from three-layer structure to intelligent two-layer structure

    图  6  制造与生产流程CPS系统

    Fig.  6  Manufacturing and production process CPS

  • [1] Cyber-Physical Systems. Program Announcements & Information. The National Science Foundation, 4201 Wilson Boulevard, Arlington, Virginia 22230, USA. 2008-09-30[Online], available: http://www.nsf.gov/publications/pub_summ.jsp?ods_key=nsf08611, July 21, 2009.
    [2] National Science and Technology Council, Networking and Information Technology Research and Development Subcommittee U.S. The national artificial intelligence research and development strategic plan[Online], available: https://www.nitrd.gov/PUBS/national_ai_rd_strategic_plan.pdf.
    [3] Executive Office of the President U.S. Summary of the 2018 White House Summit on Artificial Intelligence for American Industry, Executive Office of the President U.S., USA, 2018.
    [4] National Science Foundation U.S. Statement on artificial intelligence for American industry[Online], available: https://www.nsf.gov/news/news_summ.jsp?cntn_id=245418, May 10, 2018
    [5] Executive Office of the President U.S. Fiscal Year 2021 Administration Research and Development Budget Priorities: Memorandum for the heads of executive departments and agencies[Online], available: https://www.whitehouse.gov/wp-content/uploads/2019/08/FY-21-RD-Budget-Priorities.pdf, 2019.
    [6] Executive Office of the President U.S. Fiscal Year 2020 Administration Research and Development Budget Priorities: Memorandum for the heads of executive departments and agencies[Online], available: https://www.whitehouse.gov/wp-content/uploads/2018/07/M-18-22.pdf, 2018.
    [7] Federal Republic of Germany. Strategy Artificial Intelligence of the Federal Government. 2018.
    [8] "新一代人工智能引领下的智能制造研究"课题组. 中国智能制造发展战略研究. 中国工程科学, 2018, 20(4): 1−8

    The Research Group for Research on Intelligent Manufacturing Development Strategy. Research on intelligent manufacturing development strategy in China. Engineering Science, 2018, 20(4): 1−8
    [9] Jordan M I, Mitchell T M. Machine learning: Trends, perspectives, and prospects. Science, 2015, 349(6245): 255−260 doi: 10.1126/science.aaa8415
    [10] Kusiak A. Smart manufacturing must embrace big data. Nature, 2017, 544(7648): 23−25 doi: 10.1038/544023a
    [11] Mayr O. Zur Frühgeschichte der Technischen Regelungen. R. Massachusetts: MIT Press, 1970.
    [12] Bennett S. A History of Control Engineering 1800-1930. London: Peter Peregrinus, 1979.
    [13] Morley D. Programmable controllers: How it all began. Intech, 2008, 55(8): Article No. 82
    [14] Strothman J. M and C Technology History More than a century of measuring and controlling industrial processes. Intech, 1995, 42(6): 52−78
    [15] Darby M L, Nikolaou M, Jones J, Nicholson D. RTO: An overview and assessment of current practice. Journal of Process Control, 2011, 21(6): 874−884 doi: 10.1016/j.jprocont.2011.03.009
    [16] Chai T Y, Qin S J, Wang H. Optimal operational control for complex industrial processes. Annual Reviews in Control, 2014, 38(1): 81−92 doi: 10.1016/j.arcontrol.2014.03.005
    [17] 柴天佑, 郑秉霖, 胡毅, 黄肖玲. 制造执行系统的研究现状和发展趋势. 控制工程, 2005, 12(6): 505−510 doi: 10.3969/j.issn.1671-7848.2005.06.001

    Chai Tian-You, Zheng Bing-Lin, Hu Yi, Huang Xiao-Ling. Current research situation and development of manufacturing execution systems. Control Engineering of China, 2005, 12(6): 505−510 doi: 10.3969/j.issn.1671-7848.2005.06.001
    [18] Hakason B. Execution-Driven Manufacturing Management for Competitive Advantage, MESA White Paper 5, Manufacturing Execution Systems Assoc, USA, 1997.
    [19] 柴天佑. 自动化科学与技术发展方向. 自动化学报, 2018, 44(11): 1923−1930

    Chai Tian-You. Development directions of automation science and technology. Acta Automatica Sinica, 2018, 44(11): 1923−1930
    [20] National Science Foundation. Convergence research at NSF[Online], available: https://www.nsf.gov/od/oia/convergence/index.jsp, 2018.
    [21] Gil Y, Greaves M, Hendler J, Hirsh H. Amplify scientific discovery with artificial intelligence. Science, 2014, 346(6206): 171−172 doi: 10.1126/science.1259439
    [22] 柴天佑. 制造流程智能化对人工智能的挑战. 中国科学基金, 2018, 32(3): 251−256

    Chai Tian-You. Artificial intelligence research challenges in intelligent manufacturing processes. Bulletin of National Natural Science Foundation of China, 2018, 32(3): 251−256
    [23] 柴天佑. 工业过程控制系统研究现状与发展方向. 中国科学: 信息科学, 2016, 46(8): 1003−1015 doi: 10.1360/N112016-00062

    Chai Tian-You. Industrial process control systems: Research status and development direction. Scientia Sinica Informationis, 2016, 46(8): 1003−1015 doi: 10.1360/N112016-00062
    [24] 柴天佑, 丁进良. 流程工业智能优化制造. 中国工程科学, 2018, 20(4): 51−58

    Chai Tian-You, Ding Jin-Liang. Smart and optimal manufacturing for process industry. Strategic Study of CAE, 2018, 20(4): 51−58
    [25] Hutson M. AI Glossary: Artificial intelligence, in so many words. Science, 2017, 357(6346): Article No. 19 doi: 10.1126/science.357.6346.19
    [26] Executive Office of the President U.S. Artificial intelligence, automation, and the economy[Online], available: https://www.iotforall.com/artificial-intelligence-automation-economy, 2016.
    [27] Artificial Intelligence and Life in 2030, One Hundred Year Study on Artificial Intelligence (AI100), Report of the 2015-2016 Study Panel, Stanford University, USA, 2016.
    [28] Dietmar Harhoff, Stefan Heumann, Nicola Jentzsch, Philippe Lorenz. Outline for a German Strategy for Artificial Intelligence[Online], available: https://www.ip.mpg.de/fileadmin/ipmpg/content/aktuelles/Outline_for_a_German_Artificial_Intelligence_Strategy.pdf, 2018.
    [29] Executive Office of the President, National Science and Technology Council, Committee on Technology, U.S. Preparing for the future of artificial intelligence[Online], available: https://www.linkedin.com/pulse/preparing-future-artificial-intelligence-sergiu-robu, 2016.
    [30] Defense Advanced Research Projects Agency (DARPA) U.S., Explainable Artificial Intelligence (XAI) 2017.
    [31] Pearl J. Theoretical impediments to machine learning with seven sparks from the causal revolution. In: Proceedings of the 11th ACM International Conference on Web Search and Data Mining. Marina Del Rey, USA: ACM, 2018.
    [32] Bareinboim E, Pearl J. Causal inference and the data-fusion problem. Proceedings of the National Academy of Sciences of the United States of America, 2016, 113(27): 7345−7352 doi: 10.1073/pnas.1510507113
    [33] Carter W A, Kinnucan E, Elliot J. A National Machine Intelligence Strategy for the United States, Center for Strategic and International Studies U.S., USA, 2018.
    [34] Executive Office of the President, National Science and Technology Council, Committee on Technology, U.S. Preparing for the Future of Artificial Intelligence, Executive Office of the President U.S., USA, 2016.
    [35] Huawei Cloud BU. Industrial AI Development White Paper. 2018
    [36] Lee J, Davari H, Singh J, Pandhare V. Industrial artificial intelligence for industry 4.0-based manufacturing systems. Manufacturing Letters, 2018, 18: 20−23 doi: 10.1016/j.mfglet.2018.09.002
    [37] Palhares R M, Yuan Y, Wang Q. Artificial intelligence in industrial systems. IEEE Transactions on Industrial Electronics, 2019, 66(12): 9636−9640 doi: 10.1109/TIE.2019.2916709
    [38] Lee, Singh J, Azamfar M. Industrial artificial intelligence. arXiv: 1908.02150v1, 2019.
    [39] Munakata T. Commercial and industrial AI. Communications of the ACM, 1994, 37(3): 23−26 doi: 10.1145/175247.175248
    [40] Munakata T. New horizons in commercial and industrial AI. Communications of The ACM, 1995, 38(11): 28−31 doi: 10.1145/219717.219734
    [41] 工业互联网产业联盟. 工业智能白皮书[Online], 获取自: https://tech.sina.com.cn/roll/2020-07-06/doc-iircuyvk2170592.shtml, 2020.
    [42] National Academies of Sciences, Engineering, and Medicine. Graduate STEM Education for the 21st Century, The National Academies Press, USA, 2018.
  • 加载中
图(6)
计量
  • 文章访问数:  4293
  • HTML全文浏览量:  919
  • PDF下载量:  1565
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-09-25
  • 录用日期:  2020-10-13
  • 刊出日期:  2020-10-29

目录

    /

    返回文章
    返回