面向智能制造的自主可控工业互联网发展研究

袁礼伟, 王耀南, 谭浩然, 方遒, 李哲

中国工程科学 ›› 2025

PDF(3637 KB)
PDF(3637 KB)
中国工程科学 ›› 2025 DOI: 10.15302/J-SSCAE-2024.12.027

面向智能制造的自主可控工业互联网发展研究

作者信息 +

Independent and Controllable Industrial Internet for Intelligent Manufacturing

Author information +
History +

摘要

在制造业与新一代信息技术深度融合发展并加速智能化变革的背景下,亟需突破工业软件研发、高端装备制造等“卡脖子”技术环节,构建自主可控工业互联网技术体系以支撑智能制造全流程优化。本文梳理了智能制造与工业互联网的发展现状,从工业互联网技术要素、基于工业互联网的智能制造技术要素、自主可控工业互联网软硬件系统3个方面呈现了面向智能制造的自主可控工业互联网技术体系全貌;系统总结了面向智能制造的自主可控工业互联网技术示范应用,涵盖自主可控的机器人化智能制造、基于自主可控工业互联网的工业检测与感知、面向智能制造的网络化多机协同控制、面向智能制造的多机协同调度规划;进一步研判了面向智能制造的自主可控工业互联网的当前挑战和技术方向。可积极应用第五代移动通信、自主可控工业软件、工业互联网“云边端”协同、搭载国产分布式操作系统的机器人、自主可控的多机协同制造技术,同时加快构建自主可控标准体系,驱动工业互联网与智能制造的融合发展,为我国制造业稳健升级和高质量发展开辟新途径。

Abstract

As the manufacturing industry integrates deeply with the next-generation information technology and accelerates its transformation to intelligence, it is necessary to break the technical bottlenecks regarding industrial software development and high-end equipment manufacturing and establish an independent and controllable industrial Internet technology system to support the optimization of the entire process of intelligent manufacturing. This study analyzes the current status of intelligent manufacturing and industrial Internet, and presents an overall picture of the independent and controllable industrial Internet technology system for intelligent manufacturing from three aspects: industrial Internet technologies, intelligent manufacturing technologies based on industrial Internet, and independent and controllable software and hardware systems for industrial Internet. Moreover, this study summarizes the demonstrative applications of independent and controllable industrial Internet technologies for intelligent manufacturing, covering independent robotized intelligent manufacturing, industrial detection and perception based on independent and controllable industrial Internet, networked multi-robot collaboration for intelligent manufacturing, and multi-robot collaborative scheduling for intelligent manufacturing. The current challenges and technical directions of independent and controllable industrial Internet for intelligent manufacturing are also identified. Furthermore, it is proposed to actively apply technologies including the fifth-generation mobile communications, independent and controllable industrial software, cloud-edge-end collaboration of industrial Internet, robots equipped with domestic distributed operating systems, and independent and controllable multi-robot collaborative manufacturing. Meanwhile, it is necessary to accelerate the construction of an independent and controllable standards system to drive the integrated development of the industrial Internet and intelligent manufacturing, creating new paths for the upgrading and high-quality development of China's manufacturing industry.

关键词

工业互联网 / 智能制造 / 自主可控 / 机器人 / 软硬件系统 / 示范应用

Keywords

industrial Internet / intelligent manufacturing / independent and controllable / robotics / hardware and software system / demonstration application

引用本文

导出引用
袁礼伟, 王耀南, 谭浩然. 面向智能制造的自主可控工业互联网发展研究. 中国工程科学. 2025 https://doi.org/10.15302/J-SSCAE-2024.12.027

参考文献

[1]
杨叔子, 丁洪‍. 智能制造技术与智能制造系统的发展与研究 [J]. 中国机械工程, 1992, 3(2): 15‒18.
Yang S Z, Ding H. Development and research of intelligent manufacturing technology and intelligent manufacturing system [J]. China Mechanical Engineering, 1992, 3(2): 15‒18.
[2]
Rzevski G. A framework for designing intelligent manufacturing systems [J]. Computers in Industry, 1997, 34(2): 211‒219.
[3]
Shen W M, Maturana F, Norrie D H. MetaMorph II: An agent-based architecture for distributed intelligent design and manufacturing [J]. Journal of Intelligent Manufacturing, 2000, 11(3): 237‒251.
[4]
Kim H. The role of information technology in production control in a job shop environment considering customers and suppliers [D]. Ohio: The Ohio State University (Doctoral dissertation), 1996.
[5]
Chen M, Linkens D A. An on-line information processing method for intelligent control systems [R]. New Orleans: 34th IEEE Conference on Decision and Control, 1995.
[6]
Lau H. The new role of intranet/Internet technology for manufacturing [J]. Engineering with Computers, 1998, 14(2): 150‒155.
[7]
Zhou J, Li P G, Zhou Y H, et al. Toward new-generation intelligent manufacturing [J]. Engineering, 2018, 4(1): 11‒20.
[8]
Wang F Y, Yang J, Wang X X, et al. Chat with ChatGPT on industry 5.0: Learning and decision-making for intelligent industries [J]. CAA Journal of Automatica Sinica, 2023, 10(4): 831‒834.
[9]
Mattera G, Nele L, Paolella D. Monitoring and control the wire arc additive manufacturing process using artificial intelligence techniques: A review [J]. Journal of Intelligent Manufacturing, 2024, 35(2): 467‒497.
[10]
Mu N, Gong S L, Sun W Q, et al. The 5G MEC applications in smart manufacturing [R]. Beijing: 2020 IEEE International Conference on Edge Computing (EDGE), 2020.
[11]
付宇涵, 马冬妍, 唐旖浓, 等‍. 工业互联网平台赋能流程制造行业转型升级场景分析 [J].科技导报, 2022, 40(10):129‒136.
Fu Y H, Ma D Y, Tang Y N, et al. Scenario analysis of transformation and upgrading of process manufacturing industry based on industrial Internet platform [J]. Science & Technology Review, 2022, 40(10): 129‒136.
[12]
Hu Y J, Jia Q M, Yao Y, et al. Industrial Internet of things intelligence empowering smart manufacturing: A literature review [J]. IEEE Internet of Things Journal, 2024, 11(11): 19143‒19167.
[13]
Li B H, Chai X D, Hou B C, et al. New generation artificial intelligence-driven intelligent manufacturing (NGAIIM)‍ [R]. Guangzhou: 2018 IEEE Smart World, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation, 2018.
[14]
Lu Y Q, Wang L H, Bao J S, et al. Semantic artificial intelligence for smart manufacturing automation [J]. Robotics and Computer-Integrated Manufacturing, 2022, 77: 102333.
[15]
Song C H, Zheng H Y, Han G J, et al. Cloud edge collaborative service composition optimization for intelligent manufacturing [J]. IEEE Transactions on Industrial Informatics, 2023, 19(5): 6849‒6858.
[16]
Wan J F, Tang S L, Li D, et al. A manufacturing big data solution for active preventive maintenance [J]. IEEE Transactions on Industrial Informatics, 2017, 13(4): 2039‒2047.
[17]
张振国, 毛建旭, 谭浩然, 等‍. 重大装备制造多机器人任务分配与运动规划技术研究综述 [J]. 自动化学报, 2024, 50(1): 21‒41.
Zhang Z G, Mao J X, Tan H R, et al. A review of task allocation and motion planning for multi-robot in major equipment manufacturing [J]. Acta Automatica Sinica, 2024, 50(1): 21‒41.
[18]
Zhu Q Z, Huang S H, Wang G X, et al. Dynamic reconfiguration optimization of intelligent manufacturing system with human-robot collaboration based on digital twin [J]. Journal of Manufacturing Systems, 2022, 65: 330‒338.
[19]
孙一元‍. 上汽集团: 打造"智能制造" 新商业模式 [J]. 上海国资, 2021 (7): 50‒52.
Sun Y Y. SAIC: Building a new business model of "intelligent manufacturing" [J]. Capital Shanghai, 2021 (7): 50‒52.
[20]
Zhong R Y, Xu X, Klotz E, et al. Intelligent manufacturing in the context of industry 4.0: A review [J]. Engineering, 2017, 3(5): 616‒630.
[21]
Yang T, Yi X L, Lu S W, et al. Intelligent manufacturing for the process industry driven by industrial artificial intelligence [J]. Engineering, 2021, 7(9): 1224‒1230.
[22]
杨林, 陆亮亮‍. "互联网+"背景下制造企业智能化战略转型路径:多案例比较研究 [J]. 科技进步与对策, 2022, 39(12):92‒101.
Yang L, Lu L L. Exploring the intelligent strategic transformation path of manufacturing enterprises under the background of "Internet+": A muti-case comparative study [J]. Science & Technology Progress and Policy, 2022, 39(12): 92‒101.
[23]
Tao F, Qi Q L, Liu A, et al. Data-driven smart manufacturing [J]. Journal of Manufacturing Systems, 2018, 48: 157‒169.
[24]
Wang J J, Ma Y L, Zhang L B, et al. Deep learning for smart manufacturing: Methods and applications [J]. Journal of Manufacturing Systems, 2018, 48: 144‒156.
[25]
Tuptuk N, Hailes S. Security of smart manufacturing systems [J]. Journal of Manufacturing Systems, 2018, 47: 93‒106.
[26]
Sahoo S, Lo C Y. Smart manufacturing powered by recent technological advancements: A review [J]. Journal of Manufacturing Systems, 2022, 64: 236‒250.
[27]
Chen Y B. Integrated and intelligent manufacturing: Perspectives and enablers [J]. Engineering, 2017, 3(5): 588‒595.
[28]
Annanth V K, Abinash M, Rao L B. Intelligent manufacturing in the context of industry 4.0: A case study of Siemens industry [J]. Journal of Physics: Conference Series, 2021, 1969(1): 012019.
[29]
Lee J, Bagheri B, Kao H G. A cyber-physical systems architecture for industry 4.0-based manufacturing systems [J]. Manufacturing Letters, 2015, 3: 18‒23.
[30]
Li J Q, Yu F R, Deng G Q, et al. Industrial Internet: A survey on the enabling technologies, applications, and challenges [J]. IEEE Communications Surveys & Tutorials, 2017, 19(3): 1504‒1526.
[31]
Rehman M H, Yaqoob I, Salah K, et al. The role of big data analytics in industrial Internet of things [J]. Future Generation Computer Systems, 2019, 99: 247‒259.
[32]
Younan M, Houssein E H, Elhoseny M, et al. Challenges and recommended technologies for the industrial Internet of things: A comprehensive review [J]. Measurement, 2020, 151: 107198.
[33]
陈培全‍. 数字化制造与工业互联网融合的智能制造模式探究 [J]. 中国机械, 2023 (29): 43‒46.
Chen P Q. Research on intelligent manufacturing mode of digital manufacturing and industrial Internet integration [J]. Machine China, 2023 (29): 43‒46.
[34]
Evans P C, Annunziata M. Industrial Internet: Pushing the boundaries of minds and machines [R]. Boston: General Electric Reports, 2012.
[35]
Morrish J, Figueredo K, Haldeman S, et al. The industrial Internet of things, volume b01: Business strategy and innovation framework [R]. Boston: Industrial Internet Consortium, 2016.
[36]
Joshi S J, Mamaniya S, Shah R. Integration of intelligent manufacturing in smart factories as part of industry 4.0—A review [R]. Mumbai: 2022 Sardar Patel International Conference on Industry 4.0—Nascent Technologies and Sustainability for "Make in India" Initiative, 2022.
[37]
Li Y J, Wang D, Sun T, et al. Solutions for variant manufacturing factory scenarios based on 5G edge features [R]. Beijing: 2020 IEEE International Conference on Edge Computing (EDGE), 2020.
[38]
Ding P, Liu D, Shen Y, et al. Industrial intelligent edge computing system based on 5G [R]. Harbin: 2021 International Wireless Communications and Mobile Computing (IWCMC), 2021.
[39]
韩冬, 张晶‍. 5G端到端网络切片标准化演进研究 [J]. 现代传输, 2024 (4): 60‒63.
Han D, Zhang J. Research on standardization evolution of 5G end-to-end network slice [J]. Modern Transmission, 2024 (4): 60‒63.
[40]
Xiong Z W, Li Q K, Feng Z L. Research on model based on big data technology for process control [R]. Hangzhou: 2023 International Conference on Intelligent Computing and Next Generation Networks (ICNGN), 2023.
[41]
Sun Q, Li Y H, Zhou C J, et al. Root cause analysis for industrial process anomalies through the integration of knowledge graph and large language model [R]. Kunming: 2024 43rd Chinese Control Conference (CCC), 2024.
[42]
Mikhailov A, Tretyakov S, Andreev Y. A new approach to build industrial Internet of things (IIoT) systems based on digital twin's technologies [R]. Sochi: 2022 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), 2022.
[43]
Rayhana R, Bai L, Xiao G Z, et al. Digital twin models: Functions, challenges, and industry applications [J]. IEEE Journal of Radio Frequency Identification, 2024, 8: 282‒321.
[44]
Pansare R, Yadav G, Nagare M R. Reconfigurable manufacturing system: A systematic review, meta-analysis and future research directions [J]. Journal of Engineering, Design and Technology, 2023, 21(1): 228‒265.
[45]
Wan J F, Li X M, Dai H N, et al. Artificial-intelligence-driven customized manufacturing factory: Key technologies, applications, and challenges [J]. Proceedings of the IEEE, 2021, 109(4): 377‒398.
[46]
Kim Y G, Lee S, Son J, et al. Multi-agent system and reinforcement learning approach for distributed intelligence in a flexible smart manufacturing system [J]. Journal of Manufacturing Systems, 2020, 57: 440‒450.
[47]
Shaik A M, Rao V V S K, Rao C S. Development of modular manufacturing systems—A review [J]. The International Journal of Advanced Manufacturing Technology, 2015, 76(5): 789‒802.
[48]
Wang H, Liu M, Shen W M. Industrial-generative pre-trained transformer for intelligent manufacturing systems [J]. IET Collaborative Intelligent Manufacturing, 2023, 5(2): e12078.
[49]
Fan H L, Liu X, Fuh J Y H, et al. Embodied intelligence in manufacturing: Leveraging large language models for autonomous industrial robotics [J]. Journal of Intelligent Manufacturing, 2025, 36(2): 1141‒1157.
[50]
Sun Z J, Yang H, Li C, et al. Cloud-edge collaboration in industrial Internet of things: A joint offloading scheme based on resource prediction [J]. IEEE Internet of Things Journal, 2022, 9(18): 17014‒17025.
[51]
Zhang Q, Zhang Y F, Luo Q, et al. Cloud-edge-end-based aircraft assembly production quality monitoring system framework and applications [J]. Journal of Manufacturing Systems, 2024, 75: 116‒131.
[52]
Liao H J, Jia Z H, Zhou Z Y, et al. Cloud-edge-end collaboration in air-ground integrated power IoT: A semidistributed learning approach [J]. IEEE Transactions on Industrial Informatics, 2022, 18(11): 8047‒8057.
[53]
Qu X F, Wang H Q. Emergency task offloading strategy based on cloud-edge-end collaboration for smart factories [J]. Computer Networks, 2023, 234: 109915.
[54]
Chi H R, Wu C K, Huang N-F, et al. A survey of network automation for industrial Internet-of-things toward industry 5.0 [J]. IEEE Transactions on Industrial Informatics, 2023, 19(2): 2065‒2077.
[55]
姜红德‍. 欧拉迎来四大升级, 国产开源操作系统进一步增强 [J]. 中国信息化, 2024 (6): 10.
Jiang H D. Euler ushered in four major upgrades and the domestic open source operating system was further enhanced [J]. China Informatization, 2024 (6): 10.
[56]
孙晓霞‍. 工业软件: 纾困突围, 助推工业互联网高质量发展 [J]. 新材料产业, 2022 (3): 48‒52.
Sun X X. Industrial software: Breaking through the difficulties and promoting the high-quality development of industrial Internet [J]. Advanced Materials Industry, 2022 (3): 48‒52.
[57]
刘文广‍. CAD技术发展新趋势 [J]. 新技术新工艺, 2024 (1): 1‒9.
Liu W G. New development trends of CAD technology [J]. New Technology & New Process, 2024 (1): 1‒9.
[58]
叶瑛歆‍. 基于云知识库的数控机床智能控制器加工工艺规划方法研究 [D]. 济南: 山东大学(博士学位论文), 2019.
Ye Y X. Research on machining process planning method of CNC machine tool intelligent controller based on cloud knowledge base [D]. Jinan: Shandong University (Doctoral dissertation), 2019.
[59]
陈燕, 王禹封, 谯木, 等‍. 数字孪生在制造业中实现的关键技术及典型应用综述 [J]. 航空制造技术, 2024, 67(11): 24‒45.
Chen Y, Wang Y F, Qiao M, et al. Review on key technologies and typical applications of digital twin in manufacturing industry [J]. Aeronautical Manufacturing Technology, 2024, 67(11): 24‒45.
[60]
柴天佑, 刘强, 丁进良, 等‍. 工业互联网驱动的流程工业智能优化制造新模式研究展望 [J]. 中国科学: 技术科学, 2022, 52(1): 14‒25.
Chai T Y, Liu Q, Ding J L, et al. Perspectives on industrial-Internet-driven intelligent optimized manufacturing mode for process industries [J]. Scientia Sinica Technologica, 2022, 52(1): 14‒25.
[61]
王雷, 卢珊珊, 张松岚‍. 依托生态位战略的国产工业软件发展 [J]. 软件导刊, 2024, 23(2): 208‒214.
Wang L, Lu S S, Zhang S L. Relying on niche strategy to promote the development of domestic industrial software [J]. Software Guide, 2024, 23(2): 208‒214.
[62]
莫洋, 王耀南, 刘杰, 等‍. 我国智能机器人核心芯片技术发展战略研究 [J]. 中国工程科学, 2022, 24(4): 62‒73.
Mo Y, Wang Y N, Liu J, et al. Development strategy for the core chip technology of intelligent robot in China [J]. Strategic Study of CAE, 2022, 24(4): 62‒73.
[63]
加快建设科技强国实现高水平科技自立自强 [EB/OL]. (2022-04-30)‍[2025-02-15]. https://www.gov.cn/xinwen/2022-04/30/content_5688265.htm.
Accelerate the construction of a strong country in science and technology and achieve high-level scientific and technological self-reliance and self-improvement [EB/OL]. (2022-04-30)[2025-02-15]. https://www.gov.cn/xinwen/2022-04/30/content_5688265.htm.
[64]
范玫杉, 刘嘉, 马伟佳‍. 协作机器人技术与产业分析 [J]. 科技和产业, 2024, 24(11): 282‒288.
Fan M S, Liu J, Ma W J. Collaborative robotics technology and industry analysis [J]. Science Technology and Industry, 2024, 24(11): 282‒288.
[65]
新一代国产自主可控复合机器人"遨游300"重磅发布 [J]. 物流技术与应用, 2024, 29(1): 64.
A new generation of domestic autonomous controllable composite robot "Roaming 300" was released [J]. Logistics & Material Handling, 2024, 29(1): 64.
[66]
任文清‍. 国内首台鸿蒙系统矿用巡检机器人在神东投用 [J]. 能源科技, 2021, 19(5): 94.
Ren W Q. The first domestic HarmonyOS mine-used inspection robot has been put into use in Shendong Coal [J]. Energy Science and Technology, 2021, 19(5): 94.
[67]
杨光‍. 首款搭载鸿蒙操作系统的人形机器人夸父进厂做工 [N]. 中国信息化周报, 2024-07-15(19).
Yang G. The first humanoid robot Kuafu equipped with Harmony OS enters the factory for production [N]. China Information Weekly, 2024-07-15(19).
[68]
邓勇, 何茂松‍. "未来"已来人工智能产业无限精彩 [N]. 绵阳日报, 2024-07-25(03).
Deng Y, He M S. "The future" has arrived and the artificial intelligence industry is full of excitement [N]. Mianyang Daily, 2024-07-25(03).
[69]
Jin X H, Xu Z W, Qiao W. Condition monitoring of wind turbine generators using SCADA data analysis [J]. IEEE Transactions on Sustainable Energy, 2021, 12(1): 202‒210.
[70]
Costa T P, Costa D M B, Murphy F. A systematic review of real-time data monitoring and its potential application to support dynamic life cycle inventories [J]. Environmental Impact Assessment Review, 2024, 105: 107416.
[71]
Gagliardi J P, Renaud J, Ruiz A. On sequencing policies for unit-load automated storage and retrieval systems [J]. International Journal of Production Research, 2014, 52(4): 1090‒1099.
[72]
Zhang B Z, Chen X W, Li Z H, et al. CoNi-MPC: Cooperative non-inertial frame based model predictive control [J]. IEEE Robotics and Automation Letters, 2023, 8(12): 8082‒8089.
[73]
Zhang G Y, He X D, Mu Y, et al. Serial distributed detection in multihop multirelay wireless sensor networks with end-edge-cloud orchestration under graph-powered computing [J]. IEEE Internet of Things Journal, 2025, 12(4): 3720‒3733.
[74]
孙立宁, 许辉, 王振华, 等‍. 工业机器人智能化应用关键共性技术综述 [J]. 振动、测试与诊断, 2021, 41(2): 211‒219.
Sun L N, Xu H, Wang Z H, et al. Review on key common technologies for intelligent applications of industrial robots [J]. Journal of Vibration, Measurement & Diagnosis, 2021, 41(2): 211‒219.
[75]
薛建儒, 房建武, 吴俊, 等‍. 多机协同智能发展战略研究 [J]. 中国工程科学, 2024, 26(1): 101‒116.
Xue J R, Fang J W, Wu J, et al. Collaborative multiple autonomous systems [J]. Strategic Study of CAE, 2024, 26(1): 101‒116.
[76]
Brown K, Peltzer O, Sehr M A, et al. Optimal sequential task assignment and path finding for multi-agent robotic assembly planning [R]. Paris: 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020.
[77]
Yu J J, LaValle S M. Optimal multirobot path planning on graphs: Complete algorithms and effective heuristics [J]. IEEE Transactions on Robotics, 2016, 32(5): 1163‒1177.
[78]
Basu A, Conforti M, Di Summa M, et al. Complexity of branch-and-bound and cutting planes in mixed-integer optimization [J]. Mathematical Programming, 2023, 198(1): 787‒810.
[79]
王秋华, 吴国华, 魏东晓, 等‍. 工业互联网安全产业发展态势及路径研究 [J]. 中国工程科学, 2021, 23(2): 46‒55.
Wang Q H, Wu G H, Wei D X, et al. Research on the development trend and path of industrial Internet security industry [J]. Strategic Study of CAE, 2021, 23(2): 46‒55.
[80]
王鹤子, 张中献, 杨学‍. 国内外经典工业互联网体系架构发展研究 [J]. 数据与计算发展前沿, 2025, 7(1): 119‒134.
Wang H Z, Zhang Z X, Yang X. Research on global classic industrial Internet architecture development [J]. Frontiers of Data & Computing, 2025, 7(1): 119‒134.
基金
中国工程院咨询项目“湖南省人工智能产业创新发展战略研究”(2023-DFZD-61); 国家自然科学基金项目(62293510); 湖南省科技重大专项(2021GK1010)
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