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信息物理系统技术现状分析与趋势综述

杨挺 刘亚闯 刘宇哲 王成山

杨挺, 刘亚闯, 刘宇哲, 王成山. 信息物理系统技术现状分析与趋势综述[J]. 电子与信息学报, 2021, 43(12): 3393-3406. doi: 10.11999/JEIT211135
引用本文: 杨挺, 刘亚闯, 刘宇哲, 王成山. 信息物理系统技术现状分析与趋势综述[J]. 电子与信息学报, 2021, 43(12): 3393-3406. doi: 10.11999/JEIT211135
Ting YANG, Yachuang LIU, Yuzhe LIU, Chengshan WANG. Review on Cyber-Physical System: TechnologyAnalysis and Trends[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3393-3406. doi: 10.11999/JEIT211135
Citation: Ting YANG, Yachuang LIU, Yuzhe LIU, Chengshan WANG. Review on Cyber-Physical System: TechnologyAnalysis and Trends[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3393-3406. doi: 10.11999/JEIT211135

信息物理系统技术现状分析与趋势综述

doi: 10.11999/JEIT211135
基金项目: 国家重点研发计划(2017YFE0132100),国家自然科学基金(61971305)
详细信息
    作者简介:

    杨挺:男,1979年生,博士,教授,主要研究方向为电力信息物理系统、智能配用电、人工智能与大数据等

    刘亚闯:男,1989年生,博士生,主要研究方向为电力信息物理系统、智能配用电控制等

    刘宇哲:男,1998年生,硕士生,主要研究方向为电力信息物理系统、智能配用电控制等

    王成山:男,1962年生,博士,中国工程院院士,主要从事配电系统规划与运行、电网安全性与稳定性、分布式能源与微电网等

    通讯作者:

    杨挺 yangting@tju.edu.cn

  • 中图分类号: TP29

Review on Cyber-Physical System: TechnologyAnalysis and Trends

Funds: The National Key R&D Program of China(2017YFE0132100), The National Natural Science Foundation of China (61971305)
  • 摘要: 随着国民经济中各个产业信息化程度的提升和产业间深度交叉融合,信息物理系统(Cyber-Physical System, CPS)正成为支撑这一发展的关键技术,也被誉为是引领全球新一轮产业技术变革的核心体系。通过将客观物理世界中实体、行为以及交互环境等精准映射至信息空间,进行实时处理并反馈回物理空间,CPS能够从系统视角和不同层面解决复杂系统的分析建模、决策优化、不确定处理等难题。该文从CPS的体系架构和设计开发流程分析了其关键技术和难点瓶颈,探讨了CPS与边云协同计算、数字孪生、人工智能和区块链等前沿技术间相互支撑关系,综述了CPS在工业生产、能源电力、交通驾驶和医疗健康4个应用领域研究现状。最后,对CPS未来的技术发展进行了展望。以希望为信息物理系统及相关研究领域的专家和学者提供参考,为我国产业科技变革和智能化转型提供技术支持。
  • 图  1  CPS闭环技术体系

    图  2  CPS系统的体系架构

    图  3  CPS的技术体系

    图  4  闭环TMR电流传感器

    图  5  分布式网络系统可信接入架构

    图  6  CPS与雾-边-云计算

    图  7  CPS与数字孪生

    图  8  基于人工智能的信息物理社会系统模型

    图  9  区块链技术对CPS的支撑

    图  10  典型的工业制造CPS系统架构

    表  1  CPS概念列举

    组织概念
    中国科学院CPS是在环境感知的基础上,深度融合计算、通信和控制能力的可控可信可扩展的网络化物理系统。通过计算进程和物理进程相互影响的反馈循环实现深度融合和实时交互来增加或扩展新的功能,以安全、可靠、高效和实时的方式监测或控制物理实体。
    美国国家科学基金会CPS是基于嵌入式的计算核心实现感知、控制、集成的工程系统,信息被“深度嵌入”到每一个互联物理组件(甚至物料)中,其功能由信息和物理空间交互实现。
    欧盟第七框架计划CPS主要具有计算、通信和控制功能,并将这些功能与不同物理过程(如机械、电子和化学)深度融合。
    德国国家科学与工程院CPS是指使用传感器直接获取物理数据和执行器作用物理过程的嵌入式系统,使用来自各地的数据和服务,通过数字网络将物流、在线服务、协调与管理过程连接,其开放的技术系统使整个系统的功能、服务远远超出了当前的嵌入式系统。
    下载: 导出CSV
  • [1] 中国电子技术标准化研究院. 信息物理系统白皮书(2017)[EB/OL]. http://www.cesi.cn/201703/2251.html, 2017.

    China Electron-ics Standardization Institute. White paper: Cyber-physical system[EB/OL]. http://www.cesi.cn/201703/2251.html, 2017.
    [2] The White House. American Competitiveness Initiative[EB/OL]. http://georgewbush-whitehouse.archives.gov/stateoftheunion/2006/aci/, 2006.
    [3] CPS Week[EB/OL]. https://cpsweek2017.ece.cmu.edu/index.php/events/.
    [4] 国务院. 国务院关于深化制造业与互联网融合发展的指导意见: 国发〔2016〕28号[EB/OL]. http://www.gov.cn/zhengce/content/2016-05/20/content_5075099.htm, 2016.
    [5] MONOSTORI L, KÁDÁR B, BAUERNHANSL T, et al. Cyber-physical systems in manufacturing[J]. CIRP Annals, 2016, 65(2): 621–641. doi: 10.1016/j.cirp.2016.06.005
    [6] YU Xinghuo and XUE Yusheng. Smart grids: A cyber–physical systems perspective[J]. Proceedings of the IEEE, 2016, 104(5): 1058–1070. doi: 10.1109/JPROC.2015.2503119
    [7] WEI Guo, YI Zhang, and LI Li. The integration of CPS,CPSS, and ITS: A focus on data[J]. Tsighua Science and Technology, 2015, 20(4)): 327–335.
    [8] GU Lin, ZENG Deze, GUO Song, et al. Cost efficient resource management in fog computing supported medical cyber-physical system[J]. IEEE Transactions on Emerging Topics in Computing, 2017, 5(1): 108–119. doi: 10.1109/TETC.2015.2508382
    [9] YAN Wang. Probabilistic modeling of information dynamics in networked cyber-physical-social systems[J]. IEEE Internet of Things Journal, 2021, 8(19)–14947.
    [10] LEE J, BAGHERI B, and KAO H A. A cyber-physical systems architecture for industry 4.0-based manufacturing systems[J]. Manufacturing Letters, 2015, 3: 18–23. doi: 10.1016/j.mfglet.2014.12.001
    [11] OZTEMEL E and GURSEV S. Literature review of Industry 4.0 and related technologies[J]. Journal of Intelligent Manufacturing, 2020, 31(1): 127–182. doi: 10.1007/s10845-018-1433-8
    [12] 杨挺, 张卓凡, 刘亚闯, 等. 基于改进深度信念网络的TMR电流传感器温漂与地磁场校正方法[J]. 天津大学学报:自然科学与工程技术版, 2021, 54(8): 875–880.

    YANG Ting, ZHANG Zhuofan, LIU Yachuang, et al. Correction method for temperature drift and geomagnetic field of TMR current sensor based on improved deep belief network[J]. Journal of Tianjin University:Science and Technology, 2021, 54(8): 875–880.
    [13] CHOUDHARY G, ASTILLO P V, YOU I, et al. Lightweight misbehavior detection management of embedded IoT devices in medical cyber physical systems[J]. IEEE Transactions on Network and Service Management, 2020, 17(4): 2496–2510. doi: 10.1109/TNSM.2020.3007535
    [14] HUSSAIN F, HASSAN S A, HUSSAIN R, et al. Machine learning for resource management in cellular and IoT networks: Potentials, current solutions, and open challenges[J]. IEEE Communications Surveys & Tutorials, 2020, 22(2): 1251–1275.
    [15] BURG A, CHATTOPADHYAY A, and LAM K Y. Wireless communication and security issues for cyber–physical systems and the Internet-of-things[J]. Proceedings of the IEEE, 2018, 106(1): 38–60. doi: 10.1109/JPROC.2017.2780172
    [16] 杨挺, 刘佳林, 张亚健, 等. 电力线载波通信时频混合降噪方法[J]. 电网技术, 2018, 42(10): 3153–3160.

    YANG Ting, LIU Jialin, ZHANG Yajian, et al. Noise reduction method for lv power line carrier communication[J]. Power System Technology, 2018, 42(10): 3153–3160.
    [17] BARAKABITZE A A, AHMAD A, MIJUMBI R, et al. 5G network slicing using SDN and NFV: A survey of taxonomy, architectures and future challenges[J]. Computer Networks, 2020, 167: 106984. doi: 10.1016/j.comnet.2019.106984
    [18] WANG Tian, KE Haoxiong, ZHENG Xi, et al. Big data cleaning based on mobile edge computing in industrial sensor-cloud[J]. IEEE Transactions on Industrial Informatics, 2020, 16(2): 1321–1329. doi: 10.1109/TII.2019.2938861
    [19] SCHIZAS I D. Online data dimensionality reduction and reconstruction using graph filtering[J]. IEEE Transactions on Signal Processing, 2020, 68: 3871–3886. doi: 10.1109/TSP.2020.3003423
    [20] 刘向锋, 黄庚华, 张志杰, 等. 高分七号激光测高中全波形回波数据的EMD降噪[J]. 红外与激光工程, 2020, 49(11): 20200261. doi: 10.3788/IRLA20200261

    LIU Xiangfeng, HUANG Genghua, ZHANG Zhijie, et al. Noise reduction based on empirical mode decomposition for full waveforms data of GaoFen-7 laser altimetry[J]. Infrared and Laser Engineering, 2020, 49(11): 20200261. doi: 10.3788/IRLA20200261
    [21] ZHOU Huibin, ZHANG Dafang, and XIE Kun. Accurate traffic matrix completion based on multi-Gaussian models[J]. Computer Communications, 2017, 102: 165–176. doi: 10.1016/j.comcom.2016.11.011
    [22] 杨挺, 何周泽, 赵东艳, 等. 基于FSOM神经网络的电能质量数据缺失修复算法[J]. 电网技术, 2020, 44(5): 1941–1949.

    YANG Ting, HE Zhouze, ZHAO Dongyan, et al. Power quality data loss repair algorithm based on FSOM neural network[J]. Power System Technology, 2020, 44(5): 1941–1949.
    [23] 杨挺, 李扬, 何周泽, 等. 基于矩阵填充的泛在电力物联网电能质量数据修复算法[J]. 电力系统自动化, 2020, 44(2): 13–21. doi: 10.7500/AEPS20190814007

    YANG Ting, LI Yang, HE Zhouze, et al. Matrix completion theory based recovery algorithm for power quality data in ubiquitous power internet of things[J]. Automation of Electric Power Systems, 2020, 44(2): 13–21. doi: 10.7500/AEPS20190814007
    [24] ZHANG Linxia, NIU Dunbiao, SONG Enbin, et al. Joint optimization of dimension assignment and compression in distributed estimation fusion[J]. IEEE Transactions on Signal Processing, 2019, 67(9): 2453–2468. doi: 10.1109/TSP.2019.2904935
    [25] ZHAO Hongshan and MA Libo. Power distribution system stream data compression based on incremental tensor decomposition[J]. IEEE Transactions on Industrial Informatics, 2020, 16(4): 2469–2476. doi: 10.1109/TII.2019.2934766
    [26] 杨挺, 武金成, 袁博. 谐波和间谐波检测的压缩感知恢复算法[J]. 中国电机工程学报, 2015, 35(21): 5475–5482.

    YANG Ting, WU Jincheng, and YUAN Bo. The restoration algorithm of compressed sensing to detect harmonic and inter-harmonic[J]. Proceedings of the CSEE, 2015, 35(21): 5475–5482.
    [27] ALAM F, MEHMOOD R, KATIB I, et al. Data fusion and IoT for smart ubiquitous environments: A survey[J]. IEEE Access, 2017, 5: 9533–9554. doi: 10.1109/ACCESS.2017.2697839
    [28] YIN Xiuxing and ZHAO Xiaowei. Deep neural learning based distributed predictive control for offshore wind farm using high-fidelity LES data[J]. IEEE Transactions on Industrial Electronics, 2021, 68(4): 3251–3261. doi: 10.1109/TIE.2020.2979560
    [29] LAI Jingang, LU Xiaoqing, YU Xinghuo, et al. Distributed voltage regulation for cyber-physical microgrids with coupling delays and slow switching topologies[J]. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2020, 50(1): 100–110. doi: 10.1109/TSMC.2019.2924612
    [30] YATES R D and KAUL S K. The age of information: Real-time status updating by multiple sources[J]. IEEE Transactions on Information Theory, 2019, 65(3): 1807–1827. doi: 10.1109/TIT.2018.2871079
    [31] HUANG Xin and DONG Jiuxiang. Reliable control policy of cyber-physical systems against a class of frequency-constrained sensor and actuator attacks[J]. IEEE Transactions on Cybernetics, 2018, 48(12): 3432–3439. doi: 10.1109/TCYB.2018.2815758
    [32] YANG Ting, ZHANG Yajian, LI Wei, et al. Decentralized networked load frequency control in interconnected power systems based on stochastic jump system theory[J]. IEEE Transactions on Smart Grid, 2020, 11(5): 4427–4439. doi: 10.1109/TSG.2020.2978029
    [33] FARIVAR F, HAGHIGHI M S, JOLFAEI A, et al. Artificial intelligence for detection, estimation, and compensation of malicious attacks in nonlinear cyber-physical systems and industrial IoT[J]. IEEE Transactions on Industrial Informatics, 2020, 16(4): 2716–2725. doi: 10.1109/TII.2019.2956474
    [34] 赖英旭, 刘岩, 刘静. 一种网络间可信连接协议[J]. 软件学报, 2019, 30(12): 3730–3749.

    LAI Yingxu, LIU Yan, and LIU Jing. Trusted connection protocol between networks[J]. Journal of Software, 2019, 30(12): 3730–3749.
    [35] 王星, 杜伟, 陈吉, 等. 基于深度残差生成式对抗网络的样本生成方法[J]. 控制与决策, 2020, 35(8): 1887–1894.

    WANG Xing, DU Wei, CHEN Ji, et al. Sample generation based on residual generative adversarial network[J]. Control and Decision, 2020, 35(8): 1887–1894.
    [36] JIANG Yuchen, YIN Shen, and KAYNAK O. Data-driven monitoring and safety control of industrial cyber-physical systems: Basics and beyond[J]. IEEE Access, 2018, 6: 47374–47384. doi: 10.1109/ACCESS.2018.2866403
    [37] 杨挺, 侯昱丞, 赵黎媛, 等. 基于时-频域混合特征的变电站通信网异常流量检测方法[J]. 电力系统自动化, 2020, 44(16): 79–86.

    YANG Ting, HOU Yucheng, ZHAO Liyuan, et al. Abnormal traffic detection method of substation communication network based on time-frequency domain mixed features[J]. Automation of Electric Power Systems, 2020, 44(16): 79–86.
    [38] DING Derui, HAN Qinglong, GE Xiaohua, et al. Secure state estimation and control of cyber-physical systems: A survey[J]. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2021, 51(1): 176–190. doi: 10.1109/TSMC.2020.3041121
    [39] HUANG Jiangshuai, WANG Wei, WEN Changyun, et al. Adaptive event-triggered control of nonlinear systems with controller and parameter estimator triggering[J]. IEEE Transactions on Automatic Control, 2020, 65(1): 318–324. doi: 10.1109/TAC.2019.2912517
    [40] HU Songlin, YUE Dong, XIE Xiangpeng, et al. Resilient Event-triggered controller synthesis of networked control systems under periodic DoS Jamming attacks[J]. IEEE Transactions on Cybernetics, 2019, 49(12): 4271–4281. doi: 10.1109/TCYB.2018.2861834
    [41] 王晓辉, 季知祥, 周扬, 等. 城市能源互联网综合服务平台架构及关键技术[J]. 中国电机工程学报, 2021, 41(7): 2310–2320.

    WANG Xiaohui, JI Zhixiang, ZHOU Yang, et al. Comprehensive service platform architecture and key technologies of urban energy internet[J]. Proceedings of the CSEE, 2021, 41(7): 2310–2320.
    [42] BORDEL B, DE RIVERA D S, and ALCARRIA R. Plug-and-Play transducers in cyber-physical systems for device-driven applications[C]. Proceedings of the 10th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Fukuoka, Japan, 2016.
    [43] BALASUBRAMANIYAN S, SUBATHRA B, HEMESH R C, et al. On simulating processor schedules and network protocols within CPS using TrueTime[C]. Proceedings of 2015 IEEE International Conference on Computational Intelligence and Computing Research, Madurai, India, 2015.
    [44] KHALED A B, GAID M B, PERNET N, et al. Fast multi-core co-simulation of cyber-physical systems: Application to internal combustion engines[J]. Simulation Modelling Practice and Theory, 2014, 47: 79–91. doi: 10.1016/j.simpat.2014.05.002
    [45] 汤奕, 王琦, 邰伟, 等. 基于OPAL-RT和OPNET的电力信息物理系统实时仿真[J]. 电力系统自动化, 2016, 40(23): 15–21,92. doi: 10.7500/AEPS20160515020

    TANG Yi, WANG Qi, TAI Wei, et al. Real-time simulation of cyber-physical power system based on OPAL-RT and OPNET[J]. Automation of Electric Power Systems, 2016, 40(23): 15–21,92. doi: 10.7500/AEPS20160515020
    [46] VICENTINI F, ASKARPOUR M, ROSSI M G, et al. Safety assessment of collaborative robotics through automated formal verification[J]. IEEE Transactions on Robotics, 2020, 36(1): 42–61. doi: 10.1109/TRO.2019.2937471
    [47] BRAT G, BUSHNELL D, DAVIES M, et al. Verifying the safety of a flight-critical system[C]. Proceedings of the 20th International Symposium on Formal Methods, Oslo, Norway, 2015.
    [48] 唐伦, 肖娇, 魏延南, 等. 基于云雾混合计算的车联网联合资源分配算法[J]. 电子与信息学报, 2020, 42(8): 1926–1933. doi: 10.11999/JEIT190306

    TANG Lun, XIAO Jiao, WEI Yannan, et al. Joint resource allocation algorithms based on mixed cloud/fog computing in vehicular network[J]. Journal of Electronics &Information Technology, 2020, 42(8): 1926–1933. doi: 10.11999/JEIT190306
    [49] WANG Xiaokang, YANG L T, XIE Xia, et al. A cloud-edge computing framework for cyber-physical-social services[J]. IEEE Communications Magazine, 2017, 55(11): 80–85. doi: 10.1109/MCOM.2017.1700360
    [50] DING Kai, CHAN F T S, ZHANG Xudong, et al. Defining a digital twin-based cyber-physical production system for autonomous manufacturing in smart shop floors[J]. International Journal of Production Research, 2019, 57(20): 6315–6334. doi: 10.1080/00207543.2019.1566661
    [51] LI Wei, YANG Ting, DELICATO F C, et al. On enabling sustainable edge computing with renewable energy resources[J]. IEEE Communications Magazine, 2018, 56(5): 94–101. doi: 10.1109/MCOM.2018.1700888
    [52] 李杰, 李响, 许元铭, 等. 工业人工智能及应用研究现状及展望[J]. 自动化学报, 2020, 46(10): 2031–2044.

    LEE J, LI Xiang, XU Yuanming, et al. Recent advances and prospects in industrial AI and applications[J]. Acta Automatica Sinica, 2020, 46(10): 2031–2044.
    [53] KIM H and BEN-OTHMAN J. Toward integrated virtual emotion system with AI applicability for secure CPS-enabled smart cities: AI-based research challenges and security issues[J]. IEEE Network, 2020, 34(3): 30–36. doi: 10.1109/MNET.011.1900299
    [54] ZHAO Wenbing, JIANG Congfeng, GAO Honghao, et al. Blockchain-enabled cyber-physical systems: A review[J]. IEEE Internet of Things Journal, 2021, 8(6): 4023–4034. doi: 10.1109/JIOT.2020.3014864
    [55] ZHENG Pai, WANG Honghui, SANG Zhiqian, et al. Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives[J]. Frontiers of Mechanical Engineering, 2018, 13(2): 137–150. doi: 10.1007/s11465-018-0499-5
    [56] TAO Fei, ZHANG He, LIU Ang, et al. Digital twin in industry: State-of-the-art[J]. IEEE Transactions on Industrial Informatics, 2019, 15(4): 2405–2415. doi: 10.1109/TII.2018.2873186
    [57] 陈胜, 卫志农, 顾伟, 等. 碳中和目标下的能源系统转型与变革: 多能流协同技术[J]. 电力自动化设备, 2021, 41(9): 3–12.

    CHEN Sheng, WEI Zhinong, GU Wei, et al. Carbon neutral oriented transition and revolution of energy systems: Multi-energy flow coordination technology[J]. Electric Power Automation Equipment, 2021, 41(9): 3–12.
    [58] MONESS M and MOUSTAFA A M. A survey of cyber-physical advances and challenges of wind energy conversion systems: Prospects for internet of energy[J]. IEEE Internet of Things Journal, 2016, 3(2): 134–145. doi: 10.1109/JIOT.2015.2478381
    [59] 王成山, 吕超贤, 李鹏, 等. 园区型综合能源系统多时间尺度模型预测优化调度[J]. 中国电机工程学报, 2019, 39(23): 6791–6803.

    WANG Chengshan, LÜ Chaoxian, LI Peng, et al. Multiple time-scale optimal scheduling of community integrated energy system based on model predictive control[J]. Proceedings of the CSEE, 2019, 39(23): 6791–6803.
    [60] KONG Weicong, DONG Zhaoyang, JIA Youwei, et al. Short-term residential load forecasting based on LSTM recurrent neural network[J]. IEEE Transactions on Smart Grid, 2019, 10(1): 841–851. doi: 10.1109/TSG.2017.2753802
    [61] HERRANDO M, PANTALEO A M, WANG Kai, et al. Solar combined cooling, heating and power systems based on hybrid PVT, PV or solar-thermal collectors for building applications[J]. Renewable Energy, 2019, 143: 637–647. doi: 10.1016/j.renene.2019.05.004
    [62] 王成山, 董博, 于浩, 等. 智慧城市综合能源系统数字孪生技术及应用[J]. 中国电机工程学报, 2021, 41(5): 1597–1607.

    WANG Chengshan, DONG Bo, YU Hao, et al. Digital twin technology and its application in the integrated energy system of smart city[J]. Proceedings of the CSEE, 2021, 41(5): 1597–1607.
    [63] 陈健, 林咨良, 赵浩然, 等. 考虑信息耦合的电–气综合能源系统韧性优化方法[J]. 中国电机工程学报, 2020, 40(21): 6854–6863.

    CHEN Jian, LIN Ziliang, ZHAO Haoran, et al. Optimization method for resilience of integrated electric-gas system with consideration of cyber coupling[J]. Proceedings of the CSEE, 2020, 40(21): 6854–6863.
    [64] TOKODY D, ALBINI A, ADY L, et al. Safety and security through the design of autonomous intelligent vehicle systems and intelligent infrastructure in the smart city[J]. Interdisciplinary Description of Complex Systems, 2018, 16(3): 384–396. doi: 10.7906/indecs.16.3.11
    [65] 王同军. 中国智能高铁发展战略研究[J]. 中国铁路, 2019(1): 9–14.

    WANG Tongjun. Study on the development strategy of China intelligent high speed railway[J]. China Railway, 2019(1): 9–14.
    [66] 夏元清, 闫策, 王笑京, 等. 智能交通信息物理融合云控制系统[J]. 自动化学报, 2019, 45(1): 132–142.

    XIA Yuanqing, YAN Ce, WANG Xiaojing, et al. Intelligent transportation cyber-physical cloud control systems[J]. Acta Automatica Sinica, 2019, 45(1): 132–142.
    [67] CHEN Chen, LIU Bin, WAN Shaohua, et al. An edge traffic flow detection scheme based on deep learning in an intelligent transportation system[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(3): 1840–1852. doi: 10.1109/TITS.2020.3025687
    [68] HATZIVASILIS G, FYSARAKIS K, IOANNIDIS S, et al. SPD-Safe: Secure administration of railway intelligent transportation systems[J]. Electronics, 2021, 10(1): 92. doi: 10.3390/electronics10010092
    [69] KAVALLIERATOS G, DIAMANTOPOULOU V, and KATSIKAS S. Shipping 4.0: Security requirements for the cyber-enabled ship[J]. IEEE Transactions on Industrial Informatics, 2020, 16(10): 6617–6625. doi: 10.1109/TII.2020.2976840
    [70] YANG Geng, PANG Zhibo, DEEN M J, et al. Homecare robotic systems for healthcare 4.0: Visions and enabling technologies[J]. IEEE Journal of Biomedical and Health Informatics, 2020, 24(9): 2535–2549. doi: 10.1109/JBHI.2020.2990529
    [71] GUO Jiuchuan, TIAN Shulin, XIAN Hong, et al. Cyber-physical healthcare system with blood test module on broadcast television network for remote cardiovascular disease (CVD) management[J]. IEEE Transactions on Industrial Informatics, 2021, 17(5): 3363–3670.
    [72] JIANG Yu, SONG Houbing, WANG Rui, et al. Data-centered runtime verification of wireless medical cyber-physical system[J]. IEEE Transactions on Industrial Informatics, 2017, 13(4): 1900–1909. doi: 10.1109/TII.2016.2573762
    [73] BAYANBAY N A, BEISEMBETOV I K, OZHIKENOV K A, et al. The use of unmanned aerial vehicle for emergency medical assistance[C]. Proceedings of the 20th International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices (EDM), Erlagol, Russia, 2019.
    [74] 孔祥朋, 付君, 陈继营, 等. 5G通信技术远程指导机器人辅助全髋关节置换术两例[J]. 中国修复重建外科杂志, 2020, 34(11): 1492–1493.

    KONG Xiangpeng, FU Jun, CHEN Jiying, et al. Two cases of robot assisted total HIP arthroplasty guided by 5G communication technology[J]. Chinese Journal of Reparative and Reconstructive Surgery, 2020, 34(11): 1492–1493.
    [75] 王婷, 钱东福, 张成, 等. 基于史密斯模型的新疆克州远程医疗建设效果分析[J]. 中国卫生事业管理, 2021, 38(8): 582–586.

    WANG Ting, QIAN Dongfu, ZHANG Cheng, et al. Analysis of the effect of telemedicine construction in Kezhou of Xinjiang province based on Smith model[J]. Chinese Health Service Management, 2021, 38(8): 582–586.
    [76] XU Qichao, SU Zhou, and YU Shui. Green social CPS based E-healthcare systems to control the spread of infectious diseases[C]. Proceedings of 2018 IEEE International Conference on Communications (ICC 2018), Kansas City, USA, 2018.
    [77] MONES E, STOPCZYNSKI A, PENTLAND A S, et al. Optimizing targeted vaccination across cyber-physical networks: An empirically based mathematical simulation study[J]. Journal of the Royal Society Interface, 2018, 15(138): 20170783. doi: 10.1098/rsif.2017.0783
    [78] DIMITROV V, JAGTAP V, SKORINKO J, et al. Human-centered design of a cyber-physical system for advanced response to Ebola (CARE)[C]. Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Milan, Italy, 2015: 6856–6859.
    [79] DONG Yudi and YAO Yudong. IoT platform for COVID-19 prevention and control: A survey[J]. IEEE Access, 2021, 9: 49929–49941. doi: 10.1109/ACCESS.2021.3068276
    [80] YE Ruizhong, ZHOU Xianlong, SHAO Fei, et al. Feasibility of a 5G-based robot-assisted remote ultrasound system for cardiopulmonary assessment of patients with coronavirus disease 2019[J]. Chest, 2021, 159(1): 270–281. doi: 10.1016/j.chest.2020.06.068
    [81] YE Qing, ZHOU Jin, and WU Hong. Using information technology to manage the COVID-19 pandemic: Development of a technical framework based on practical experience in China[J]. JMIR Medical Informatics, 2020, 8(6): e19515. doi: 10.2196/19515
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  • 收稿日期:  2021-10-01
  • 修回日期:  2021-10-15
  • 网络出版日期:  2021-10-20
  • 刊出日期:  2021-12-10

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