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Journal of Library and Information Science in Agriculture

   

Human-Intelligent Information System Collaboration in Digital Twin Environment: Value Proposition, Key Technologies, and Practical Approaches

Fangrui BAI1,2, Shaobo LIANG1,3, Dan WU1,3(), Yuheng REN4, Fan YANG4   

  1. 1. School of Information Management, Wuhan University, Wuhan 430072
    2. National Demonstration Center for Experimental Library and Information Science Education, Wuhan 430072
    3. Center for Studies of Human-Computer Interaction and User Behavior, Wuhan University, Wuhan 430072
    4. Xiamen Kunlu IoT Information Technology Co. , Ltd, Xiamen 361000
  • Received:2024-06-09 Online:2024-10-24
  • Contact: Dan WU

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Abstract:

[Purpose/Significance] Previous studies or reviews of digital twins have focused either on conceptual analysis and theoretical models, or on the current state of the art and implementation, with only a few studies analyzing human-digital twin interaction and collaboration. This paper explores the collaboration between human and digital twin systems, and offers recommendations on how digital twins can catalyze the progress of societal digitization. It envisions a future where the interaction and collaboration between humans and digital twins is not only deepened but also transcended, moving closer to the harmonious integration of man and machine. The proposed strategies aim to unlock the full potential of digital twins in promoting a more connected and intelligent world. [Method/Process] This paper is a systematic literature review focusing on the partnership between human and digital twin systems, emphasizing the role of artificial intelligence. We analyze the value positioning, key technologies and practical applications through 45 papers from home and abroad. Then, we explore the construction path of digital twin technology-enabled information resource management. [Results/ [Conclusions] The study shows that human and digital twin systems have different unique values in the whole ecology, the irreplaceable wisdom of the human brain is reflected in innovation and decision-making, and the core function of the digital twin system is to support and enhance the communication between man and machine. AI technology plays the role of the pedestal in the interaction. There are common enabling technologies for human intelligence collaboration in the digital twin ecosystem, and the types of key technologies supporting human intelligence collaboration in different twin modules are not identical but synergistic with each other. The supporting technologies in digital twin environment mainly involve data acquisition and data transmission, model twin mainly lies in data fusion and management, image recognition and processing, and process twin mainly involves human-computer interfaces, immersive perception and other key technologies. In terms of application areas, human-intelligent information system cooperation at home and abroad has rich applications in industry, healthcare, smart cities and public cultural services, especially in public cultural services, where cooperation has accelerated the intelligentization process of public cultural service institutions. Finally, the study categorizes the human-intelligent information system collaboration methods in the digital twin ecology into three types: pre-determined, collaborative, and autonomous, proposes a holistic framework for the twin co-intelligence system of intelligent connections, and summarizes the current obstacles and development strategies from a realistic perspective. There are some limitations due to the limited samples, which can be increased in the future to deepen the mining and analysis to optimize the form of human-intelligent information system collaboration in the digital twin ecosystem.

Key words: digital twins, human-intelligent information system collaboration, artificial intelligence, human-centered

CLC Number: 

  • TP391.9

Table 1

Retrieval method and results"

数据库 检索式 检索结果/篇
WOS核心集 TS=(digital twin) AND (TS=((AI) OR (artificial intelligence))) AND (TS=((human-computer collaboration) OR (user))) 317
Scopus (TITLE-ABS-KEY ("digital twin") AND TITLE-ABS-KEY ("AI" OR " artificial intelligence") AND TITLE-ABS-KEY ("HCI" OR " human-computer collaboration " OR "user")) 269
CNKI (TKA%='数字孪生') AND TKA%=('人机协作'+'用户') AND TKA%=('人智协作'+'人工智能'+'协同智能') 179

Fig.1

Literature retrieval process"

Table 2

Literature information extracted"

抽取信息 信息范围 研究内容
基础信息 标题、作者、年份、第一作者国家 情况概览
文献类型 期刊、会议、书籍 情况概览
研究对象 文章研究的重点内容 情况概览
人类的职能定位 数字孪生生态人智协作中人类的独特价值、职能定位 RQ1
数字孪生系统的职能定位 数字孪生生态人智协作中数字孪生系统的独特价值、职能定位 RQ1
人工智能的作用 人工智能技术发挥的关键作用 RQ1
关键技术 支撑数字孪生生态人智协作的技术类型 RQ2
人智协作场景 数字孪生生态人智协作实践场景 RQ3
人智协作方式 数字孪生生态人智协作方式 RQ3

Fig.2

Artificial intelligence-enabled collaboration between human and digital twin"

Fig.3

Types of key technologies in different twin modules"

Table 3

Practices in different domains"

行业领域 数字孪生人智协作应用
工业 生产计划控制、交互式操作指引、工人安全保障
医疗保健 患者诊断与护理、医院设计与管理
智慧城市 城市规划设计、交通和环境管控、建筑安全管理
公共文化服务 沉浸式知识服务、机构场馆管理、文化遗产保护

Fig.4

Collaboration approaches in the digital twin ecology"

Fig.5

Twin co-intelligence framework diagram"

1
TUEGEL E J, INGRAFFEA A R, EASON T G, et al. Reengineering aircraft structural life prediction using a digital twin[J]. International journal of aerospace engineering, 2011, 2011(1): 154798.
2
KASEY P. Gartners top 10 technology trends 2017[EB/OL]. (2016-10-18)[2024-04-17].
3
KASEY P. Gartner top 10 strategic technology trends for 2018[EB/OL]. (2017-10-03)[2024-04-17].
4
KASEY P. Gartner top 10 strategic technology trends for 2019[EB/OL]. (2018-10-15)[2024-04-17].
5
吴江, 曹喆, 陈佩, 等. 元宇宙视域下的用户信息行为: 框架与展望[J]. 信息资源管理学报, 2022, 12(1): 4-20.
WU J, CAO Z, CHEN P, et al. Users' information behavior from the perspective of metaverse: Framework and prospect[J]. Journal of information resources management, 2022, 12(1): 4-20.
6
陶飞, 刘蔚然, 张萌, 等. 数字孪生五维模型及十大领域应用[J]. 计算机集成制造系统, 2019, 25(1): 1-18.
TAO F, LIU W R, ZHANG M, et al. Five-dimension digital twin model and its ten applications[J]. Computer integrated manufacturing systems, 2019, 25(1): 1-18.
7
ENDERS M R, HOßBACH N. Dimensions of digital twin applications - A literature review[C]//Cancun: Twenty-fifth Americas Conference on Information Systems, 2019.
8
KAUL R, OSSAI C, FORKAN A R M, et al. The role of AI for developing digital twins in healthcare: The case of cancer care[J]. Wiley interdisciplinary reviews: Data mining and knowledge discovery, 2023, 13(1): e1480.
9
SONG H H, YANG G, LI H J, et al. Digital twin enhanced BIM to shape full life cycle digital transformation for bridge engineering[J]. Automation in construction, 2023, 147: 104736.
10
WANG H, CHEN X W, JIA F, et al. Digital twin-supported smart city: Status, challenges and future research directions[J]. Expert systems with applications, 2023, 217: 119531.
11
LIU Z Y, HANSEN D W, CHEN Z Y. Leveraging digital twins to support industrial symbiosis networks: A case study in the Norwegian wood supply chain collaboration[J]. Sustainability, 2023, 15(3): 2647.
12
庄存波, 刘检华, 熊辉, 等. 产品数字孪生体的内涵、体系结构及其发展趋势[J]. 计算机集成制造系统, 2017, 23(4): 753-768.
ZHUANG C B, LIU J H, XIONG H, et al. Connotation, architecture and trends of product digital twin[J]. Computer integrated manufacturing systems, 2017, 23(4): 753-768.
13
陶飞, 张贺, 戚庆林, 等. 数字孪生模型构建理论及应用[J]. 计算机集成制造系统, 2021, 27(1): 1-15.
TAO F, ZHANG H, QI Q L, et al. Theory of digital twin modeling and its application[J]. Computer integrated manufacturing systems, 2021, 27(1): 1-15.
14
KRITZINGER W, KARNER M, TRAAR G, et al. Digital Twin in manufacturing: A categorical literature review and classification[J]. IFAC-PapersOnLine, 2018, 51(11): 1016-1022.
15
陶飞, 程颖, 程江峰, 等. 数字孪生车间信息物理融合理论与技术[J]. 计算机集成制造系统, 2017, 23(8): 1603-1611.
TAO F, CHENG Y, CHENG J F, et al. Theories and technologies for cyber-physical fusion in digital twin shop-floor[J]. Computer integrated manufacturing systems, 2017, 23(8): 1603-1611.
16
张辰源, 陶飞. 数字孪生模型评价指标体系[J]. 计算机集成制造系统, 2021, 27(8): 2171-2186.
ZHANG C Y, TAO F. Evaluation index system for digital twin model[J]. Computer integrated manufacturing systems, 2021, 27(8): 2171-2186.
17
王昊琪, 李浩, 文笑雨, 等. 基于数字孪生的产品设计过程和工作量预测方法[J]. 计算机集成制造系统, 2022, 28(1): 17-30.
WANG H Q, LI H, WEN X Y, et al. Digital twin-based product design process and design effort prediction method[J]. Computer integrated manufacturing systems, 2022, 28(1): 17-30.
18
李浩, 陶飞, 王昊琪, 等. 基于数字孪生的复杂产品设计制造一体化开发框架与关键技术[J]. 计算机集成制造系统, 2019, 25(6): 1320-1336.
LI H, TAO F, WANG H Q, et al. Integration framework and key technologies of complex product design-manufacturing based on digital twin[J]. Computer integrated manufacturing systems, 2019, 25(6): 1320-1336.
19
TAO F, SUI F Y, LIU A, et al. Digital twin-driven product design framework[J]. International journal of production research, 2019, 57(12): 3935-3953.
20
WANG T, LI J K, DENG Y J, et al. Digital twin for human-machine interaction with convolutional neural network[J]. International journal of computer integrated manufacturing, 2021, 34(7/8): 888-897.
21
ERRANDONEA I, BELTRÁN S, ARRIZABALAGA S. Digital Twin for maintenance: A literature review[J]. Computers in industry, 2020, 123: 103316.
22
李海峰. 中美数字孪生研究主题的比较分析——兼论基于结构话题模型的文献主题数据挖掘方法[J]. 情报杂志, 2022, 41(1): 156-163.
LI H F. A comparative analysis of the topics about digital twin between China and U.S. - Also discussing research on data mining method of literature subject based on structural topic model[J]. Journal of intelligence, 2022, 41(1): 156-163.
23
陶飞, 张萌, 程江峰, 等. 数字孪生车间——一种未来车间运行新模式[J]. 计算机集成制造系统, 2017, 23(1): 1-9.
TAO F, ZHANG M, CHENG J F, et al. Digital twin workshop: A new paradigm for future workshop[J]. Computer integrated manufacturing systems, 2017, 23(1): 1-9.
24
苗田, 张旭, 熊辉, 等. 数字孪生技术在产品生命周期中的应用与展望[J]. 计算机集成制造系统, 2019, 25(6): 1546-1558.
MIAO T, ZHANG X, XIONG H, et al. Applications and expectation of digital twin in product lifecycle[J]. Computer integrated manufacturing systems, 2019, 25(6): 1546-1558.
25
秦晓珠, 张兴旺. 数字孪生技术在物质文化遗产数字化建设中的应用[J]. 情报资料工作, 2018, 39(2): 103-111.
QIN X Z, ZHANG X W. Application of digital twin technology in the digital construction of material cultural heritage[J]. Information and documentation services, 2018, 39(2): 103-111.
26
李浩, 刘根, 文笑雨, 等. 面向人机交互的数字孪生系统工业安全控制体系与关键技术[J]. 计算机集成制造系统, 2021, 27(2): 374-389.
LI H, LIU G, WEN X Y, et al. Industrial safety control system and key technologies of digital twin system oriented to human-machine interaction[J]. Computer integrated manufacturing systems, 2021, 27(2): 374-389.
27
杨赓, 周慧颖, 王柏村. 数字孪生驱动的智能人机协作: 理论、技术与应用[J]. 机械工程学报, 2022, 58(18): 279-291.
YANG G, ZHOU H Y, WANG B C. Digital twin-driven smart human-machine collaboration: Theory, enabling technologies and applications[J]. Journal of mechanical engineering, 2022, 58(18): 279-291.
28
BARRICELLI B R, FOGLI D. Digital twins in human-computer interaction: A systematic review[J]. International journal of human–computer interaction, 2024, 40(2): 79-97.
29
WILHELM J, PETZOLDT C, BEINKE T, et al. Review of digital twin-based interaction in smart manufacturing: Enabling cyber-physical systems for human-machine interaction[J]. International journal of computer integrated manufacturing, 2021, 34(10): 1031-1048.
30
PAGE M J, MCKENZIE J E, BOSSUYT P M, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews[J]. International journal of surgery, 2021, 88: 105906.
31
LIBERATI A, ALTMAN D G, TETZLAFF J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration[J]. Journal of clinical epidemiology, 2009, 62(10): e1-e34.
32
姜婷婷, 陈佩龙, 许艳闰. 国外心流理论应用研究进展[J]. 信息资源管理学报, 2021, 11(5): 4-16.
JIANG T T, CHEN P L, XU Y R. Research progress in the application of flow theory abroad[J]. Journal of information resources management, 2021, 11(5): 4-16.
33
张斌. 开源情报对国家情报工作制度创新的影响: 基于系统性文献综述视角[J]. 信息资源管理学报, 2021, 11(4): 60-69.
ZHANG B. The impact of open source intelligence on national intelligence work system innovation: A systematic literature review[J]. Journal of information resources management, 2021, 11(4): 60-69.
34
LATIF H, SHAO G D, STARLY B. A case study of digital twin for a manufacturing process involving human interactions[C]//2020 Winter Simulation Conference (WSC). Piscataway, New Jersey: IEEE, 2020: 2659-2670.
35
UBINA N A, LAN H Y, CHENG S C, et al. Digital twin-based intelligent fish farming with Artificial Intelligence Internet of Things (AIoT)[J]. Smart agricultural technology, 2023, 5: 100285.
36
王秉, 徐方廷, 曹春秀. 孪生应急: 数字孪生赋能应急管理的新范式[J]. 情报杂志, 2023, 42(11): 147-152.
WANG B, XU F T, CAO C X. Twin emergency: A new paradigm for digital twin enabling emergency management[J]. Journal of intelligence, 2023, 42(11): 147-152.
37
ALMATARED M, LIU H X, ABUDAYYEH O, et al. Digital-twin-based fire safety management framework for smart buildings[J]. Buildings, 2023, 14(1): 4.
38
CHEN J Y, YI C Y, DU H Y, et al. A revolution of personalized healthcare: Enabling human digital twin with mobile AIGC[J]. IEEE network, 2024, 99: 1.
39
KIM D B, BAJESTANI M S, SHAO G D, et al. Conceptual architecture of digital twin with human-in-the-loop-based smart manufacturing[C]//Volume 3: Advanced Manufacturing. American Society of Mechanical Engineers, 2023: V003T03A076.
40
LI C X, ZHENG P, LI S F, et al. AR-assisted digital twin-enabled robot collaborative manufacturing system with human-in-the-loop[J]. Robotics and computer-integrated manufacturing, 2022, 76: 102321.
41
YANES A R, ABBASI R, MARTINEZ P, et al. Digital twinning of hydroponic grow beds in intelligent aquaponic systems[J]. Sensors, 2022, 22(19): 7393.
42
PERUZZINI M, PRATI E, PELLICCIARI M. A framework to design smart manufacturing systems for Industry 5.0 based on the human-automation symbiosis[J]. International journal of computer integrated manufacturing, 2024, 37(10/11): 1426-1443.
43
肖飞, 张为华, 王东辉, 等. 数字孪生驱动的固体发动机总体设计体系架构与应用[J]. 计算机集成制造系统, 2019, 25(6): 1405-1418.
XIAO F, ZHANG W H, WANG D H, et al. System architecture and applications for overall design of solid rocket motor based on digital twin[J]. Computer integrated manufacturing systems, 2019, 25(6): 1405-1418.
44
TAO F, QI Q. Make more digital twins[J]. Nature, 2019, 573(7775): 490-491.
45
MADNI A M, MADNI C C, LUCERO S D. Leveraging digital twin technology in model-based systems engineering[J]. Systems, 2019, 7(1): 7.
46
BARRICELLI B R, CASIRAGHI E, FOGLI D. A survey on digital twin: Definitions, characteristics, applications, and design implications[J]. IEEE access, 2019, 7: 167653-167671.
47
FEDDOUL Y, RAGOT N, DUVAL F, et al. Exploring human-machine collaboration in industry: A systematic literature review of digital twin and robotics interfaced with extended reality technologies[J]. The international journal of advanced manufacturing technology, 2023, 129(5): 1917-1932.
48
杨晓楠, 房浩楠, 李建国, 等. 智能制造中的人-信息-物理系统协同的人因工程[J]. 中国机械工程, 2023, 34(14): 1710-1722, 1740.
YANG X N, FANG H N, LI J G, et al. Human factor engineering for human-cyber-physical system collaboration in intelligent manufacturing[J]. China mechanical engineering, 2023, 34(14): 1710-1722, 1740.
49
PAIRET È, ARDÓN P, LIU X K, et al. A digital twin for human-robot interaction[C]//2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). Piscataway, New Jersey: IEEE, 2019: 372.
50
ANGULO C, GONZALEZ-ABRIL L, RAYA C, et al. A proposal to evolving towards digital twins in healthcare[M]//Lecture Notes in Computer Science. Cham: Springer International Publishing, 2020: 418-426.
51
胡慧娟, 王明帮, 雷崎方, 等. 数字孪生医院: 改变医疗的未来[J]. 生物医学工程学杂志, 2024, 41(2): 376-382.
HU H J, WANG M B, LEI Q F, et al. Digital twin hospitals: Transforming the future of healthcare[J]. Journal of biomedical engineering, 2024, 41(2): 376-382.
52
LI X C, ZHANG S, ZHANG Q, et al. Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: A retrospective, multicohort, diagnostic study[J]. The lancet oncology, 2019, 20(2): 193-201.
53
VAMATHEVAN J, CLARK D, CZODROWSKI P, et al. Applications of machine learning in drug discovery and development[J]. Nature reviews drug discovery, 2019, 18(6): 463-477.
54
ANTUNES A. Designing a digital twin for adaptive serious games-based therapy[C]//Proceedings of the 22nd International Conference on Mobile and Ubiquitous Multimedia. New York: ACM, 2023: 574-576.
55
张捷, 钱虹, 周宏远. 数字孪生技术在社区老年人安全健康监测领域的应用探究[J]. 中国医疗器械杂志, 2019, 43(6): 410-413, 421.
ZHANG J, QIAN H, ZHOU H Y. Application and research of digital twin technology in safety and health monitoring of the elderly in community[J]. Chinese journal of medical instrumentation, 2019, 43(6): 410-413, 421.
56
LV Z H, QIAO L, LV H B. Cognitive computing for brain–computer interface-based computational social digital twins systems[J]. IEEE transactions on computational social systems, 2022, 9(6): 1635-1643.
57
BENJAMENS S, DHUNNOO P, MESKÓ B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: An online database[J]. NPJ digital medicine, 2020, 3: 118.
58
朱惠斌. 国内外数字孪生技术研究进展与实践展望[J]. 信息通信技术, 2022, 16(5): 75-80.
ZHU H B. International digital twin technology research and experience inspection[J]. Information and communications technologies, 2022, 16(5): 75-80.
59
ELFARRI E M, RASHEED A, SAN O. Artificial intelligence-driven digital twin of a modern house demonstrated in virtual reality[J]. IEEE access, 2023, 11: 35035-35058.
60
任萍萍. 5G技术驱动下的智慧图书馆应用场景与智慧平台模型构建[J]. 情报理论与实践, 2020, 43(7): 95-102.
REN P P. Application scenario and smart platform model construction of smart library driven by 5G technology[J]. Information studies: Theory & application, 2020, 43(7): 95-102.
61
石晶, 李红宇, 刘佳. 基于通用人工智能的公共图书馆服务的优化与创新[J]. 图书馆建设, 2024(2): 94-101.
SHI J, LI H Y, LIU J. Optimization and innovation of public library service based on artificial general intelligence[J]. Library development, 2024(2): 94-101.
62
石婷婷, 徐建华, 张雨浓. 数字孪生技术驱动下的智慧图书馆应用场景与体系架构设计[J]. 情报理论与实践, 2021, 44(3): 149-156.
SHI T T, XU J H, ZHANG Y N. Application scenario and model construction of smart library driven by digital twin technology[J]. Information studies: Theory & application, 2021, 44(3): 149-156.
63
MUKHERJEE D, GUPTA K, CHANG L H, et al. A survey of robot learning strategies for human-robot collaboration in industrial settings[J]. Robotics and computer-integrated manufacturing, 2022, 73: 102231.
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