Digital twin data: methods and key technologies [version 2; peer review: 4 approved]
As a promising technology to converge the traditional industry with the digital economy, digital twin (DT) is being investigated by researchers and practitioners across many different fields. The importance of data to DT cannot be overstated. Data plays critical roles in constructing virtual models,...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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F1000 Research Ltd
2022-02-01
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| Series: | Digital Twin |
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| Online Access: | https://digitaltwin1.org/articles/1-2/v2 |
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| _version_ | 1846126268039299072 |
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| author | Ang Liu Meng Zhang Lihui Wang Fei Tao Biqing Huang A. Y. C. Nee Nabil Anwer |
| author_facet | Ang Liu Meng Zhang Lihui Wang Fei Tao Biqing Huang A. Y. C. Nee Nabil Anwer |
| author_sort | Ang Liu |
| collection | DOAJ |
| description | As a promising technology to converge the traditional industry with the digital economy, digital twin (DT) is being investigated by researchers and practitioners across many different fields. The importance of data to DT cannot be overstated. Data plays critical roles in constructing virtual models, building cyber-physical connections, and executing intelligent operations. The unique characteristics of DT put forward a set of new requirements on data. Against this background, this paper discusses the emerging requirements on DT-related data with respect to data gathering, interaction, universality, mining, fusion, iterative optimization, and on-demand usage. A new notion, namely digital twin data (DTD), is introduced. This paper explores some basic principles and methods for DTD gathering, interaction, storage, association, fusion, evolution and servitization, as well as the key enabling technologies. Based on the theoretical underpinning provided in this paper, it is expected that more DT researchers and practitioners can incorporate DTD into their DT development process. |
| format | Article |
| id | doaj-art-2df8e1e3251540dfb61c2b5ac8ec00ba |
| institution | Kabale University |
| issn | 2752-5783 |
| language | English |
| publishDate | 2022-02-01 |
| publisher | F1000 Research Ltd |
| record_format | Article |
| series | Digital Twin |
| spelling | doaj-art-2df8e1e3251540dfb61c2b5ac8ec00ba2024-12-13T01:00:13ZengF1000 Research LtdDigital Twin2752-57832022-02-01118840Digital twin data: methods and key technologies [version 2; peer review: 4 approved]Ang Liu0https://orcid.org/0000-0001-9353-0948Meng Zhang1https://orcid.org/0000-0002-2253-4025Lihui Wang2https://orcid.org/0000-0001-8679-8049Fei Tao3https://orcid.org/0000-0002-9020-0633Biqing Huang4A. Y. C. Nee5Nabil Anwer6School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW, 2052, AustraliaDepartment of Automation, Tsinghua University, Beijing, 100084, ChinaDepartment of Production Engineering, KTH Royal Institute of Technology, Stockholm, SE-10044, SwedenSchool of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, ChinaDepartment of Automation, Tsinghua University, Beijing, 100084, ChinaDepartment of Mechanical Engineering, National University of Singapore, Singapore, 117576, SingaporeAutomated Production Research Laboratory, Paris-Saclay University, ENS Paris-Saclay, LURPA, 91190, Gif-sur-Yvette, FranceAs a promising technology to converge the traditional industry with the digital economy, digital twin (DT) is being investigated by researchers and practitioners across many different fields. The importance of data to DT cannot be overstated. Data plays critical roles in constructing virtual models, building cyber-physical connections, and executing intelligent operations. The unique characteristics of DT put forward a set of new requirements on data. Against this background, this paper discusses the emerging requirements on DT-related data with respect to data gathering, interaction, universality, mining, fusion, iterative optimization, and on-demand usage. A new notion, namely digital twin data (DTD), is introduced. This paper explores some basic principles and methods for DTD gathering, interaction, storage, association, fusion, evolution and servitization, as well as the key enabling technologies. Based on the theoretical underpinning provided in this paper, it is expected that more DT researchers and practitioners can incorporate DTD into their DT development process.https://digitaltwin1.org/articles/1-2/v2digital twin (DT) digital twin data (DTD) principles methods key technologieseng |
| spellingShingle | Ang Liu Meng Zhang Lihui Wang Fei Tao Biqing Huang A. Y. C. Nee Nabil Anwer Digital twin data: methods and key technologies [version 2; peer review: 4 approved] Digital Twin digital twin (DT) digital twin data (DTD) principles methods key technologies eng |
| title | Digital twin data: methods and key technologies [version 2; peer review: 4 approved] |
| title_full | Digital twin data: methods and key technologies [version 2; peer review: 4 approved] |
| title_fullStr | Digital twin data: methods and key technologies [version 2; peer review: 4 approved] |
| title_full_unstemmed | Digital twin data: methods and key technologies [version 2; peer review: 4 approved] |
| title_short | Digital twin data: methods and key technologies [version 2; peer review: 4 approved] |
| title_sort | digital twin data methods and key technologies version 2 peer review 4 approved |
| topic | digital twin (DT) digital twin data (DTD) principles methods key technologies eng |
| url | https://digitaltwin1.org/articles/1-2/v2 |
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