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Author:

Li, Xin (Li, Xin.) (Scholars:李欣) | Shen, Yuanfei (Shen, Yuanfei.) | Cheng, Haolun (Cheng, Haolun.) | Yuan, Fei (Yuan, Fei.) | Huang, Lucheng (Huang, Lucheng.)

Indexed by:

SSCI EI Scopus SCIE

Abstract:

Digital twin is increasingly prominent for realizing the digital and intelligent transformation of various industries as an emerging technological means to connect the physical and virtual world. While there has been a recent growth of interest in digital twin in industry, finance, and academia, most relevant studies lack a systematic analysis of the status quo, development trends, and technological competition situations for digital twin. In this article, we used bibliometrics and patent analysis to conduct comprehensive and in-depth research of digital twin by reviewing the current status of academic research and technological development, distribution of countries and institutions, and technological competition situations. We found that academic research and technological development in digital twin are currently in the early stages of rapid growth, which is radiating from applications in smart manufacturing to other scenarios such as medical and health, smart cities, energy, transportation, public emergency, and agricultural food. Artificial intelligence technology, digital twin integrated architecture and system, intelligent real-time control have gradually become the key topics of academic research and technology research and development in the field of digital twin in recent years. The digital framework, sustainable digital twin, deep learning and neural network algorithms, and full lifecycle management have the potential to become technology development trends. USA and Germany are the technology leaders and occupy first-mover advantage at present, while China, the U.K., and South Korea are the powerful chasers in the future.

Keyword:

Hidden Markov models Bibliometrics Digital twin evolutionary trends competitive situation Industries Patents Market research digital twin patent analysis Analytical models Bibliometric analysis

Author Community:

  • [ 1 ] [Li, Xin]Beijing Univ Technol, Coll Management & Econ, Beijing 100124, Peoples R China
  • [ 2 ] [Shen, Yuanfei]Beijing Univ Technol, Coll Management & Econ, Beijing 100124, Peoples R China
  • [ 3 ] [Yuan, Fei]Beijing Univ Technol, Coll Management & Econ, Beijing 100124, Peoples R China
  • [ 4 ] [Huang, Lucheng]Beijing Univ Technol, Coll Management & Econ, Beijing 100124, Peoples R China
  • [ 5 ] [Cheng, Haolun]Beijing Inst Graph Commun, Coll New Media, Beijing 102600, Peoples R China

Reprint Author's Address:

  • [Li, Xin]Beijing Univ Technol, Coll Management & Econ, Beijing 100124, Peoples R China;;

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Source :

IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT

ISSN: 0018-9391

Year: 2022

Volume: 71

Page: 1998-2021

5 . 8

JCR@2022

5 . 8 0 0

JCR@2022

ESI Discipline: ECONOMICS & BUSINESS;

ESI HC Threshold:44

JCR Journal Grade:2

CAS Journal Grade:3

Cited Count:

WoS CC Cited Count: 10

SCOPUS Cited Count: 14

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 11

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