数字孪生:数据探索、架构、实现与未来。

Digital twin: Data exploration, architecture, implementation and future.

作者信息

Dihan Md Shezad, Akash Anwar Islam, Tasneem Zinat, Das Prangon, Das Sajal Kumar, Islam Md Robiul, Islam Md Manirul, Badal Faisal R, Ali Md Firoj, Ahamed Md Hafiz, Abhi Sarafat Hussain, Sarker Subrata Kumar, Hasan Md Mehedi

机构信息

Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.

出版信息

Heliyon. 2024 Feb 21;10(5):e26503. doi: 10.1016/j.heliyon.2024.e26503. eCollection 2024 Mar 15.

Abstract

A Digital Twin (DT) is a digital copy or virtual representation of an object, process, service, or system in the real world. It was first introduced to the world by the National Aeronautics and Space Administration (NASA) through its Apollo Mission in the '60s. It can successfully design a virtual object from its physical counterpart. However, the main function of a digital twin system is to provide a bidirectional data flow between the physical and the virtual entity so that it can continuously upgrade the physical counterpart. It is a state-of-the-art iterative method for creating an autonomous system. Data is the brain or building block of any digital twin system. The articles that are found online cover an individual field or two at a time regarding data analysis technology. There are no overall studies found regarding this manner online. The purpose of this study is to provide an overview of the data level in the digital twin system, and it involves the data at various phases. This paper will provide a comparative study among all the fields in which digital twins have been applied in recent years. Digital twin works with a vast amount of data, which needs to be organized, stored, linked, and put together, which is also a motive of our study. Data is essential for building virtual models, making cyber-physical connections, and running intelligent operations. The current development status and the challenges present in the different phases of digital twin data analysis have been discussed. This paper also outlines how DT is used in different fields, like manufacturing, urban planning, agriculture, medicine, robotics, and the military/aviation industry, and shows a data structure based on every sector using recent review papers. Finally, we attempted to give a horizontal comparison based on the features of the data across various fields, to extract the commonalities and uniqueness of the data in different sectors, and to shed light on the challenges at the current level as well as the limitations and future of DT from a data standpoint.

摘要

数字孪生(DT)是现实世界中物体、过程、服务或系统的数字副本或虚拟表示。它最早由美国国家航空航天局(NASA)在20世纪60年代的阿波罗任务中引入世界。它可以根据其物理对应物成功设计出虚拟对象。然而,数字孪生系统的主要功能是在物理实体和虚拟实体之间提供双向数据流,以便它能够不断升级物理对应物。它是一种用于创建自主系统的先进迭代方法。数据是任何数字孪生系统的核心或基石。在线找到的文章一次只涵盖一两个关于数据分析技术的领域。在网上没有发现关于这种方式的全面研究。本研究的目的是概述数字孪生系统中的数据层面,并且它涉及各个阶段的数据。本文将对近年来数字孪生已应用的所有领域进行比较研究。数字孪生处理大量数据,这些数据需要进行组织、存储、链接和整合,这也是我们研究的一个动机。数据对于构建虚拟模型、建立网络物理连接以及运行智能操作至关重要。已经讨论了数字孪生数据分析不同阶段的当前发展状况和面临的挑战。本文还概述了数字孪生如何应用于不同领域,如制造业、城市规划、农业、医学、机器人技术以及军事/航空工业,并使用最近的综述论文展示了基于每个领域的数据结构。最后,我们试图基于各个领域数据的特征进行横向比较,提取不同部门数据的共性和独特性,并从数据角度阐明当前水平的挑战以及数字孪生的局限性和未来发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e94/10912257/d46d9c714805/gr001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索