使用Python Flask开发数字双操作平台

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2022-01-31 DOI:10.1017/dce.2022.1
M. Bonney, M. de Angelis, M. Dal Borgo, Luis Andrade, S. Beregi, N. Jamia, D. Wagg
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引用次数: 8

摘要

摘要数字孪生概念已发展成为一种从数据中提取价值的方法,并正在发展成为一项新技术,用于飞机、发电厂和风力涡轮机等高价值工程系统的设计和资产管理。在实施方面,许多专有的数字孪生软件解决方案已在该领域上市。相比之下,本文描述了最近发布的数字双胞胎开源软件框架,该框架使用Python和Flask提供了一个基于浏览器的操作平台。新平台旨在最大限度地提高用户和从物理双胞胎中获得的数据之间的连接。本文描述了如何使用这种类型的数字孪生操作平台(DTOP)将物理孪生和其他物联网设备连接到用户和云计算服务。该软件的最新版本DTOP Cristallo使用了一个三层结构的例子作为要管理的工程资产。在DTOP Cristallo中,已经开发了用于数字孪生的特定工程软件工具,这些工具用于演示这一概念。在这个阶段,所提出的框架是一个原型。然而,使用网络连接的开源数字孪生软件的潜力是未来研发的一个非常大的领域。
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Development of a digital twin operational platform using Python Flask
Abstract The digital twin concept has developed as a method for extracting value from data, and is being developed as a new technique for the design and asset management of high-value engineering systems such as aircraft, energy generating plant, and wind turbines. In terms of implementation, many proprietary digital twin software solutions have been marketed in this domain. In contrast, this paper describes a recently released open-source software framework for digital twins, which provides a browser-based operational platform using Python and Flask. The new platform is intended to maximize connectivity between users and data obtained from the physical twin. This paper describes how this type of digital twin operational platform (DTOP) can be used to connect the physical twin and other Internet-of-Things devices to both users and cloud computing services. The current release of the software—DTOP-Cristallo—uses the example of a three-storey structure as the engineering asset to be managed. Within DTOP-Cristallo, specific engineering software tools have been developed for use in the digital twin, and these are used to demonstrate the concept. At this stage, the framework presented is a prototype. However, the potential for open-source digital twin software using network connectivity is a very large area for future research and development.
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
期刊最新文献
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