Although digital twins have recently emerged as a clear alternative for reliable asset representations, most of the solutions and tools available for the development of digital twins are tailored to specific environments. Furthermore, achieving complex digital twins often requires the orchestration of technologies and paradigms such as machine learning, the Internet of Things, and 3D visualization, which are rarely seamlessly aligned in open-source solutions. In this paper, we present an open-source framework for the development of compositional digital twins, i.e., advanced digital twins that link individual entities or subsystems to create a higher degree digital twin, allowing knowledge sharing and data relationships. In this open framework, digital twins can be easily developed and orchestrated with 3D-connected visualizations, IoT data streams, and real-time machine-learning predictions. To demonstrate the feasibility of the framework, a use case in the Petrochemical Industry 4.0 has been developed.

尽管数字孪生最近已成为可靠资产表示的明显替代方案，但用于开发数字孪生的大多数解决方案和工具都是针对特定环境量身定制的。此外，实现复杂的数字孪生通常需要协调机器学习、物联网和 3D 可视化等技术和范式，而这些技术和范式在开源解决方案中很少能无缝结合。在本文中，我们提出了一个用于开发组合数字孪生的开源框架，即先进的数字孪生，它连接各个实体或子系统以创建更高程度的数字孪生，从而允许知识共享和数据关系。在这个开放框架中，可以通过 3D 连接的可视化轻松开发和编排数字孪生，物联网数据流和实时机器学习预测。为了证明该框架的可行性，开发了石化工业 4.0 的用例。