With the advances of IoT developments, copious sensor data are communicated through wireless networks and create the opportunity of building Digital Twins to mirror and simulate the complex physical world. Digital Twin has long been believed to rely heavily on domain knowledge, but we argue that this leads to a high barrier of entry and slow development due to the scarcity and cost of human experts. In this paper, we propose Digital Twin Graph (DTG), a general data structure associated with a processing framework that constructs digital twins in a fully automated and domain-agnostic manner. This work represents the first effort that takes a completely data-driven and (unconventional) graph learning approach to addresses key digital twin challenges.

翻译：随着物联网的发展，海量传感器数据通过无线网络进行通信，为构建数字孪生以镜像和仿真复杂物理世界创造了机遇。长期以来，数字孪生被认为高度依赖领域知识，但我们认为这会导致较高的入门门槛，且因人类专家的稀缺性和成本而发展缓慢。本文提出数字孪生图（DTG），这是一种通用数据结构及配套处理框架，能够以完全自动化和领域无关的方式构建数字孪生。本研究首次尝试采用完全数据驱动及（非传统）图学习方法应对数字孪生的关键挑战。