A digital twin contains up-to-date data-driven models of the physical world being studied and can use simulation to optimise the physical world. However, the analysis made by the digital twin is valid and reliable only when the model is equivalent to the physical world. Maintaining such an equivalent model is challenging, especially when the physical systems being modelled are intelligent and autonomous. The paper focuses in particular on digital twin models of intelligent systems where the systems are knowledge-aware but with limited capability. The digital twin improves the acting of the physical system at a meta-level by accumulating more knowledge in the simulated environment. The modelling of such an intelligent physical system requires replicating the knowledge-awareness capability in the virtual space. Novel equivalence maintaining techniques are needed, especially in synchronising the knowledge between the model and the physical system. This paper proposes the notion of knowledge equivalence and an equivalence maintaining approach by knowledge comparison and updates. A quantitative analysis of the proposed approach confirms that compared to state equivalence, knowledge equivalence maintenance can tolerate deviation thus reducing unnecessary updates and achieve more Pareto efficient solutions for the trade-off between update overhead and simulation reliability.

本文提出了知识等价性维护的概念和通过知识比较和更新的等价性维护方法，重点探讨了智能系统的数字孪生模型，这些系统具有有限的知识感知能力，以及在虚拟空间中复制它们在物理系统中的知识感知能力所需的新颖等价性维护方法。通过量化分析，证明与状态等价性相比，知识等价性维护可以承受偏差以减少不必要的更新，并实现更多的 Pareto 有效解决方案，以平衡更新开销和模拟可靠性。

智能系统数字孪生中的知识等效性