A framework and method for equipment digital twin dynamic evolution based on IExATCN

Dynamic evolution is the most typical feature of a digital twin, making it different from a traditional digital model. Dynamic evolution is also the core technology for building equipment digital twins because it ensures consistency between physical space and virtual space. This paper proposes a dynamic evolution framework for black box equipment digital twins. The framework consists of three main parts: data acquisition and processing, an evolution triggering mechanism and an evolution algorithm. A formal description of the dynamic evolution of a black box digital twin is also given. Furthermore, by synthetically considering the computational accuracy and efficiency, we design an incremental external attention temporal convolution network (IExATCN) model to instantiate the proposed framework. Finally, the significance of digital twin dynamic evolution and the effectiveness of the IExATCN is verified by 3D equipment attitude estimation datasets.