Abstract: Digital twin technology solves the problem of integration in the cyber-physical world, and has been widely used in the field of industrial Internet. In order to solve the problem of dynamic correction between digital twin and physical entity, this paper proposes a real-time self-correction method for digital twin model based on consistency measurement. Using the speed of data change, the model is divided into two parts: a gradual model and a fast model. A quick parameter search method is constructed. Combined with Latin hypercube global search and greedy local search, an iterative update mechanism is introduced to achieve the consistency measurement of physical entities and digital twins. The results show that the digital twin model improves the randomness of the adjustable parameter selection by optimizing the model adjustable parameter selection process, achieves ahigh degree of consistency between the model and the physical entity, and meets the requirements of model real time self-correction.

Key words: consistent measurement, digital twin, real-time self-correction, Latin hypercube sampling