A virtual model knowledge updating method driven by data

Abstract

In the initial construction of unit-level virtual models in the discrete production system, the understanding of physical entities and the actual operating environment is lacking, and the virtual models need to be iteratively updated to resemble the physical entities more closely. To solve the above problems, an updating method for knowledge in the virtual model driven by data is studied. Firstly, the behavior model in the virtual model can be divided into basic functional models such as forward kinematics, trajectory planning, and material generation, and the knowledge components to support functional model computation are constructed. Secondly, the evaluation indexes for the quality, maintainability, and reliability of the knowledge components are established, and appropriate knowledge component combinations can be selected from the knowledge component base. Then, based on the measurements, it is used to the triggering judgment, and a knowledge component combination with the smallest difference between the simulation output of the virtual model and the real data of physical entities is selected from multiple combinations to realize the iterative update of the model. Finally, the feasibility and effectiveness of the method are verified by examples.