Digital twin is an important emerging technology for digital transformation and intelligent upgrading. Digital twin models are the prerequisite for digital twin applications, and their quality directly affects the quality of digital twin services in monitoring, simulation, prediction, optimization, and other areas. However, researchers have paid insufficient attention to the quality control of digital twin models, thus hindering their effective application. To effectively control model construction and optimize model quality in the design process, this study developed digital twin model quality optimization and control methods based on workflow management. First, a workflow process model integrating digital twin model evaluation was constructed, which integrated the design process and model evaluation methods into workflow management. Then, digital twin model quality control and optimization in different stages were achieved at the macro and micro levels. Thus, the digital twin model quality was effectively controlled during the design process, and targeted design resources were selected to optimize model quality. Finally, the validity of the proposed method of model quality optimization and control was verified using the digital twin models of a practical teaching platform and a multifunctional lift-and-slide experimental line. All evaluation indexes of the model achieved good values, and the target quality optimization of the model could be performed during the design process. The results indicate that the proposed method can effectively control and optimize the model quality, which has excellent feasibility and enables the effective application of the digital twin.

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The initial construction of the unit-level virtual model in the discrete production line lacks the understanding of physical entities and the actual operating environment, which makes it difficult for the virtual model knowledge to correctly drive the behavior of physical entities. In order to solve the above problems, a virtual model knowledge updating method driven by data is studied. Firstly, the knowledge in the virtual model is divided into basic functional units according to functions, and the knowledge of each functional unit is encapsulated into knowledge components such as forward kinematics, motion control, material generation, etc., and the knowledge component base is constructed. Secondly, this method establishes the evaluation index of effectiveness, usability, and reliability of knowledge components, and can screen out appropriate groups of knowledge component combinations from the knowledge component base. Then, based on the measurement value, it is used to update the trigger judgment and select a group of knowledge components with the smallest difference between the simulation output of virtual model and the real data of physical entities from multiple groups of knowledge combinations, so as to realize the iterative update of the model. Finally, the feasibility and effectiveness of the method are verified by examples.

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