# Digital Twin Model Quality Optimization and Control Methods Based on Workflow Management
###### Abstract  
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. digital twin model; model evaluation; workflow; quality optimization and control +
Footnote †: journal: Medical and Electrical Engineering, Wuhan University of Technology, Wuhan 430070, China  
and provided 29 quantitative calculation methods. [PERSON] et al. [11] performed digital twin construction and evaluation based on the modeling simulation theory and methods. [PERSON] et al. [12] proposed an adaptability approach for assessing the potential applications of digital twin models. In terms of model optimization, [PERSON] et al. [13] proposed a digital twin geometric model lightweighting method, which reduced geometric errors in the mesh model and improved the frame rate. To reasonably modify the parameters of digital twin models, [PERSON] et al. [14] proposed optimizing model output accuracy and control parameters with gradient descent. [PERSON] et al. [15] proposed a model simplification method that preserved important mesh details and reduced redundant surfaces, which facilitated the transfer, loading, and browsing of digital twin models. [PERSON] et al. [16] proposed a fast render method for digital twin models in large-scale scenes, which was of great significance for the dynamic real-time performance and interactivity of digital twin systems. [PERSON] et al. [17] proposed a lightweighting method for the digital twin model of bogies and verified the validity of lightweight models in dynamics simulation. In summary, various digital twin model evaluation indexes, evaluation methods, and optimization solutions have been proposed in recent years to effectively improve the quality of digital twin models. In terms of digital twin model evaluation, the relevant studies are mainly conducted from the perspective of generality, and the proposed evaluation indexes and methods are numerous and mainly focused on the final results, while the subsequent model quality optimization is rarely considered. In terms of digital twin model optimization solutions, scholars have also proposed optimization solutions for digital twin model application scenarios, such as mesh models, model parameter refinement and tuning, etc., which are mainly focused on the optimization of a particular aspect.