# Digital Twin Model Quality Optimization and Control Methods Based on Workflow Management
## 3 Modeling of the Workflow Integrated with Model Evaluation  
### Digital Twin Model Quality Evaluation  
Quantitative evaluation of the digital twin model quality can effectively reflect the current model quality status and inform digital twin model quality optimization and control. To quantitatively score each index of the digital twin model, each index (\(C_{i}\), the \(i\)th index) is calculated using the quantitative calculation method for the 29 digital twin model evaluation indexes, such as static parameter accuracy and dynamic parameter accuracy, proposed by [PERSON] et al. [10]. As each index of the digital twin model is scored according to the aspects of validity, generality, intuitiveness, and intelligence, the quality, performance, and applicability of the digital twin models can be reasonably evaluated. The model quality evaluation method set can be expressed as:  
\[VEC=\{B_{1},B_{2},B_{3},B_{4},B_{5},B_{6},B_{7},B_{7},B_{8}\} \tag{1}\]  
where \(B_{1}\) denotes the validity evaluation set, \(B_{1}\) = {\(C_{1}\), \(C_{2}\), \(C_{3}\), \(C_{4}\)}, which may involve static and dynamic parameter accuracy, parameter association accuracy, and the degree of co-occurrence of virtual and actual entities; \(B_{2}\) denotes the generality evaluation set, \(B_{2}\) = {\(C_{5}\), \(C_{6}\), \(C_{7}\), \(C_{8}\)}, which may involve the format standardization degree, dimension and unit standardization degree, interface standardization degree, and description standardization degree; \(B_{3}\) denotes the efficiency evaluation set, \(B_{3}\) = {\(C_{9}\), \(C_{10}\), \(C_{11}\), \(C_{12}\)}, which may involve geometric model efficiency, physical model efficiency, behavior model efficiency, and rule model efficiency; \(B_{4}\) denotes the intuitiveness evaluation set, \(B_{4}\) = {\(C_{13}\), \(C_{14}\), \(C_{15}\), \(C_{16}\)}, which may involve parameter intuitiveness, structural intuitiveness, operational process intuitiveness, and evolutionary process intuitiveness; \(B_{5}\) denotes the connectivity evaluation set, \(B_{5}\) = {\(C_{17}\), \(C_{18}\), \(C_{19}\), \(C_{20}\)}, which can involve virtual and physical model connectivity, inter-digital twin model connectivity, digital twin model and service connectivity, and model and digital twin data connectivity; \(B_{6}\) denotes the integrity evaluation set, \(B_{6}\) = {\(C_{21}\), \(C_{22}\), \(C_{23}\)}, which can involve the multidimensional data fusion degree, the multidimensional feature fusion degree, and the multidimensional submodel decision fusion degree; \(B_{7}\) denotes the flexibility evaluation set, \(B_{7}\) = {\(C_{24}\), \(C_{25}\), \(C_{26}\)}, which can involve configurability, assemblability, and scalability; \(B_{8}\) denotes the intelligence evaluation set, \(B_{8}\) = {\(C_{27}\), \(C_{28}\), \(C_{29}\)}, which can involve adaptability, autonomy, and self-learning capability.  
The digital twin model of a robotic arm processed by the geometric model lightweight solution in [15] is shown in Figure 2, and the \(C_{9}\) calculation is expressed in Equation (2). Then, the geometric model efficiency is 0.858. In Equation (2), \(n_{9}\) is the total number of geometric models, \(ngmp_{i}\) is the total number of parameters of the \(i\)th geometric model before lightweighting, and \(ngmp_{i}^{l}\) is the number of parameters of the \(i\)th geometric model after lightweighting.  
\[IC_{9}=\frac{\sum_{i=1}^{n_{9}}\left(\frac{ngmp_{i}-ngmp_{i}^{l}}{ngmp_{i}^{ l}}\right)}{n_{9}} \tag{2}\]