# Digital Twin Model Quality Optimization and Control Methods Based on Workflow Management
## 6 Instance Verification
### Instance Validation and Result Analysis
#### 6.2.2 Model Quality Optimization and Control Results Analysis
Through micro control, targeted geometric model optimization solutions are selected to optimize abnormal index \(C_{9}\). According to the evaluation by Equation (2), the scores before and after optimization are 0.76 and 0.93, respectively, as shown in Figure 12. Due to the absence of a direct solution for abnormal index \(C_{15}\) in the solution library, the new solution for it is stored in the solution library to solve similar problems in the future. The experiments showed that by spreading the model evaluation tasks across the transitions of the workflow and avoiding addressing too many abnormal indexes in concentrated evaluation and optimization, we effectively prevented early abnormal activities from affecting subsequent design activities while providing targeted solutions to abnormal indexes. To verify the overall effect of the method in this paper on the quality control and optimization of models E\({}_{1}\) and E\({}_{2}\), the two models were executed in the workflow to the archive node. The number of times each model was controlled at the original node in the workflow during execution, the number of model optimization solutions obtained, and the optimization effects were counted, as shown in Figures 13-15, respectively. Referring to [PERSON]. [PERSON] et al. [10], some of the indicators in Figure 15 are calculated as described below. The physical model/behavior model efficiency \(C_{10/11}\) in the digital twin model is calculated as follows:  
\[IC_{10/11}=\sum_{i=1}^{n_{10/11}}\left(\left(ngmp_{i}-ngmp_{i}^{l}\right)/ngmp_ {i}^{l}\right)/n_{10/11} \tag{14}\]  
In Equation (14), \(n_{10/11}\) is the total number of physical/behavior models; \(ngmp_{i}\) is the total number of parameters before the \(i\)th physical/behavior model is lightened; \(ngmp_{i}^{l}\) is the number of parameters after the \(i\)th physical/behavior model is lightened. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Instance** & **Indexes** & \(C_{9}\) & \(C_{15}\) & \(C_{16}\) \\ \hline E\({}_{2}\) & 0 & 1 & 0 \\ \hline E\({}^{\prime}\)\({}_{2}\) & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Abnormal evaluation indexes. Figure 12: Values of index \(C_{9}\) before and after the optimization of model E\({}_{2}\). The digital twin model scalability \(C_{26}\) is calculated as follows:  
\[IC_{26}=(mce\times w_{mce}+mct\times w_{mct}) \tag{15}\]  
In Equation (15), \(mce\) and \(mct\) denote the degree of functional expansion and tailoring of the digital twin model, respectively. \(w_{mce}\) and \(w_{mct}\) are the weights of \(mce\) and \(mct\), respectively, which are determined according to the business requirements and take values in the range of [0, 1], \(w_{mce}+w_{mct}=1\). The digital twin model adaptivity \(C_{27}\) is calculated as follows:  
\[IC_{27}=(atc\times w_{atc}+aec\times w_{acc}+acc\times w_{acc}) \tag{16}\]  
In Equation (16), \(atc,aec,acc\) denote the degree of autonomous adaptation of the digital twin model to task requirements, operating environment, and changes in its functions, respectively, and take the values of [0, 1]; \(w_{atc}\)\(w_{acc}\) and \(w_{acc}\) are the weights of \(atc,aec,\) and \(acc\), respectively, determined according to business requirements, \(w_{atc}+w_{acc}+w_{acc}=1\). The range of values for digital twin model autonomy \(C_{28}\) is shown in Table 4.  
Figure 14: The number of model optimization solutions obtained for each model. Figure 13: The number of times each model was controlled at the original node in the workflow. As can be seen from Figure 13, in the workflow, the total number of times that the two models were controlled at the original nodes by the method presented in this paper is six. The results show that the method in this paper can effectively control the models with the abnormal indicators at the original workflow nodes through the workflow.