# 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  
Based on the above work, the macro-control model quality advantage and the integrated consideration of macro-control and micro-optimization model quality advantage were verified by two processes. To verify the advantages of macro control, models E\({}_{1}\) and E\({}_{2}\) underwent the flow from node N\({}_{0}\) to node N\({}_{1}\). As a result, model E\({}_{1}\) successfully flowed to the next workflow node N\({}_{1}\), while model E\({}_{2}\) with abnormal quality indexes was left at its original workflow node N\({}_{0}\). The abnormal quality index for model E\({}_{2}\) in this execution stage is C\({}_{9}\), where the current \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Node** & **Description** & **Node** & **Description** \\ \hline N\({}_{0}\) & Geometric model construction & N\({}_{2}\) & Model fusion \\ N\({}_{1}\) & Physical/behavior model construction & N\({}_{3}\) & Model checking \\ Start & Creating model-related files & End & Archive \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of some activities in the workflow. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Node** & **Description** & **Transition** & **Description** \\ \hline T\({}_{0}\) & Submitting geometric model construction & T\({}_{4}\) & Submitting for archiving \\ T\({}_{1}\) & Submitting physical/behavior model construction & T\({}_{5}\) & Geometric model reconstruction \\ T\({}_{2}\) & Submitting model fusion & T\({}_{6}\) & Physical/behavior model reconstruction \\ T\({}_{3}\) & Submitting model checking & T\({}_{7}\) & Model fusion reconstruction \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of transitions in the workflow. Figure 9: Modeling of the workflow integrated with model evaluation. \(C_{9}\) of models E\({}_{1}\) and E\({}_{2}\) is shown in Figure 10. It can be seen that the \(C_{9}\) value of model E\({}_{1}\) achieves the target threshold. If the threshold values of other required evaluation indexes are satisfied, the workflow can proceed to the next node N\({}_{1}\) according to the constraint rules. Due to the low \(C_{9}\) value in model E\({}_{2}\), the target threshold is not achieved, and the model does not meet the requirement of flowing to the next node, remaining at the original workflow node N\({}_{0}\). Then, models E\({}_{1}\) and E\({}_{2}\) underwent the entire workflow to the end, and the methods (MQCM) in this paper were compared with the critical quality characteristic immune preventive control method (CQCTIPCM) [23] without considering macro quality control. CQCTIPCM is not implemented from the beginning of geometric modeling, and concentrated quality evaluation and optimization for the previous stages are performed before model fusion. Figure 11 shows the abnormal indexes of each model before and after quality control. It can be observed that the method proposed in this paper can keep models with abnormal indexes in the workflow through macro-level model quality control, thus avoiding the output of models with abnormal indexes. Figure 11: Comparison results of the number of abnormal indexes before and after quality control. Figure 10: Values of index \(C_{9}\) for models E\({}_{1}\) and E\({}_{2}\). To verify the advantages of combining macro and micro control, the flow of model E\({}_{2}\) from workflow node N\({}_{2}\) to N\({}_{3}\) was used as an example for analysis. Based on the macro-level control results, micro-level quality optimization was further applied to the model with abnormal indexes, and the results are presented in Table 3. The E\({}_{2}\) abnormal indexes in Table 3 are those generated when applying macro quality control methods, while the E\({}^{\prime}\)\({}_{2}\) abnormal indexes are those generated without the quality optimization of model E\({}_{2}\). According to Table 3, the C\({}_{9}\) and C\({}_{16}\) abnormal indexes interact with each other, and the results of the earlier design activities affect the subsequent modeling quality.