## 4 Results  
As shown in Figure 6, the interface model to be filtered is a refrigerated van. Taking the \"rear wall assembly\" model as an example, the MKD-ICP algorithm is used to perform screening and analysis on the key component models of the refrigerated van. Figure 6: A certain refrigerated van. Based on the assembly structure tree of the \"rear wall assembly\" model, the hierarchy is divided, and model descriptors are established for each subcomponent of the assembly following Step 2 in Section 3.4, as shown in Table 2.  
The similarity sim(_Nw,Np_)sim(_Nw,Np_) between the model and the historical case model is calculated using Equation (18), where the AHP (Analytic Hierarchy Process) method is employed to assign weights to the attributes at each hierarchy level. The judgment matrix \(A\) is constructed based on the hierarchical structure factors. \[A= \begin{bmatrix}1&2&2&6\\ 1&1&2&4\\ \frac{1}{4}&\frac{1}{2}&1&3\\ \frac{1}{6}&\frac{1}{4}&\frac{1}{3}&1\end{bmatrix} \]  
According to Step 4 in Section 3.4, the weights \(\omega_{i}\) for each sub-level attribute of the \"Rear Wall Assembly\" are calculated based on the judgment matrix A. The allocation results are shown in Table 3.  
Taking the \"Rear Wall Assembly\" as the target model \(M_{T}\), the attribute similarity at the \"Wall Assembly\" level between the target model and historical case model \(M_{H}\) is retrieved by combining the sub-level model descriptors and the sub-node attribute weight distribution values. The overall similarity \(S(M_{T},M_{H})\) is calculated and sorted to obtain the model with the most similar hierarchical structure and model features. The node attribute similarity calculation results are shown in Table 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Node** & & \multicolumn{3}{c}{_sim(M\({}_{TZ}\),Mi\({}_{Hz}\)), \(i\) = 1, 2, 3, 4..._} \\ \hline Foam & 0.912 & 0.896 & 0.878 & 0.863 &... \\ Outer Skin & 0.948 & 0.915 & 0.893 & 0.875 &... \\ Inner Skin & 0.921 & 0.898 & 0.864 & 0.847 &... \\ Framework & 0.870 & 0.903 & 0.889 & 0.856 &... \\ \(S(M_{T}\),Mi\({}_{H}\)) & 0.9186 & 0.9020 & 0.8801 & 0.8621 &... \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Node attribute similarity between MT and MH at the same level. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Element Symbol** & **Model Name** & **Number** & **Vector Set Parameters** \\ \hline \(N_{1}\) & Foam & KF7.491 & (0.555, 0.330, 0.045, 0.110, 0.375, 0.520) \\ \(N_{2}\) & Outer Skin & KF9.652 & (0.645, 0.295, 0.005, 0.140, 0.505, 0.400) \\ \(N_{3}\) & Inner Skin & KF9.651 & (0.710, 0.250, 0.010, 0.520, 0.200, 0.265) \\ \(N_{4}\) & Framework & KF9.610 & (0.870, 0.060, 0.070, 0.020, 0.480, 0.485) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Descriptors for key subcomponents of the “Rear Wall Assembly” model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Element Symbol** & **Model Name** & **Number** & **Weight Value** \\ \hline \(N_{1}\) & Foam & KF7.491 & 0.438 \\ \(N_{2}\) & Outer Skin & KF9.652 & 0.273 \\ \(N_{3}\) & Inner Skin & KF9.651 & 0.209 \\ \(N_{4}\) & Framework & KF9.610 & 0.080 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Attribute weight allocation for each sub-level of the “Rear Wall Assembly”. Based on the calculation results in Table 5, a reference model with a high similarity score of \(S(M_{T},M_{H})\) = 0.9186 is identified. The GetSystemValue2 function is used to extract key dimensional parameters from this reference model. These parameters represent essential measurement attributes of critical components in the assembly. Using these dimensional data, the key components for dimension annotation are determined. The extracted reference dimensions are then used as a basis to filter and identify the corresponding key components in the target model, the Rear Wall Assembly.