## 3 Research Process and Methods
### Establishing a QFD Design Factor Mapping Model  
To ensure the scientificity and reliability of our design decisions, this study uses the hierarchical analysis method (AHP) to determine the weights of each index and then combines it with the quality function development (QFD) to carry out the design optimization [69]. QFD quantifies and visualizes the relationship between user requirements and product technical characteristics, so as to determine the absolute and relative weights of each technical characteristic and to make clear the priority between each design requirement [42]. The weights of the three core requirements from the AHP output are used as the left wall requirements of the quality house, and the interface design elements are mapped to the roof area as design elements [70]. Correlations between requirements and features were assessed via a focus group (members as above) using the Delphi symbol system: \(\Delta\) (weak = 1 point), \(\cap\) (medium = 3 points), \(\bullet\) (strong = 5 points). Based on the symbolic scoring data, a multidimensional correlation matrix between requirements and design \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Citeria** & **First Level** & **Indicator** & **Secondary** & **Comprehensive** & **Weight** \\
**Layer** & **Weight** & **Layer** & **Weight** & **Weight** & **Sorting** \\ \hline \multirow{3}{*}{**B\({}_{1}\)**} & \multirow{3}{*}{0.6999} & C\({}_{1}\) & 0.5650 & 0.3599 & 1 \\  & & C\({}_{2}\) & 0.1175 & 0.0748 & 5 \\  & & C\({}_{3}\) & 0.2622 & 0.1670 & 3 \\  & & C\({}_{4}\) & 0.0553 & 0.0352 & 7 \\ \multirow{3}{*}{**B\({}_{2}\)**} & \multirow{3}{*}{0.2137} & C\({}_{5}\) & 0.8333 & 0.2152 & 2 \\  & & C\({}_{6}\) & 0.1667 & 0.0430 & 6 \\ \multirow{3}{*}{**B\({}_{3}\)**} & \multirow{3}{*}{0.0864} & C\({}_{7}\) & 0.1667 & 0.0175 & 8 \\  & & C\({}_{8}\) & 0.8333 & 0.0873 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The weight of perceptual imagery in ordering interface design. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Design Features** & **Level 1** & **Level 2** & **Level 3** & **Level 4** \\ \hline Interface structure & Layout & Module Partitioning & \\ Visual Design & Color scheme & Font Style & \\ Function Tips & Status prompt & Dynamic Information & \\ Emotional Design & Illustration and decoration & Emotional language & User Rewards & \\ Interactive Experience & Feedback Mechanism & Convenience of operation & \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Analysis of the meal ordering interface. elements was established and the importance of each design element was calculated using the weighted summation formula [71] as shown in Figure 7.  
We calculated the absolute weights and relative weights of design elements [62]. The calculation formulas are shown in (7) and (8). In the formula, \(W_{j}\) denotes the absolute weight value of technical requirements, Wi denotes the weight value of user requirements (see Table 8 for the combined weight value of each specific sub-division of requirements), \(P_{ij}\) denotes the correlation coefficient (the three symbols correspond to the QFD of the quality house, in which the strong correlation \(\bullet\) = 5, the medium correlation \(\circ\) = 3, and the weak correlation \(\Delta\) = 1), and \(W_{k}\) denotes the relative weight value of technical requirements:  
\[W_{j}=\sum_{i=1}^{q}W_{i}P_{ij} \tag{7}\]  
\[W_{k}=\frac{W_{j}}{\sum_{i=1}^{q}W_{j}} \tag{8}\]  
The following conclusions are drawn; see Table 13.  
Based on the analysis of the weighting data and the construction of the mapping model, this study proposes the following conclusions and further deepens the theoretical background and empirical analysis of design optimization: