## 3 Establishment of Core Indicator System
### Core Indicator System
\[\hat{A}=\left[\sqrt{\lambda_{1}}r_{1},\sqrt{\lambda_{2}}r_{2},\ \ \ \ \ \ \cdots,\ \ \ \ \sqrt{\lambda_{n}}r_{m}\right] \tag{7}\]  
where the parameter \(m\) is determined by the cumulative variance contribution of the common factor. \[m=argmin\left(\frac{\sum_{i=1}^{m}\lambda_{i}}{\sum_{i=1}^{p}\lambda_{i}} \right)\geq r \tag{8}\]  
It is generally believed that, for the data obtained from the questionnaire survey, when the cumulative variance contribution ratio of the first m public factors exceeds 70% [38], it can be considered that the linear combination of the first m public factors can essentially restore the original variable information. The eigenvalues and total variance interpretations are obtained as shown in Table 5. \begin{table}
\begin{tabular}{l l l} \hline KMO Value & & 0.807 \\ \hline \multirow{2}{*}{Bartlett sphericity test} & approximate chi-square & 285933.639 \\  & df & 465 \\  & P & 0.0002 \\ \hline \end{tabular}
\end{table} 
Table 4: Values of KMO test and Bartlett’s test. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multicolumn{3}{c}{**Explanatory Rate of Variance before Rotation**} & \multicolumn{3}{c}{**Post-Rotation Variance Explained**} \\ \cline{2-9}  & **Characteristic** & **Explanation of Variance (\%)** & **Cumulative Variance (\%)** & **Factor** & **Characteristic Root** & **Explanation of Variance (\%)** & **Cumulative Variance (\%)** & **Cumulative Variance (\%)** \\ \hline
1 & 6.457 & 20.828 & 30.828 & 1 & 485.446 & 15.66 & 25.66 \\
2 & 3.586 & 11.567 & 42.395 & 2 & 293.767 & 9.476 & 35.136 \\
3 & 2.604 & 8.4 & 50.796 & 3 & 286.541 & 9.243 & 44.379 \\
4 & 1.74 & 5.613 & 56.409 & 4 & 264.02 & 8.517 & 52.896 \\
5 & 1.691 & 5.454 & 61.863 & 5 & 168 & 5.419 & 58.315 \\
6 & 1.185 & 3.823 & 65.686 & 6 & 162.372 & 5.238 & 63.553 \\
7 & 1.113 & 3.591 & 69.277 & 7 & 157.675 & 5.086 & 68.639 \\
8 & 1.08 & 3.484 & 75.761 & 8 & 127.704 & 4.119 & 74.759 \\
9 & 0.976 & 3.147 & 78.908 & & & & \\
10 & 0.959 & 3.094 & 80.002 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Eigenvalues and total variance explanatory rate of factors. Factor 3 has a greater correlation with the number of poor mos quality and the number of dropped calls, which can be summarized as the current situation of voice stability problems;  
Factor 4 has a greater correlation with colleges and universities and commercial streets, which can be summarized as the current situation of the problem in densely populated areas;  
Factor 5 has a high correlation with the number of crosstalk in calls, which can be summarized as the current status of the voice route independence problem;  
Factor 6 has a high correlation with customer volume level identification, which can be summarized as the current status of the voice service level problem;  
Factor 7 has a high correlation with the number of times off-network, which can be summarized as the current status of the voice signal stability problem;  
Factor 8 has a high correlation with voice mode and can be summarized as the current status of the voice mode problem. Summarized in Table 6 below. The highest weight of factor 1 is obtained, which is 24.952%, i.e., the status quo of indoor speech problems has the greatest influence. The absolute value of the component is taken to be greater than 0.1% as the core influence factor, as shown in Table 7. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factor** & **Nomenclature** & **Factor** & **Nomenclature** \\ \hline Factor 1 & State of the indoor voice problem & Factor 5 & Status of the voice signal stability problem \\ Factor 2 & State of the art of voice problems in traffic & Factor 6 & Status of voice route independence issues \\ Factor 3 & Speech stability issues & Factor 7 & Current status of voice service level issues \\ Factor 4 & Densely populated areas & Factor 8 & Current status of voice mode issues \\ \hline \hline \end{tabular}
\end{table} 
Table 6: New nomenclature of factors.