## 2 Model Method
### Model Step  
To sum up, the network analysis method is selected as the modeling method in this paper. The network analysis method model can reflect the relationship between non-adjacent nodes in the new energy vehicle charging station network, and can also reflect the degree of correlation between some distant nodes. In this paper, on the assumption that the up-going and down-going lines of bus routes overlap, the nodes of charging stations are connected to each other. This paper studies the charging station network based on the network analysis method, so as to analyze its layout significance more objectively. Define the charging station network as \(G=(V,E,W)\). The specific steps are as follows:  
Step 1: Determine the node set of the new energy vehicle charging station network. To build the charging station network model, we first need to determine the node set. Determine the research area R, capture all the new energy vehicle charging stations in the area R on the open platform of AmAP, abstract the charging station as nodes in the network, and the relationship between charging stations as the edges in the website; the set formed by all nodes in the region is \(V\), the set formed by all edges is \(E\), and the weight of the edge is \(W\). Build a charging station network model, which can be expressed as follows:  
\[G=(V,E,W)\left\{ \begin{array}{l}V=\left\{v_{1},v_{2},v_{3},\cdots v_{i}, \cdots,v_{j}\right\}\\ E=\left\{e_{1},e_{2},e_{3},\cdots e_{i},\cdots,e_{j}\right\}\\ W=\left\{w_{e1},w_{e2},w_{e3},\cdots w_{ei},\cdots,w_{ej}\right\}\end{array} \right.\]  
Obtain the specific location information (expressed by latitude and longitude) of all new energy vehicle charging stations in Lixia District, Shizhong District, Licheng District, Tianqiao District, and Huaiyin District of Jinan City through the open platform of AmAP, and then select the location information of all bus routes and stations according to the official website of Jinan Public Transport. Considering from the charging station to the adjacent bus lines, setting 100 m as the limit in this paper, set a node \(i\); if the node \(i\) and the area-adjacent bus line distance is less than 100 m, and the bus line passes through the node \(i\), select the node \(i\) and repeat the above steps, selecting all the nodes. Step 2: Calculate the correlation coefficient matrix of the new energy vehicle charging station network. The collection of bus stops corresponding to charging stations \(v_{i}\) and \(v_{j}\) is \(B_{i}\) and \(B_{j}\), \(B_{i}=\left\{B_{i}^{1},B_{i}^{2},B_{i}^{3}\cdots B_{i}^{S}\right\}\), \(B_{j}=\left\{B_{i}^{1},B_{j}^{2},B_{i}^{3}\cdots B_{j}^{T}\right\}\). \(w_{ij}^{pq}=1\) represents when \(B_{i}^{p}\) and \(B_{j}^{q}\) are on the same bus route; \(w_{ij}^{pq}=0\) represents when \(B_{i}^{p}\) and \(B_{j}^{q}\) are not on the same bus route. The edge weight of charging stations \(v_{i}\) and \(v_{j}\) is \(w_{ij}=\sum\limits_{p=1}^{S}\sum\limits_{q=1}^{T}w_{ij}^{pq}\). Then, the charging station correlation coefficient matrix is as follows:  
\(w= \begin{pmatrix}w_{11}&w_{12}&\cdots&w_{1t}&\cdots&w_{1n}\\ w_{21}&w_{22}&\cdots&w_{2t}&\cdots&w_{2n}\\ \vdots&\vdots&\ddots&\vdots&\ddots&\vdots\\ w_{s1}&w_{s2}&\cdots&w_{st}&\cdots&w_{sn}\\ \vdots&\vdots&\ddots&\vdots&\ddots&\vdots\\ w_{n1}&w_{n2}&\cdots&w_{nn}&\cdots&w_{nm}\end{pmatrix} \)  
Step 3: Determine the strong correlation coefficient matrix of the charging station. Starting from the column direction of the charging station correlation coefficient matrix \(w\), the W-T index is calculated to determine the critical value, and then the charging station's strong correlation coefficient matrix is calculated. The steps are as follows:  
1.