## 2 Methods and Steps
### Evaluation Method of Driving Behavior of Mining Truck Drivers
#### 2.4.2 Calculation Procedure for Principal Component Analysis  
Principal component analysis is based on the idea of dimensionality reduction, which gradually converts diversified indicators into a small number of comprehensive indicators, namely corresponding principal components. Each principal component can accurately and intuitively reflect most of the information on the condition that the information is not repeated [31; 32]. This method can greatly eliminate the interference of human factors and ensure the objectivity, scientific accuracy, and effectiveness of the evaluation process and results. It is suitable for the evaluation system with closely related and systematic evaluation indicators. The specific analysis process is as follows [33]:  
(1) Step 1: original data standardization processing  
It is assumed that there are m index variables that can characterize the driving behavior of mining truck drivers, namely \(x_{1}\), \(x_{2}\),\(\cdots\), \(x_{m}\). There are \(n\) evaluation objects in total, and the value of the \(j\) index of the \(i\)th evaluation object is \(x_{ij}\). Will each acquire \(x_{ij}\) into a standardized index \(\widetilde{x}_{ij}\) relation can be expressed as:  
\[\widetilde{x}_{ij}=\frac{x_{ij}-\overline{x}_{j}}{s_{j}},(i=1,2,\cdots,n;j=1,2,\cdots,m) \tag{16}\]  
Among them, \(\overline{x}_{j}=\frac{1}{n}\sum\limits_{i=1}^{n}x_{ij},s_{j}=\sqrt{\frac{1} {n-1}\sum\limits_{i=1}^{n}\left(x_{ij}-\overline{x}_{j}\right)^{2}},(j=1,2, \cdots,m)\), and \(\overline{x}_{j}s_{j}\), indicate for the first \(j\), the index, the sample mean, and the standard deviation, respectively. (2) Step 2: Obtain the correlation coefficient matrix \(R\)  
After standardizing the index data representing the driving behavior of mining truck drivers, it is necessary to further calculate and obtain the correlation coefficient matrix R, whose relationship can be expressed as:  
\[R=\left(r_{ij}\right)_{m\times m} \tag{17}\]  
Among them, \(r_{ij}=\frac{\sum\limits_{k=1}^{m}\bar{x}_{ij}\times\bar{x}_{ij}}{n-1}\), \((i,j=1,2,\cdots,m)\), \(r_{ii}=1\), \(r_{ij}=r_{ji}\), and \(r_{ij}\) are the correlation coefficients between the \(i\)th index and the \(j\)th index. (3) Step 3: Determine the eigenvalues and eigenvectors  
Using the step 2 calculation of the correlation coefficient matrix, \(R\) eigenvalues from large to small are \(\lambda_{1}\geq\lambda_{2}\geq\lambda_{3}\geq\cdots\geq\lambda_{m}\geq 0\), and the corresponding eigenvector \(u_{1}\), \(u_{2}\), \(\cdots\), \(u_{m}\), where \(u_{j}\)=(\(u_{1j}\), \(u_{2j}\), \(\cdots\), \(u_{m}\))\({}^{\frac{1}{2}}\), composed of m new index variables can be expressed as:  
\[\left\{ \begin{array}{l}y_{1}=u_{11}\bar{x}_{1}+u_{21}\bar{x}_{2}+\cdots u_{ n1}\bar{x}_{n}\\ y_{2}=u_{12}\bar{x}_{1}+u_{22}\bar{x}_{2}+\cdots u_{n2}\bar{x}_{n}\\ \cdots\cdots\\ y_{m}=u_{1m}\bar{x}_{1}+u_{2m}\bar{x}_{2}+\cdots u_{mm}\bar{x}_{n}\\ \end{array} \right. \tag{18}\]  
where \(y_{1}\) represents the first principal component, \(y_{2}\) represents the second principal component, and \(y_{m}\) represents the principal component. (4) Step 4: Select \(p\) (\(p\leq m\)) principal components to calculate the comprehensive evaluation value  
Finally, the corresponding information contribution rate and cumulative contribution rate are calculated according to the eigenvalue determined in Step 3. Then, through the information contribution rate and cumulative contribution rate of each principal component, the comprehensive evaluation value corresponding to the driving behavior samples of each group of mining truck drivers can be calculated.