## 3 Methods
### Risk Evaluation Based on FCE  
Fuzzy reasoning is a reasoning method based on fuzzy logic that can be applied in risk assessment, allowing for reasoning and decision-making in situations of uncertainty  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Importance Level** & **Score** \\ \hline Unimportance & 1 \\ Somewhat important & 2 \\ Quite important & 3 \\ Very important & 4 \\ Extremely important & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Risk factor weight scoring basis.  
or fuzzy information. In modern risk management, the fuzzy comprehensive evaluation method has been widely used due to its ability to handle uncertainty and fuzziness.  
In the process of determining the risk level, the first step is to clarify the risk factors to be evaluated, and based on this, form an expert team to score each risk factor and form a fuzzy evaluation matrix. The fuzzy evaluation matrix not only reflects the risk level of each factor, but also reflects the subjective judgment of experts on the relative importance of these factors. Next, the scoring results will be processed using fuzzy mathematics theory. In this study, risk levels were classified into three levels: low, medium, and high. By scoring each risk factor, its membership degree is calculated at each risk level. After obtaining the membership degrees of each risk factor, a comprehensive risk evaluation value is formed by using a fuzzy comprehensive evaluation model for comprehensive calculation. Finally, based on the comprehensive evaluation results, decision-makers can clarify the risk level to which each risk factor belongs and thus formulate corresponding risk control strategies.  
1. Construction of Fuzzy Evaluation Matrix  
The fuzzy evaluation matrix is determined by determining the membership degree set of risk factors. The elements of the evaluation set can be qualitative or quantitative. This article divides risk levels into three levels: low risk, medium risk, and high risk, which are the evaluation sets \(V=\{low,medium,high\}\). Experts evaluate different risk factors based on the division of the evaluation set, and use the proportion of people belonging to a certain level to the total number of people as the corresponding evaluation value \(R_{j}\) for that risk, as shown in Formula (5). Combining the evaluation values of all risk factors yields the membership subset \(R\) of the risk factors, as shown in Formula (6).  
\[R_{j}=\big{(}\frac{a_{j}}{p},\frac{b_{j}}{p},\frac{c_{j}}{p}\big{)}, \tag{5}\]  
\[R=\begin{pmatrix}R_{1}\\ R_{2}\\ \vdots\\ R_{j}\\ \vdots\\ R_{m}\end{pmatrix}, \tag{6}\]  
where \(R_{j}\) is the evaluation value of the \(j\)-th risk factor. \(a_{j}\), \(b_{j}\), and \(c_{j}\) are the numbers of evaluators for low, medium, and high risks in the \(j\)-th risk factor, respectively. \(p\) is the total number of experts, and \(a_{j}+b_{j}+c_{j}=p\).  
2. Comprehensive evaluation calculation  
After determining the weight matrix and evaluation matrix of risk factors, the fuzzy evaluation set vector can be obtained through corresponding algorithms, usually using matrix multiplication, as shown in Formula (7).  
\[C=W\circ R, \tag{7}\]  
where \(C\) is the fuzzy comprehensive evaluation set vector. \(W\) is the risk factor weight matrix. \(R\) is the risk factor evaluation matrix.  
The evaluation result is a \(1\times 3\) matrix, where the elements from left to right represent the membership degrees of low risk, medium risk, and high risk. According to the maximum membership principle of the fuzzy comprehensive evaluation method, the evaluation level with a higher membership degree is selected as the final risk level.