## 3 Case Research
### Bayesian Network Construction  
#### 3.2.1 Basic Event Probability Analysis  
During the course of the study, five safety management experts from the oil and gas sector were invited, each possessing at least 10 years of experience in safety management. The selection of these experts was based on their professional expertise in the safety domain and their extensive practical experience. Additionally, to ensure the reliability and accuracy of the data collection, a multi-verification mechanism was employed. After each interview, the collected data were systematically organized and reviewed by another expert. Each expert's opinion was processed independently, and for risk indicators that showed significant discrepancies, the experts were invited to discuss and reach a consensus. By collecting the leakage reports of an oil field in Xinjiang province in recent years, the invited experts were allowed to use a seven-level natural language scale to judge the basic events in the risk of leakage of ethane sub-tanks, respectively. Meanwhile, to reduce the subjectivity of experts, this paper introduces the \(t\)-norm algorithm and the SAM for fuzzy operation and constructs a fuzzy Bayesian network. In this paper, risk event X\({}_{29}\) (soil corrosion) is taken as an example for calculation and analysis. According to Equations (4)-(8), the expert fuzzy evaluation risk event X\({}_{29}\) is reasonably tested, and the \(t\)-norm algorithm is used to compute the comprehensive fuzzy number \(\widetilde{R}_{z}\) of the expert evaluation event, with the results displayed in Table 6. According to the the calculation results, it can be observed that the degree of consistency \(OH_{uv}\) between two and two of the fuzzy evaluations of the invited experts in safety management in the petrochemical industry is high, and the relative consistency index \(R(H_{u})\) is basically the same. This indicates that the invited experts have a relatively balanced understanding of the ethane leakage in the oilfield, and the assessments are relatively objective. According to Formula (9), the comprehensive fuzzy numbers obtained from the expert evaluations for the basic event X\({}_{29}\) are defuzzied as follows:  
\[FPS_{29}=\frac{\int_{0.5180}^{0.5397}\left(\frac{x-0.5180}{0.0217}\right)xdx+ \int_{0.5397}^{0.5796}xdx+\int_{0.5796}^{0.6014}\left(\frac{x-0.5796}{0.6014- 0.5796x}\right)xdx}{\int_{0.5180}^{0.5397}\left(\frac{x-0.5180}{0.0217}\right) dx+\int_{0.5397}^{0.5796}1 dx+\int_{0.5796}^{0.6014}\left(\frac{x-0.6014-x}{0.6014- 0.5796}\right)dx}=0.5597\] \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Evaluation** & **Indicator** & **Evaluation** & **Indicator** & **Evaluation** & **Indicator** & **Evaluation** & **Indicator** \\
**Indicators** & **Value** & **Indicators** & **Value** & **Indicators** & **Value** & **Indicators** & **Value** \\ \hline \(OH_{12}\) & 0.850 & \(A(H_{1})\) & 0.919 & \(R(H_{1})\) & 0.2042 & \(C_{DC}(H_{1})\) & 0.2171 \\ \(OH_{13}\) & 0.825 & \(A(H_{2})\) & 0.881 & \(R(H_{2})\) & 0.1958 & \(C_{DC}(H_{2})\) & 0.2129 \\ \(OH_{14}\) & 1 & \(A(H_{3})\) & 0.863 & \(R(H_{3})\) & 0.1917 & \(C_{DC}(H_{3})\) & 0.1858 \\ \(OH_{15}\) & 1 & \(A(H_{4})\) & 0.919 & \(R(H_{4})\) & 0.2042 & \(C_{DC}(H_{4})\) & 0.1921 \\ \(OH_{23}\) & 0.975 & \(A(H_{5})\) & 0.919 & \(R(H_{5})\) & 0.2042 & \(C_{DC}(H_{5})\) & 0.1921 \\ \(OH_{24}\) & 0.850 & & & & & & \\ \(OH_{25}\) & 0.850 & & & & & & \\ \(OH_{34}\) & 0.825 & & & & & & \\ \(OH_{35}\) & 0.825 & & & & & & \\ \(OH_{45}\) & 1 & & & & & & \\ \hline Comprehensive evaluation & & & & & & & \\ results \(\widetilde{R}_{29}\) & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Expert consistency test and evaluation results.