## 4 Discussion
### Statistical Analysis of Frequency  
Statistical frequency-based accident risk causation analysis can provide a comprehensive understanding and analysis of historical accident data, revealing potential patterns and regularities in accident occurrence. Through extensive collection of accident data, high-frequency causal factors can be identified, and their relevance and impact can be analyzed. The formula for calculating the frequency of security risk factors is as follows:  
\[P(w_{i})=\frac{Count(D_{w_{i}})}{Count(D)} \tag{17}\]  
where \(Count(D)\) denotes the total number of accident investigation reports, \(w_{i}(i=1,2,\ldots,33)\) refers to 33 safety risk factors, and \(Count(D_{w_{i}})\) denotes the number of accident investigation reports that contain risk factor \(w_{i}\). The results of frequency statistics are detailed in Figure 12.  
As can be seen from Figure 12, the statistical frequency of the first 15 items is more significant than 0.3, and with the rest of the items there is a frequency fault, these 15 items are: failure to conscientiously fulfill the responsibility of safety supervision R2, leakage problems E4, abnormal concentration of hazardous gases E2, abnormal pressure/temperature values E3, operators not licensed or not accompanied by supervisors O2, hidden dangers rectification and tracking is not in place R1, unreliable equipment and facilities E1, inadequate equipment maintenance and management E5, careless or misrepresentation  
Figure 12: Statistical frequency of accident safety risk factors. of the work site inspection M1, inadequate fire and explosion prevention measures M5, inadequate safety education and training C1, failure to wear protective equipment O1, inadequate safety system C2, non-compliant operation O5, failure of safety monitoring and control equipment E6. It can be found that the environmental and equipment factors are basically in the top 15 of the frequency faults. This also proves that no matter what the initial cause of the accident is and what triggers it, the accident will eventually manifest itself in these environmental and equipment factors, which reveals that the safety management personnel, in the daily risk warning work, need to focus on these aspects to strengthen the monitoring, so as to achieve early warning and nip the accident in the bud. In addition to the environment and equipment factors, the frequency of the top 15 is also more evenly distributed among the other four types of risk factors, which indicates that the environment and equipment factors are not the root cause of accidents; the root cause often comes from the people and management. After obtaining the results of sensitive risk factor analysis, critical causal path analysis, and frequency statistical analysis, the investigation established that some factors existed in a set of factors of these three analyses, indicating these factors are critical in chemical enterprise accidents. These three results intersect and determine the essential risk factors leading to chemical enterprise accidents. In contrast, the non-intersecting part of the results of the three analyses belongs to the critical risk factors, and the remaining ones are the general risk factors with the following formula:  
\[W_{k}=A_{s}\cap A_{r}\cap A_{f} \tag{18}\]  
\[W_{i}=A_{s}\cup A_{r}\cup A_{f}-W_{k} \tag{19}\]  
\[W_{o}=U-A_{s}\cup A_{r}\cup A_{f} \tag{20}\]  
where \(A_{s}\), \(A_{r}\), and \(A_{f}\) refer to sensitive factors, causal path factors, and high-frequency factors, respectively. \(W_{k}\), \(W_{i}\), and \(W_{o}\) refer to critical, important, and general factors, respectively, and \(U\) is the set of all risk factors in the structure of the Bayesian network. The results are detailed in Table 8.