## 3 Results and Discussion
### Deep Learning  
_Bayesian Neural Network Predicts Accident Frequency_  
(1) Problem definition and network structure design. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Conditional Combination Path** & \
\begin{tabular}{c} **Raw** \\ **Coverage** \\ \end{tabular} &
\begin{tabular}{c} **Unique** \\ **Coverage** \\ \end{tabular} & **Consistency** \\ \hline Path 1: B*D*F & 0.94 & 0.58 & 1 \\ Path 2: B*-C*D*-E & 0.42 & 0.06 & 1 \\ \cline{2-4} Solution coverage & & 1 & \\ \cline{2-4} Consistency of solution & & 1 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Robustness verification condition combination path. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Consistency** & **Coverage** \\ \hline A & 0.54 & 1 \\ B & 1.00 & 1 \\ C & 0.28 & 1 \\ D & 1.00 & 1 \\ E & 0.34 & 1 \\ F & 0.94 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Necessity test of robustness verification conditions. This study aimed to predict the frequency distribution of biogas poisoning and suffocation accidents in the pre-treatment pool workshop, so the problem was defined as a regression problem. The input layer was the probability of occurrence of 10 accident causes: X\({}_{1}\), X\({}_{2}\), X\({}_{3}\), X\({}_{4}\), X\({}_{5}\), X\({}_{6}\), X\({}_{7}\), X\({}_{8}\), X\({}_{9}\), X\({}_{10}\). The meaning of each input variable was the same as in Bow-Tie model. The output layer was the predicted accident frequency: y_pred. The hidden layer was set to 3 layers, and the number of neurons in each hidden layer was 32, 64, and 32, respectively. Choosing the architectural parameters (number of layers and neurons per layer) of a Bayesian neural network (BNN) is a process that combines domain knowledge, rules of thumb, and computational resource constraints. The 32-64-32 structure balances model expressiveness and computational feasibility. Determine the dataset. It required the distribution of input variables and a model to learn from these data. The input variable X\({}_{1}\) was the probability of not setting up forced ventilation facilities. Through field visits to several biogas plants and evaluation by experts, the probability of this event was determined to be 0.15. The input variables X\({}_{2}\), X\({}_{3}\), X\({}_{4}\), X\({}_{5}\), and X\({}_{10}\) represented the probability of equipment failure or malfunction. The Weibull distribution is one of the commonly used equipment failure rate distributions [57]. After evaluation by experts and enterprise staff, the distribution of each variable over time (days) is shown in Table 7.  
The values of input variables X\({}_{6}\), X\({}_{7}\), X\({}_{8}\) and X\({}_{9}\) were obtained based on on-site surveys at enterprises. A total of 84 confined space operation tickets and saved operation monitoring videos of a certain biogas plant in the past three years were reviewed, of which 5 were illegal operations without conducting gas environment testing, 2 were illegal operations in which harmful gases were detected to exceed the standard but no forced exhaust was performed, 1 was illegal operation in which harmful gases were detected to exceed the standard and forced exhaust was performed but not diluted to a reasonable concentration, and 8 were illegal operations in which workers did not wear protective equipment. The occurrence frequency of events was used instead of the event probability, that is, X\({}_{6}\)(t) = 0.0595, X\({}_{7}\)(t) = 0.0238, X\({}_{8}\)(t) = 0.0119, and X\({}_{9}\)(t) = 0.0952. With the distribution of input variables, we also need to determine a dataset that represents the accident frequency corresponding to these input variables. After deep learning from these existing data, we can predict the frequency of such accidents in the future.