## 2 Methods
### Bayesian Network  
A Bayesian network, also called a reliability network, is an extension of the Bayesian method, and it is one of the most effective theoretical models in the uncertain knowledge expression and reasoning field. A Bayesian network is a directed acyclic graph (DAG) consisting of representative variable nodes and directed edges connecting these nodes [33]. The nodes represent random variables. The directed edges between the nodes represent the correlation between the nodes. Conditional probability is used to express the relational strength. The prior probability is used to express the information of the nodes without father nodes. If there is no connection edges between the nodes, then the variables corresponding to the nodes are conditionally independent relative to the father nodes. A typical ordinary Bayesian network is shown in Figure 1, where \(Y\) is the parent node, and \(X_{1}\) and \(X_{2}\) are the child nodes. The joint probability distribution function is shown in Formula (3):  
\[P(Y,X_{1},X_{2})=P(Y)P(X_{1}|Y)P(X_{2}|Y) \tag{3}\]  
One of the important characteristics of a Bayesian network is that it can perform bidirectional reasoning, which means that it can infer the result from the reason and infer the reason from the result. The Bayesian network takes advantage of [PERSON] theorem to update the probability of variables when given new information, as shown in Formula (4) [34]. Therefore, after inputting a piece of information into the result variable, the Bayesian network model reproduces the reverse probability and deduces the probability of the cause variable [35].  
\[P(U|E)=\frac{P(U)\times P(E|U)}{\sum\limits_{\overline{U}}P(U)\times P(E|U)} \tag{4}\]  
Here, \(P(E|U)\) is the updated joint probability, and \(\sum\limits_{\overline{U}}(\cdot)\) is the summation over all values of \(U\), except for \(E\). Bayesian networks have been extensively employed in the fields of safety, risk, and reliability analyses.  
Before using the Bayesian network for deduction, the construction of the Bayesian network should be completed. Bayesian network construction includes the determination of the network topology and the determination of the probability distribution function of each node. The determination of the Bayesian network topology needs to determine the causality between nodes, which is a difficulty in the application process of Bayesian networks. In practical applications of Bayesian networks, it has been found that if the Bayesian network construction is based on expert experience only, the network structures given by various experts are usually different. If the expert can construct the network topology based on objective data analysis results, it will help to improve the objectivity and consistency of the network topology [36]. The determination of the probability distribution function is the key point of Bayesian network application. If the variable is discrete, it is represented by a conditional probability table (CPT), with different values of the nodes considering their parent states [34]. Bayesian network applications are usually limited due to the lack of data and uncertain probabilities. In this paper, the research object is a highly information-based enterprise, so the data are more sufficient, making prior probability and conditional probability easily obtained using statistical methods.