## 1 Introduction  
The risk evaluation of coupled factors throughout the system mission process has received increasing attention in recent years due to its effectiveness in preventing hazards and ensuring system safety, which offers a potential application in safety risk analysis. Safety analysis and risk assessment aim to eliminate and control various hazards through design activities and preventive measures, so as to prevent accidents that lead to casualties, equipment damage, and mission failures during the operation of the system [1, 2]. With the development of science and technology, a series of analysis methods for evaluating system failures and risk events has been developed, especially in high-risk fields such as aerospace, chemical, nuclear, and other industrial fields. However, these methods have been found to be insufficient in a number of safety problems caused by the coupling characteristics in complex large scale systems. The current safety analysis methods can be divided into single-factor and multi-factor analysis due to the differences in analysis objects [3], which have been extensively studied and have achieved good results. Specifically, failure mode and effects analysis (FMEA), functional hazard analysis (FHA), preliminary hazard analysis (PHA), and hazard and operability analysis (HAZOP) are effective methods for identifying and analyzing single failure modes or risk factors. For example, [PERSON] et al. realized the safe guidance design of a train system by combining PHA and FMEA methods [4], [PERSON] et al. improved the risk assessment model based on fuzzy theory and the analytic hierarchy process (AHP), which provided a more accurate evaluation results [5, 6], and [PERSON] [7] and [PERSON] [8] applied the HAZOP method to medical diagnostic systems and road-safety measures, to identify not only hazards but also operational problems. Moreover, with the development of the accident cause theory, a large number of methods for multi-factor safety analysis, such as fault tree analysis (FTA), Petri nets, Bayesian networks, etc., have also been invented. For example, [PERSON] et al. [9] combined FTA and FMEA in a recursive manner and applied it to an additive manufacturing system, so as to improve the efficiency of risk analysis. However, there exists in the method a defect whereby the bottom events in the fault tree model need to be independent. [PERSON] et al. [10] presented a safety assessment of unmanned aerial vehicles (UAVs) based on Petri nets, with the operation of the system represented by Petri nets to avoid undesired events. A Bayesian network (BN) is a directed acyclic graph (DAG) composed of nodes and arcs, and its structure is very suitable for displaying important causal relationships among various factors, and provides a powerful framework for the research of complex coupling relationships [11; 12]. The main advantage of a BN is to calculate the probability of occurrence of other uncertain nodes through an effective algorithm after determining any set of nodes [13]. [PERSON] et al. [14] established the structure of a BN through a simple questionnaire survey, and [PERSON] et al. [15] proposed a BN modeling method based on a health assessment survey, which provided guidance for the management of survey data. In addition, [PERSON] et al. [16] constructed a dynamic Bayesian network (DBN) by adding time variables, so as to carry out dynamic risk analysis for deep water drilling operations. However, the current BN modeling methods mainly have two weaknesses. The methods are effective in assessing potential risk factors, but with little consideration about the coupling relationships, which may reduce the completeness of these analyses. The methods also have a strong dependence on the experience of the analysts, which may lead to the inaccuracy and inconsistency of the models.