## 2 Literature Review
### Human Reliability Research  
Human reliability is one of the important factors to ensure the safe and reliable operation of the industrial system [24]. Human reliability refers to people's ability to complete specified tasks without error under specified times and conditions [25]. Human reliability is studied from the two perspectives of the person and the tasks [26]. The studies from operators' perspective are mainly divided into two categories: first, mechanism of human error detection and diagnosis and decision-making under emergency conditions [27], which concerns the influencing factors and the human factor reliability model. Second, the methods for the analysis and prediction of human error [28]. Representative achievements of such research include the finding that human error can be due to perception errors concerning environmental information [29]. The brain makes a wrong decision when it senses and processes information, and an intended action is not completed [30]. The physiological aspects of human error refer to the limits of human ability, including human perception, feelings, reaction speed, physical strength, and biological rhythm, and human psychological aspects refer to temperament, character, emotions, and attention. [PERSON] and [PERSON] et al. (2016) proposed a human reliability analysis model that combines the features related to facility conditions to determine the probability of human error in the probabilistic safety analysis of a process plant and to prove that the human factor is the leading cause of accidents [31]. [PERSON] et al. (2016) studied the framework for the classification and analysis of human factors related to accidents and events [32]. The main indicators involve task complexity, such as multiple faults occur simultaneously, multiple devices cannot be used at the same time, the memory requirements are large, the interdependence between devices is unclear, an indicator is misleading or missing, a single fault masks the symptoms of other faults, and the scale and scope of the task are large. There are many steps in the completion of a specific task. The relationship between elements and tasks is complex, and the task is dynamic and uncertain. There are many information interferences, the task structure is complex, the numerical control system is complicated, the completion time is tight, the knowledge width and depth are high, the human-machine interface is complex, the factors are interdependent, the tasks are unstandardized and unconventional, and there is a lack of understanding of tasks and difficulty in operation [33]. Human reliability analysis methods include mainly human error rate prediction technology, the operator action tree, accident initiation and evolution analysis, the successful likelihood index, pairwise comparisons, social-technical-human factor reliability analysis, maintenance person behavior simulations, the multisequence failure model, the cognitive event tree system, cognitive reliability and error analysis, fuzzy set analysis [34] and comprehensive evaluations [35]. A representative method is [PERSON] et al. (2016) Bayesian network(BN) model that uses additional and qualitative causal paths to provide traceability, and that combines expert probabilities with information from operator performance databases such as the scenario authoring, characterization, and debriefing application to quantify the model and enrich the quantitative approach to rein the last decade, Bayesian networks (BNs) have been identified as a powerful tool for human reliability analysis [36]. [PERSON] et al. (2018) constructed a cognitive reliability and error analysis methods model for human reliability analysis based on fuzzy Bayesian networks [37]. [PERSON] and [PERSON] (2018) introduced a dependency-based and human error-based evidence network approach to the belief rule and uncertainty measurement [38]. [PERSON] et al. (2018) proposed a large-scale group decision-making method based on dependency-based human reliability [39].