## 1 Introduction
### Literature Review  
Many researchers have used a combination of dynamics and macroscopic and microscopic characteristics in evacuation situations to establish mathematical models for crowd evacuation within bounded areas [8], including models based on macroscopic flow and microscopic forces. The flow-based model treats crowds as a continuous medium and uses equations like fluid mechanics to describe crowd movement and evacuation behavior through the relationship between density, velocity, and flow rate. For example, [PERSON] et al. [9] and others used a coarse network to partition and describe building scenes and proposed a personnel evacuation model (ESM) based on flow theory. [PERSON] et al. [10]. proposed a personnel evacuation model for high-rise buildings, which introduces characteristic length for each person to describe the heterogeneity of personnel density in different network nodes, i.e., different rooms, as well as the physiological and psychological differences of personnel regarding gender and age. The advantage of flow-based evacuation models lies in their low computational complexity and fast computation speed, which played an essential role in the past when computer technology was not fully developed. The disadvantage is that the processing method needs to be simplified, and accuracy is difficult to ensure. [PERSON] et al. [11] simulated the evacuation scenario of two groups of interacting pedestrians based on the methods of the kinetic theory of active particles. They evaluated how the geometric shape of the domain and the details of fear propagation affect the evacuation dynamics. Force-based models mainly include social force, cellular automata, and agent-based models. These models are represented by the Social Force Model (SFM) proposed by [PERSON] [2]. This type of model uses mechanical theory to describe people's force and action intentions more accurately; for example, [PERSON] et al. used a social force model to simulate the walking direction at narrow passages [3] and the phenomenon of \"fast is slow\" in emergencies [2]; [PERSON] et al. [12] used game theory tools to model interactions between humans and other pedestrians and the environment, including walls, exits, and obstacles, transforming them into crowd dynamics. This model allows pedestrians to balance between two competing behaviors: searching for areas with lower levels of congestion and unconsciously following the river in panic situations. [PERSON] et al. [13] added factors such as the density of the surrounding population, memory of exit positions, and whether the pedestrian applying force is facing or facing the pedestrian being subjected to force to the social force model; [PERSON] et al. [14] added information transmission between pedestrians in emergencies to the social force model, simulating herd behavior during evacuation. However, its disadvantage is that it is challenging to explain individual differences. Although it can effectively reproduce the interactions between pedestrians and the environment, it still lacks some behavioral characteristics of pedestrians. It has a large number of parameters that need to be calibrated. The computational workload will increase significantly, especially when the number of people in the scene increases. Another idea is the rule-based model, in which the interaction rules and logic between the individual, the individual, and the environment are preset, and then the group evolves according to these established rules. The main rule model is the element cellular automata model [15; 16; 17]. Cellular automata effectively solve the discretization problem of evacuation models in time and space dimensions through space-time modeling. However, this model also has certain limitations: since the cellular automaton only sets the conversion rules of cellular states, it is difficult to describe microscopic behaviors from the perspective of personnel fully; at the same time, the set of cellular states is limited and cannot depict the complex behaviors of personnel.