## 1 Introduction
In the context of ports, researchers have primarily studied loading and unloading operations, port equipment, and dangerous goods. For instance, [PERSON] et al. [18] developed a novel region-specific risk assessment model for container ports. This model divides the container port into four areas--the loading and unloading area, internal transportation area, storage area, and gate area--based on the container transport process. By combining Failure Mode and Effect Analysis (FMEA) with a quantitative risk analysis model, it assesses the risk of accidents in each segment of the process. [PERSON] et al. [19] proposed an advanced FMEA method based on a fuzzy rule-based Bayesian network (FRBN) to evaluate the severity of hazardous events (HEs) at container terminals. [PERSON] and [PERSON] [20] developed a risk assessment model for container ports using hazard identification, risk assessment, and fault tree analysis methods. [PERSON] et al. [21] analyzed the risk factors affecting safe loading and unloading operations, their causal relationships, and interconnections. They used the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to evaluate the core risk factors for container handling operations at Taiwan's Kaohsiung Port. Other methods include safety assessment models for dangerous goods within the port [22; 23]. In maritime navigation, various types of risk assessments are employed. Previous studies on quantitative risk analysis of maritime navigation have utilized different methods for risk evaluation, each with distinct applications, such as event trees (ETs), fault trees (FTs) [24], Bayesian networks (BNs) [25; 26], and the Analytic Hierarchy Process (AHP) [27; 28]. ETs and FTs are particularly useful for analyzing the causal effects of specific risks and are easy to understand. However, they rely heavily on historical data and can become extremely time-consuming as the number of factors increases. BNs, on the other hand, are quantitative tools applied to maritime traffic modeling and have indeed extended into the field of maritime traffic safety. While BNs allow for the integration of data with expert knowledge and are suitable for complex systems with probabilistic uncertainties, their complexity and the difficulty in determining expert probabilities can present challenges [29]. A summary and analysis of the literature are presented in Table 1.  
Currently, few risk assessment methods directly target quark crane operators at container ports, and they mostly focus on real operating scenarios, with limited research on risk assessment in virtual environments. Therefore, this paper proposes an automated evaluation method for the risk behaviors of quay crane operators at ports. Building upon the existing automated quay crane remote operation simulator, a risk simulation module is designed, and a Deep Q-Network (DQN) model is constructed to learn the operational methods and specifications of skilled operators. This model, trained using a large amount of simulated operational data, accurately reflects the operational behaviors and decision-making processes of skilled operators. Based on this model, a method for evaluating operators' risk behaviors is proposed. Through interaction with the baseline model, the system can simulate various operational scenarios, monitor and evaluate operators' behaviors in real time, and provide an objective and effective assessment of their risk behaviors.