This study proposes a Bayesian network-based method for evaluating the security of human-machine interaction systems. It considers the effects of time pressure and scenario fusion control transitions on group member performance factors (such as cognitive load, BDI, and distributed situational awareness), and their relationship with system security. By constructing a multi-agent discrete dynamic event framework, it investigates the independent impacts of time pressure and external environmental disturbances on system security and establishes a model for system security. Additionally, it analyzes various risk scenarios in aerospace systems, including ground risks, launch and in-orbit teleoperation risks, and extravehicular activity risks, while considering risk factors in different scenarios. Ultimately, the dynamic risk level of the system is assessed through dynamic Bayesian networks, resulting in a calculated accident probability of 0.14‰, demonstrating the effectiveness of the method. This research provides a new technological approach for evaluating the security of human-machine interaction and contributes to enhancing the safety of aerospace systems.
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Liu et al. (2024) studied this question.
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