Because the oceanaut plays a significant role in safety and capability during manned deep-diving scientific tasks, preventing oceanaut performance decline is of paramount importance. However, the factors responsible for oceanaut performance are almost entirely unexplored. To address the preceding issues, a quantitative method of fuzzy integrated Bayesian network (FIBN) was modeled within the limits of oceanaut operating procedures. To quantify the probabilities of the influencing factors, the probability of each node in the FIBN was calculated using integrated expert judgement, fuzzy logic theory, and Bayesian network. By considering a total of 28 factors related to oceanaut performance in the “Jiaolong” manned submersible, this study found that difficult sampling, long sampling times, cabin equipment failure, oceanaut physical decline, and declining decision-making ability are important factors that affect oceanaut performance. The FIBN proposed in our study fused the qualitative and quantitative methods and can be developed into a versatile tool for analysis of comprehensive systems that contain both static and dynamic factors.

Relevance to industry

The results provide a powerful basis for the design of manned submersible and assignment of tasks to oceanauts, while the fuzzy integrated Bayesian network (FIBN) method proposed can be effectively applied to various quantitative assessment fields which direct researchers to deal with analysis problems of complex systems.

中文翻译：

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利用模糊逻辑理论的贝叶斯综合网络对海员的运行性能进行分析

由于在载人深潜科学任务中，海员在安全和能力方面起着重要作用，因此防止海员的性能下降至关重要。但是，导致航海人员表现的因素几乎是完全未知的。为了解决上述问题，在海洋航行者操作规程的范围内对模糊综合贝叶斯网络（FIBN）的定量方法进行了建模。为了量化影响因素的概率，使用集成的专家判断，模糊逻辑理论和贝叶斯网络来计算FIBN中每个节点的概率。通过考虑与“ J龙”载人潜水器中的海员性能相关的总共28个因素，这项研究发现，取样困难，取样时间长，机舱设备故障，海员体力下降，和决策能力的下降是影响海员绩效的重要因素。在我们的研究中提出的FIBN融合了定性和定量方法，可以发展成为分析包含静态和动态因素的综合系统的通用工具。

与产业的关系

研究结果为有人值班潜水器的设计和任务分配提供了有力的依据，而所提出的模糊综合贝叶斯网络（FIBN）方法可以有效地应用于各种定量评估领域，指导研究人员处理复杂系统的分析问题。 。