用动态贝叶斯与机器学习模型，实时预测核电操作员态势感知能力。
A Dynamic Bayesian and Machine Learning Framework for Quantitative Evaluation and Prediction of Operator Situation Awareness in Nuclear Power Plants
- 融合贝叶斯推理与神经网络，建模多层认知因素的时序演化。
- 预测误差仅13.8%，与人工评估结果无统计差异。
- 适合核电人因安全研究与智能控制室系统开发人员。
操作员态势感知是复杂核电控制环境中人类可靠性的重要但难以捉摸的决定因素。现有评估方法如SAGAT和SART仍为静态、事后且脱离动态认知过程。为此，本文提出动态贝叶斯机器学习态势感知框架（DBML SA），融合概率推理与数据驱动智能，实现可量化、可解释、可预测的态势感知建模。基于212份2007至2021年的运行事件报告，该框架重构了11个性能影响因素在多个认知层级上的因果时序结构。贝叶斯部分实现不确定性下的动态推断，神经网络部分从性能影响因素（PSFs）到SART评分建立非线性预测映射，平均绝对百分比误差为13.8%，与主观评估具统计一致性（p > 0.05）。结果表明培训质量与压力动态是态势感知退化的主因。总体而言，DBML SA突破传统问卷评估局限，支持实时认知监控、敏感性分析与早期预警预测，为下一代数字化主控室的人机可靠性智能管理铺平道路。
原文摘要 · Abstract (English)
Operator situation awareness is a pivotal yet elusive determinant of human reliability in complex nuclear control environments. Existing assessment methods, such as SAGAT and SART, remain static, retrospective, and detached from the evolving cognitive dynamics that drive operational risk. To overcome these limitations, this study introduces the dynamic Bayesian machine learning framework for situation awareness (DBML SA), a unified approach that fuses probabilistic reasoning and data driven intelligence to achieve quantitative, interpretable, and predictive situation awareness modeling. Leveraging 212 operational event reports (2007 to 2021), the framework reconstructs the causal temporal structure of 11 performance shaping factors across multiple cognitive layers. The Bayesian component enables time evolving inference of situation awareness reliability under uncertainty, while the neural component establishes a nonlinear predictive mapping from PSFs to SART scores, achieving a mean absolute percentage error of 13.8 % with statistical consistency to subjective evaluations (p > 0.05). Results highlight training quality and stress dynamics as primary drivers of situation awareness degradation. Overall, DBML SA transcends traditional questionnaire-based assessments by enabling real-time cognitive monitoring, sensitivity analysis, and early-warning prediction, paving the way toward intelligent human machine reliability management in next-generation digital main control rooms.
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