Optimizing Bayesian networks for parametric modeling and intelligent risk mitigation in human factors engineering

Aiming at the problem of insufficient real-time dynamic risk modeling in Human Factors Engineering (HFE), this study proposes the Parameterized Bayesian Network (PBN-KL) optimization method. The method fuses multi-source physiological, cognitive, and environmental parameters and significantly improves risk prevention and control effectiveness through KL scatter-driven adaptive structure learning and real-time decision engine. In the validation of NASA-TLX and UCI-HAR datasets, the prevention and control accuracy is improved by 15.2%, the response delay is reduced by 40.7%, and the prevention and control success rate reaches 92.3% compared with the traditional method. The framework provides an interpretable and real-time active security solution for high interaction scenarios under the national strategic needs of intelligent manufacturing “digital twin” and precision medicine, bridging the gap between theoretical modeling and practical application needs.