Dynamic assessment method for human factor risk of manned deep submergence operation system based on SPAR-H and SD

Abstract

Due to the complex and changeable contextual environment, the human factor risk of the manned deep submergence operating system shows dynamic characteristics. Compared with the traditional static human reliability analysis (HRA) method, dynamic HRA method can better simulate the dynamic characteristics and the nonlinear information feedback mechanism of the operating system. This paper proposed a dynamic risk assessment model based on System Dynamics and SPAR-H. The cognitive load was introduced into the Performance Shaping Factor (PSF) network to make it more suitable for the task and environment of manned deep submergence. In addition, in order to measure the compensation effect of PSF on the work efficiency of oceanauts, eight compensation functions were constructed between PSFs and work efficiency. Finally, key risk tasks and sensitive PSFs were identified. Taking the 12 h manned deep diving mission as an example, the dynamic quantification of human error probability and work efficiency of oceanauts was simulated. The results indicate that the dynamic simulation results of the constructed risk assessment model are consistent with the actual situation, and can effectively predict the changes of dynamic risk.