Human-computer Interaction Evaluation Method for Nuclear Power Plant Control Room Based on Operator Physiological Characteristics

Abstract

In the design optimization process of the control room in a nuclear power plant, personnel workload is an important evaluation indicator. A well-designed control room can effectively reduce the workload of operators during their work processes, improve work outcomes and efficiency, and reduce the possibility of accidents caused by human errors. Currently, subjective evaluation methods are mainly used to evaluate personnel workload in related research. These methods are simple and easy to implement with short evaluation times, but they are highly subjective and difficult to perform a comprehensive and objective quantitative evaluation. Physiological measurement method is a research approach that observes and measures the physiological data changes related to behavior in order to analyze the state of individuals, providing more objective and reliable quantitative results. With the advancement of sensor and computer technology, it has become a hot research topic. Among them, physiological characteristics such as electroencephalography (EEG) and eye movement are widely studied, and their relationship with the psychological and mental states of individuals have been fully medically validated. This paper proposes a method for evaluating the workload of nuclear power plant control room operators by collecting EEG and eye movement physiological signals and analyzing their features using advanced machine learning algorithms. It also explores evaluation methods for control room design achievements.

Keywords: personnel workload evaluation, eye movement, EEG, nuclear power plant control room

DOI: 10.54941/ahfe1004046