Quantification study of mental load state based on AHP–TOPSIS integration extended with cloud model: methodological and experimental research

Abstract

Abstract Mental load affects the work efficiency and mental health of operators, and it has a vital effect on the efficiency and reliability of human–machine systems. In this study, the evaluation index system of operators’ mental load was used to quantitatively evaluate the mental load state of workers. The system was established by selecting indices from the operators’ physiological parameters, subjective feelings, and time perception. We propose an extended cloud evaluation model of mental load states that combines cloud model (CM) theory with analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) and provides mental load levels. An energetic material initiation experiment was conducted to evaluate the mental load state of the operators using the proposed method, and the results of a fuzzy comprehensive evaluation and subjective questionnaire were used to verify the performance of the method. The results show that the extended CM evaluation method scientifically and reliably quantified the mental load state. Applying the AHP-TOPSIS integration extended with the CM theory evaluation method in mental load state evaluation provides a new scientific method for studying the quantification of the mental load state and occupational health of workers in hazardous environments. The results of this study are a reference for assessing the mental state of personnel and analyzing occupational suitability for dangerous posts.

Keywords

• Mental load
• Cloud model
• Multi-attribute decision-making
• AHP
• TOPSIS
• Level evaluation