Tractor driver psychological load evolution paradigm and experimental verification

Developments in agricultural mechanization have witnessed a gradual transition from manned equipment to unmanned equipment. Meanwhile, the psychological load of the tractor drivers’ original field operations has been transferred to unmanned tractor monitors. This study constructed a psychological load paradigm model and identified the physical meaning of parameters based on the leaky bucket principle and the basic hypothesis of ergonomics. The mapping architecture of the psychological load measurement principle was analyzed, and the feasibility of the questionnaire measurement method was demonstrated. Further, an evaluation questionnaire index system was designed. A continuous method was selected to conduct a man-machine semi-physical test to obtain an evolution paradigm model of six types of psychological loads using a multivariate nonlinear regression method. The structural parameters of the paradigm were analyzed, and the degree of coupling of psychological load generation and mitigation was deconstructed item by item. The driving mechanism and evolution law of psychological load were analyzed. Consequently, a real vehicle was designed and constructed and a topology test was conducted to verify the scientific applicability and universality of the paradigm model, respectively. The results confirmed that a continuous psychological questionnaire could effectively measure a driver’s psychological load. The interaction of various psychological loads constituted the distributed state- space of psychological load, and the dynamic paradigm model drove the psychological load of the human-computer interaction interface. The paradigm model evolution was a negative exponential growth model that included comfort and fatigue accumulation rates. With the accumulation of working time, the specific rules and parameters of the psychological load changed for different drivers, but the evolution paradigm was the same. According to the state-space analysis of the mental load model, the mental model exhibited controllability, observability, stability, and so on, which accurately revealed the evolution law of mental load. The research results provide a positive design for human-computer interaction.