Plowing operations can improve the quality of the shallow soil layer, but plowing tractors still cause compaction effects on the deeper soil layers. The temporal and spatial accumulation of compaction effects can hinder crop growth, thereby reducing the overall efficiency of plowing operations and creating obstacles to soil ecological health. It is urgent to address the bottlenecks in mechanized ecological operations. This paper addresses the unclear mechanical effects of heavy-duty tractor tillage equipment on soil compaction in the tillage layer. By integrating and constructing a ‘‘heavy-duty tractor-tillage layer soil’’ system dynamics coupling model, it identifies the primary influencing factors of joint tillage operations on soil compaction in the tillage layer and explores the response patterns of tillage equipment parameters to the soil compaction process. The research results indicate that as the number of compaction operations increases, soil compaction increases, and soil stress transmission shows a gradually decreasing trend; when the tillage unit’s operating speed is 3 km/h, the soil stress at a depth of 10 cm can reach a maximum of 499.2 kPa; when the operating speed is 6 km/h, the soil stress at a depth of 10 cm reaches a maximum of 469.1 kPa; when the operating speed is 9 km/h, the soil stress at a depth of 10 cm reaches a maximum of 438.8 kPa; when the acceleration and longitudinal acceleration increase, soil stress correspondingly increases; when the direction of lateral acceleration changes, the center of gravity of the tractor shifts, and the trends in soil stress on the inner and outer sides of the tires are opposite. The research findings can provide theoretical references for improving the operational efficiency of the unit and the development of black soil protection and utilization technologies.

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.