To rapidly detect the wheat moisture content (WMC) without harm to the wheat and before harvest, this paper measured wheat and panicle moisture content (PMC) and the corresponding spectral reflectance of panicle before harvest at the Beijing Tongzhou experimental station of China Agricultural University. Firstly, we used correlation analysis to determine the optimal regression model of WMC and PMC. Secondly, we derived the spectral sensitive band of PMC before filtering the redundant variables competitive adaptive reweighted sampling (CARS) to select the variable subset with the least error. Finally, partial least squares regression (PLSR) was used to build and analyze the prediction model of PMC. At the early stage of wheat harvest, a high correlation existed between WMC and PMC. Among all regression models such as exponential, univariate linear, polynomial models, and the power function regression model, the logarithm regression model was the best. The determination coefficients of the modeling sample were: R² = 0.9284, the significance F = 362.957, the determination coefficient of calibration sample R²v = 0.987, the root mean square error RMSEv = 3.859, and the relative error REv = 7.532. Within the range of 350–2500 nm, bands of 728–907 nm, 1407–1809 nm, and 1940–2459 nm had a correlation coefficient of PMC and wavelength reflectivity higher than 0.6. This paper used the CARS algorithm to optimize the variables and obtained the best variable subset, which included 30 wavelength variables. The PLSR model was established based on 30 variables optimized by the CARS algorithm. Compared with the all-sensitive band, which had 1103 variables, the PLSR model not only reduced the number of variables by 1073, but also had a higher accuracy in terms of prediction. The results showed that: RMSEC = 0.9301, R²c = 0.995, RMSEP = 2.676, R²p = 0.945, and RPD = 3.362, indicating that the CARS algorithm could effectively remove the variables of spectral redundant information. The CARS algorithm provided a new way of thinking for the non-destructive and rapid detection of WMC before harvest.

With the intensification of global environmental pollution and the energy crisis, the new energy EV industry is developing rapidly, and FWID-EV is a popular direction for future vehicle development. For the sake of improving the swing regulate steadiness and safety of EV, the study uses a particle swarm algorithm to optimize and improve the BP neural network PID, and designs an EV steering regulator to regulate the transverse swing torque and slip rate of EV to improve the safety and steadiness of EV steering. The research results display that the maximum value of the transverse swing angular velocity of the regulation algorithm is 0.156 rad/s, that the car slip rate is controlled within 0.046, and the steadiness is high, and that the maximum values of the car torque under the double shift line and snake conditions are 100 N-m and 179.4 N-m, respectively, which can effectively avoid the danger caused by steering. This demonstrates that the improved neural network PID regulator can effectively distribute the steering torque of the EV and improve the steering steadiness and safety of the EV while maintaining the driving dynamics. The use of the improved neural network PID algorithm to achieve the steering steadiness regulation of EV is of great significance to improve the safety of new energy EV, and helps to promote the widespread use of new energy EV.

Aiming at the poor stability of attitude control of mobile robot mechanism, a kind of attitude stability control system of mobile robot mechanism based on nanodisplacement sensor is designed. In the hardware part, a hydraulic drive is used to control the action posture of the mobile robot, a nanodisplacement sensor is used to collect the walking data of the robot, and serial communication of the upper computer is used to convert the data into electrical signals to realise the robot posture control. In the software part, the mathematical coordinate system of robot walking is constructed, and the rotating posture of the robot is controlled by the Euler angle. The experimental results show that the control performance of the designed system is stable and the control precision is high, which can realise the attitude stabilisation control of the mobile robot.