To solve the trouble of cervical vertebra disease caused by long working hours, this work is aimed at designing an active monitor stand to achieve the best dynamic trajectory planning. The stand operation system consists of image input, face positioning, and robotic arm. According to the user’s face positioning, the inverse kinematics method is used to calculate the steering angle in each direction, and the height and angle are adjusted to make the corresponding selection for the user’s display position on the screen. The C++ is used to program the algorithm, and its core is to control the rotation speed of the motor to ensure that the display screen will not cause any interference to the normal work of the user when the bracket moves. In the inverse operation, the Piper method is used to solve the inverse kinematics issues. After testing, the sliding wedge stroke of the developed active display stand is 0.6 mm, and the height position difference at the reference zero point after the display stand is measured with a digital altimeter is 7.12 mm, which indicates that the stand shows a large deformation after being mounted. This work is of important reference value for realizing the movement of active display stand.
Aiming at the problem of large mean square error of regression coefficient in traditional e-commerce platform consumer purchase behavior preference analysis, this paper designs an e-commerce platform consumer purchase behavior preference analysis based on lightgbm algorithm. Firstly, the index system of consumer purchase behavior preference on e-commerce platform is constructed. Then, based on lightgbm algorithm, the classification variables of consumer purchase behavior preference data are trained. Then, the weight of consumer purchase behavior preference index is calculated. Finally, the analysis curve of consumer purchase behavior preference on e-commerce platform is fitted to realize preference analysis. The experimental results show that the mean square error of regression coefficient of the experimental group is significantly lower than that of the control group, which can solve the problem of large mean square error of regression coefficient of traditional preference analysis.
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