COVID-19 has a comprehensive impact on China’s economy and labor market, and has a significant impact on the production and operation of Chinese enterprises. It urges enterprises to optimize and adjust their human resources in a more scientific way, so as to cope with the challenges brought by the COVID-19. By studying the human resource management strategies of enterprises under the COVID-19, this paper makes an in-depth analysis from five aspects: human resource security, employee relationship management, employee security, performance management and salary management, and provides Suggestions for enterprises by combining the Internet Plus new human resource management mode.

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The identification and positioning of a master is essential for the master-slave cooperative operation of agricultural machinery. This study aimed to develop an agricultural vehicle dynamic identification and tracking method for agricultural master-slave follow-up operation using improved YOLO v4 and binocular positioning. The regular pruning algorithm was used to trim the original YOLO v4 channel to achieve a fast and accurate identification of master vehicle. The principle of binocular vision positioning was used to calculate the position of the master. The method (IYO-BPO) proposed in this study fused target detection and binocular vision positioning together, which was used to identify and track the master vehicle in real time. To evaluate the performance of the developed method, RTK-GPS and a gyroscope were installed on the master vehicle to obtain the reference position and heading angle. Identification and tracking experiments of both the linear and S-shaped movement of the master were conducted. The RMS errors were 0.067 m, 0.203 m, and 2.602° in terms of the longitudinal, lateral, and heading angle deviation, respectively, when the master moved along a straight line. The results show that the master vehicle could be identified and tracked by the IYO-BPO correctly and effectively.

Tractors are prone to large slips when they are in field operation. The degree of slip plays a vital role in traction efficiency and fuel efficiency. This paper presents a method for measuring the slip ratio of tractors in field operation based on machine vision. The accurate measurement of slip ratio needs to obtain actual velocity and theoretical velocity separately. For obtaining the actual velocity, a monocular camera mounted on the tractor vertically faces down at the ground to collect images. Then, the feature points of inter-frame ground images are matched by the ORB (Oriented FAST and Rotated BRIEF) algorithm for calculating the translational displacement. Next, a homography matrix based on camera calibration is proposed to complete the transformation of a point from the pixel coordinate system to the world coordinate system. Aiming to acquire the theoretical velocity, a method that takes the variations in tire radius into account is proposed, and the tire radii of the driving wheels are indirectly determined by the tire inflation pressure in real-time. The proposed measurement method was verified with an experimental tractor. The results show that the mean absolute errors of the tractor driving wheels’ slip ratio measured by the machine vision method are less than 0.75%, and the maximum of the absolute errors is not more than 2.22%, which shows good performance.

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