Given the problems of using traditional training methods and insufficient funds in college sports agility training, the agility training system based on Wireless MESH Network is developed. The lower computer realizes the automatic networking between nodes based on the ESP-MESH network, and describes the networking process, intra-group communication and network management of the MESH network in detail. When the number of network layers ≤ 2, the node response time is about 300ms, and the packet loss rate is close to 0, it is proved that the Wireless MESH Network can transmit network data in real-time. The upper computer adopts the software design based on Android, which can view the agility training time of each point in the movement. In this paper, 10 university sports students were trained in stages for up to 9 weeks with the aid of an agility apparatus. After the training, the ability of rapid direction change, movement change and decision-making related to agile quality was significantly improved compared with those before the training (p<0.01). The experimental results show that agile coaches are practical in improving college students' agility.

The classic Photovoltaic system maximum power point tracking technique cannot concurrently take into account the dynamic response speed and steady-state accuracy when the light intensity changes. To address this issue, a new composite variable step MPPT control algorithm is developed in this study. Based on the three-stage variable step incremental conductance method, the algorithm adds the Kalman filtering algorithm to pre-process the photovoltaic cells output signal, and uses a new calculation approach to adjust the variable step coefficient. As a result, the perturbation step can be automatically modified according to changes in the external environment, which resolves the issues with poor dynamic reaction speed when the classic variable step algorithm started and the light changed. Compared to conventional MPPT control algorithms, the improved MPPT strategy can be easily realized using a hardware control system since it has a simplified control logic and requires less data to be calculated. In this study, the hardware circuit of the enhanced MPPT control algorithm is built using the ESP32 as the primary control chip. This chip can be utilized in conjunction with the Internet of Things to enable remote monitoring of the solar power system’s operational state. According to test results, the algorithm can instantly detect the maximum power point in all lighting circumstances with tracking accuracy of up to 99.6% and a reduction in dynamic response time of the system to 0.12 s.

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