Numerical simulation of ocean sea temperature based on feature prediction and monitoring of coastal running health data

Abstract

The condition of ocean underlying surface is an important factor to determine the predictability of short-term climate. Different forms of ocean boundary conditions are often used in dynamic forecasting models for forecasting objects with different characteristic scales. Many institutions are still using a forced general circulation model (GCM) based on given SST to predict climate, but there are still differences in the degree of short-term climate prediction affected by different SST frequencies. Considering this, this paper uses the BCC model of the National Climate Center_ Agcm3 which is used to develop and conduct multiple sets of numerical simulations and experiments to predict the impact of SST at different frequencies of each month, week, and day. The results show that the frequency of SST forcing field is similar to that of Asian summer monsoon. As we all know, sprint and middle- and long-distance race belong to speed race group or endurance race group. The movement speed of two types of sports depends on their physiological mechanism, the transformation speed of cerebral cortex motor center, and the performance of nervous system. Although there are many studies on the speed of short-distance running and medium- and long-distance running, there are relatively few studies on the characteristics of middle- and long-distance runners in various sports. The system uses STM32 + MTK dual processor system and uses mpu9250 nine axis inertial sensor and max30102 heart rate sensor to develop acquisition module, positioning module, low-power consumption module, motion data detection module, and heart rate software and hardware system. Finally, this paper designs the results of SST numerical simulation and prediction experiment.