Edge Computing Deployment Algorithm and Sports Training Data Mining Based on Software Defined Network.

The wireless sensor network collects data from various areas through specific network nodes and uploads it to the decision-making layer for analysis and processing. Therefore, it has become a perception network of the Internet of Things and has made great achievements in monitoring and prevention at this stage. At this stage, the main problem is the motive power of sensor nodes, so the energy storage and transmission of wireless sensor network is imminent. Mobile edge computing technology provides a new type of technology for today's edge networks, enabling it to process resource-intensive data blocks and feedback to managers in time. It is a new starting point for cloud computing services, compared to traditional cloud computing services. The transmission speed is more efficient and will be widely used in various industries and serve them in the future. Among them, education and related industries urgently need in-depth information, which in turn promotes the rapid development of data mining by sensor networks. This article focuses on data mining technology, mainly expounds the meaning and main mining methods of data mining technology, and conducts data mining on sports training requirements from the aspects of demand collection and analysis, algorithm design and optimization, demand results and realization, etc. Monitor the training status and give the trainer reasonable suggestions. Through the processing of the training data mining results and proofreading the database standardized training data, we can formulate a personalized program suitable for sportsmen, reduce sports injuries caused by no trainer's guidance, and open new doors for training modes. Therefore, this paper studies the sensor network technology, edge computing deployment algorithm, and sports training data mining.

Dynamic variations of the COVID-19 disease at different quarantine strategies in Wuhan and mainland China.

BACKGROUND: The Coronavirus Disease 2019 (COVID-19) firstly announced in Wuhan of Hubei province, China is rapidly spreading to all the other 31 provinces of China and to more than 140 countries. Quarantine strategies play the key role on the disease controlling and public health in the world with this pandemic of the COVID-19 defined by the World Health Organization. METHODS: In this study, a SEIRQ epidemic model was developed to explore the dynamic changes of COVID-19 in Wuhan and mainland China, from January 27, 2020 to March 5, 2020. Moreover, to investigate the effects of the quarantine strategies, two perspectives are employed from the different quarantine magnitudes and quarantine time points. RESULTS: The major results suggest that the COVID-19 variations are well captured by the epidemic model with very high accuracy in the cumulative confirmed cases, confirmed cases, cumulative recovered cases and cumulative death cases. The quarantine magnitudes in the susceptible individuals play larger roles on the disease control than the impacts of the quarantines of the exposed individuals and infectious individuals. For the quarantine time points, it shows that the early quarantine strategy is significantly important for the disease controlling. The time delayed quarantining will seriously increase the COVID-19 disease patients and prolongs the days of the disease extinction. CONCLUSIONS: Our model can simulate and predict the COVID-19 variations and the quarantine strategies are important for the disease controlling, especially at the early period of the disease outbreak. These conclusions provide important scientific information for the government policymaker in the disease control strategies.

Evaluation and prediction of the COVID-19 variations at different input population and quarantine strategies, a case study in Guangdong province, China.

In this study, an epidemic model was developed to simulate and predict the disease variations of Guangdong province which was focused on the period from Jan 27 to Feb 20, 2020. To explore the impacts of the input population and quarantine strategies on the disease variations at different scenarios, four time points were assumed as Feb 6, Feb 16, Feb 24 and Mar 5 2020. The major results suggest that our model can well capture the disease variations with high accuracy. The simulated peak value of the confirmed cases is 1002 at Feb 10, 2020 which is mostly close to the reported number of 1007 at Feb 9, 2020. The disease will become extinction with peak value of 1397 at May 11, 2020. Moreover, the increased numbers of the input population can mainly shorten the disease extinction days and the increased percentages of the exposed individuals of the input population increase the number of cumulative confirmed cases at a small percentage. Increasing the input population and decreasing the quarantine strategy together around the time point of the peak value of the confirmed cases, may lead to the second outbreak.