In recent years, sports injuries in professional tennis players have gradually increased and sports injuries will break the sports training system and affect the long-term growth of new tennis players. Avoiding athlete injuries has become an important factor in improving training quality and game performance and ensuring the sustainable development of young tennis players’ competitiveness. Therefore, this article will use the RBF neural network algorithm and cluster analysis method to establish a tennis sports injury risk early warning model and finally establish a tennis sports injury risk early warning system so that tennis players can reduce their injuries. In this article, we use the questionnaire survey method, expert interview method, mathematical statistics method, and logical analysis method to investigate and analyze the results of training injuries of Chinese tennis players and coaches. The experimental results in this article show that among 48 tennis players of different ages, who are participating in formal training and tennis competitions, 15 young tennis players have been injured more than 6 times, accounting for 31.2% of the total; 20 have been injured 3 to 6 times, accounting for 41.7% of the total; 9 of them have been injured several times, accounting for 18.8% of the total; and 4 have been injured, accounting for 8.3% of the total. After using the tennis sports injury risk warning system based on the algorithm of RBF neural network in mobile computing, the tennis sports injury rate has dropped to 5%. It can be seen that the system has high feasibility and practicability.

Problems such as excessive centralization of sports industry operations, unclear value, and lack of IP protection hinder the benign interaction between various formats of the sports industry and the healthy operation of the industrial ecosystem. This article is aimed at studying the use of the decentralization, openness and transparency, smart contracts, copyright traceability, and other characteristics of the BT to construct a PCMR model, which can solve the pain points faced by the sports industry ecosystem in a targeted manner, and forming a way to reconstruct trust, the specific implementation path of the integration of BT and sports industry with mechanism as the core. This paper proposes blockchain network and data communication algorithms such as consensus algorithm, signature core algorithm, and practical Byzantine fault-tolerant algorithm to provide technical support for the application of blockchain technology in the sports industry ecosystem and, at the same time, provide technical support for blockchain network and data communication. The feasibility analysis of the application of the sports industry ecosystem is proposed, and the implementation path of the blockchain network and data communication to reconstruct the sports industry ecosystem is analyzed. Finally, the empirical test of the support of blockchain sports to the sports industry is carried out. By analyzing the panel data of 240 observations of 48 listed companies in 5 years as a sample of the sports industry, the experimental results show that the dominant modulus of the sample does not have other differences, and the leftovers all pass significance test at the 10% level at least, denoting that the fundamental sample is more rational.

：The conventional monitor mode of intensive crowd depends primarily on manpower or video surveillance, which employed to manage the crowd to prevent stampede is very difficult to implement effectively. For the purpose of judging the state aggregation of pedestrian in time, in this paper the correction coefficient is presented by comparing the measured with the mobile phone location data, based on mobile phone positioning technology to make the implementation of monitoring and alert classification of dense population more practical, in 2015 Beijing Ditan Temple Fair. Furthermore, through the numerical simulation analysis of evacuation of the BaiTai of Ditan' crowd, the reasons of delay time for evacuation have been found out as well as the advancement of supervision. This research method and the conclusion in this paper can be applied for early warning and forecast of preventing the stampede on crowded places and safety evacuation. Research Background Along with continuous economic and social development, acceleration of urbanization and gradual prosperity of commercial and trade activities, the risk of stampede accident resulting from dense crowds has been increasing, and the safety management of dense crowds has become more difficult. The conventional monitoring mode of intensive crowd depends primarily on manpower or video surveillance, which makes it very difficult to carry out real-time all-round monitoring in densely-populated areas with multimode vertical-crossing walking facilities and complicated internal distribution. Traditional on-site monitoring merely plays the role of alarming for disposal, and lacks the forecasting and early-warning of dense crowd risk, therefore making it difficult to attain the goal of "prevention beforehand". Therefore, it appears very necessary to set up a set of dense crowd risk monitoring and early-warning system by using the mobile phone location safety technology, judge the aggregation state of pedestrians in time, and control the behaviors that may give rise to risks. At present, the theory of mobile phone network location is relatively mature. By collecting mobile phone network location data and connecting the path matching algorithm, we can acquire the travel tracks of mobile phone users, including travel time, average speed and travel distance information. By making full use of Internet, database and GIS technology, we can collect and analyze various key protected targets, danger sources and emergency data of cities, timely and effectively assemble various resources, carry out emergency measures, and offer assisted decision-making to emergency command, so as to reduce the threats of emergencies to resident health, property and life safety, perfect the emergency reaction mechanism of government to public emergency events, and set up an all-round "safety network" of emergency early-warning and treatment for cities. The collection technology of traffic information based on mobile phone location has aroused the universal attention of many domestic and overseas scientific re...

The importance of digital information collections is growing. Collections are typically represented with text-only, in a linear list format, which turns out to be a weak representation for cognition. We learned this from empirical research in cognitive psychology, and by conducting a study to develop an understanding of current practices and resulting breakdowns in human experiences of building and utilizing collections. Because of limited human attention and memory, participants had trouble finding specific elements in their collections, resulting in low levels of collection utilization. To address these issues, this research develops new collection representations for visual cognition. First, we present the image+text surrogate, a concise representation for a document, or portion thereof, which is easy to understand and think about. An information extraction algorithm is developed to automatically transform a document into a small set of image+text surrogates. After refinement, the average accuracy performance of the algorithm was 90%. Then, we introduce the composition space to represent collections, which helps people connect elements visually in a spatial format. To ensure diverse information from multiple sources to be presented evenly in the composition space, we developed a new control structure, the ResultDistributor. A user study has demonstrated that the participants were able to browse more diverse information using the ResultDistributor-enhanced composition space.