At present, the health monitoring system of world sports events uses the system's on-site information or periodic test information to evaluate the health of the system. In order to make the data more accurate, the application of cloud computing is necessary. This article is based on the cloud computing spatial structure sports event health monitoring Internet of Things system, aiming to analyze the data from the structure's resilience, surface load and other aspects, combined with cloud computing means to explore this monitoring system. This research uses literature retrieval methods, cloud computing particle swarm optimization and interpolation fitting methods to process data. Aiming at the data characteristics of spatial structure health monitoring, the overall framework of the Internet of Things system for spatial structure health monitoring is proposed. Taking advantage of cloud computing in processing computing-intensive tasks, an algorithm for data processing at the application layer is established, and the cloud data for spatial structure monitoring is completed. Management system design. Among them, the Internet of Things system, structural health monitoring system and other models are mainly combined with the various levels of the model to carry out targeted analysis; the factors involved are the arrangement of sensors and the installation of sensors at the junctions, for example, magnetoelectric acceleration sensors and optical fiber optical shed sensors for data recording of large stadiums with different structures. The experimental results show that when extreme temperatures exist, the stress of the rods as a whole will decrease to a certain extent. The maximum difference between the test data considering extreme weather and not considering extreme weather is 226.

Traffic safety plays a crucial role in the development of autonomous vehicles which attracts significant attention in the community. It is a challenge task to ensure autonomous vehicle safety under varied traffic environment interference, especially for airport-like closed-loop conditions. To that aim, we analyze autonomous vehicle safety at typical roadway conditions and traffic state constraints (e.g., car-following state at different speed distributions) by simulating the airport-like traffic conditions. The experimental results suggest that traffic collision risk is in a positive relationship with the speed difference and distance among adjacent vehicles. More specifically, the autonomous vehicle may collide with neighbors when the time to collision (TTC) indicator is lower than 4 s, and vice versa. The research findings can help both research community and practioners obtain additional information for improving traffic safety for autonomous vehicles.