As an effective way of improving traffic efficiency, vehicle platoon control has attracted extensive interest recently. Communication between vehicles tends to be affected by communication noises. Aimed at improving communication efficiency, an event-triggered vehicle platoon control under random communication noises is studied in this paper. First, for vehicle platoons with linear third-order dynamics, a time-varying consensus gain c(t) is introduced to reduce the effects of the communication noises. Second, with the introduction of the algebraic graph theory and matrix analysis theory, conditions for internal stability and l p -string stability under random additive communication noises are derived. Third, by utilizing the Lyapunov approach and Itô stochastic differential equations, the consensus of vehicle platoon under random additive communication noises is proved. Last, to reduce the frequent communication between vehicles, an event-triggered mechanism is introduced, and the design for the triggering parameter is derived. The effectiveness of the proposed method is verified with some numerical simulations.

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To make the computer useful in the field of artificial intelligence in the context of big data. In this paper, based on the analysis and comparison of the big data algorithm model and artificial intelligence algorithm in computers, we propose an algorithm based on a decision tree and logistic regression model in big data to query literature papers in the field of artificial intelligence as an example and compare and analyze the accuracy, accuracy, completeness, and F1 value of the obtained data through two categories of experiments. The experimental results show that the decision tree and logistic regression algorithm model based on big data can make the data finding accuracy of 89%, accuracy of 92%, and completeness of 87%, and optimize the speed and quality of the computer algorithm in the process of processing big data. This shows that the computer in the context of big data can provide access to data through algorithmic models in the field of artificial intelligence, which can improve the accuracy and authenticity of data sources and provide data support for in-depth research in the field of artificial intelligence.

Digital technologies such as the Internet, big data, cloud computing, mobile communication, and artificial intelligence have played an important role in the prevention and control of this new crown epidemic. Driven by big data technology, collaborative governance in the digital era has injected new dynamics for collaborative governance in the general sense, especially in this new crown epidemic prevention and control shows a different governance logic from the past, and the refinement and extraction of this governance logic is the key to enhance the ability of new crown epidemic prevention and control. Compared with the previous model in which a single governmental governance body played a role. In this paper, the necessity of building a big data analysis system is clarified by analyzing the limitations of the data analysis means of the existing trip code system. The overall epidemic prevention advantage of adopting the big data trip code policy has increased by 51.95% for regions with high crisis risk level and strong economic power, such as Guangdong, Zhejiang and Shanghai, which need to face the risk spread from domestic and imported risks under the big data trip code control, the increase of new cases compared to the previous comparison has decreased by 68.5%, which can visually see the big data trip code epidemic prevention We can visually see the effectiveness of big data trip codes in preventing the epidemic and making excellent contributions to the global fight against the epidemic.