With the development of the automobile industry, artificial intelligence, big data, 5G, and other technologies, the Internet of Vehicles (IoV) industry has entered a stage of rapid development. In this paper, a pollutant diffusion model based on an artificial neural network is designed in the context of a vehicle network. The application of artificial neural networks in haze prediction is studied. This paper first analyzes the causes and influencing factors of haze and selects the most representative and relatively large meteorological factors from temperature, wind, relative humidity, and several pollutant factors. Through training and simulation, a haze prediction model in the Beijing, Tianjin, and Hebei regions of China is established. Finally, according to the collected meteorological data, the pollutant diffusion model is established. The model is deduced by a standard mathematical formula, which makes the prediction results more accurate and rigorous, and the main conclusions and feasible scientific suggestions are obtained. The simulation results show that the method is effective. By strengthening the service system of the IoV, meteorological services can be more intelligent, and the information acquisition and service ability of the vehicle network can be effectively improved.

中文翻译：

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车联网环境下PM2.5浓度影响因素分析及基于人工神经网络的污染物扩散模型设计

随着汽车产业、人工智能、大数据、5G等技术的发展，车联网产业进入快速发展阶段。本文在车辆网络的背景下设计了基于人工神经网络的污染物扩散模型。研究了人工神经网络在雾霾预测中的应用。本文首先分析了雾霾的成因和影响因素，从温度、风、相对湿度以及几种污染物因子中选取了最具代表性、影响较大的气象因子。通过训练和仿真，建立了我国京津冀地区雾霾预测模型。最后，根据收集到的气象数据，建立污染物扩散模型。该模型通过标准数学公式推导，使得预测结果更加准确和严谨，得出主要结论和可行的科学建议。仿真结果表明该方法是有效的。通过强化车联网服务体系，使气象服务更加智能化，有效提升车联网信息获取和服务能力。