Abstract: In order to improve the big data analysis and traffic data management ability of the Jiangxi high-speed traffic condition monitoring platform, a multi-dimensional multi-scale analysis model of Jiangxi high-speed traffic condition characteristics based on vehicle networking and traffic big data information fusion is proposed. On the basis of building a good knowledge base, model base, and method base, the model base feature analysis and dynamic detection method are used to effectively mine big data of the Jiangxi high-speed traffic status monitoring platform. Combined with information fusion theory, the multi-dimensional model of the Jiangxi high-speed traffic status monitoring platform based on text information, location information, picture, audio, video, and other Jiangxi high-speed traffic status data can be realized. The test results show that big data mining in the platform with this method has better clustering and achieves high multi-dimensional multi-scale fusion of high-speed traffic condition characteristics in Jiangxi. The efficiency of data scheduling and access are improved effectively, enhancing the multi-dimensional multi-scale analysis and resource scheduling ability of Jiangxi high-speed traffic condition characteristics.

Key words: car networking, Jiangxi high-speed traffic, state characteristics, multi-dimensional multi-scale, big data