关键词: 智能网联汽车；汽车故障诊断；多源异构数据融合；AI 预测模型

Abstract: To address the problems of traditional vehicle fault diagnosis, such as reliance on manual experience, low recognition rate of hidden faults, and diagnostic lag, this paper constructs a new approach to fault diagnosis by integrating intelligent connected technology. An overall architecture of "multi-source heterogeneous data fusion+artificial intelligence (AI) dynamic diagnosis+vehicleroad-cloud collaborative tracing" is designed. Comparative verification through an experimental platform shows that this approach achieves a diagnostic accuracy of 96.7%, reduces the average diagnosis time by 90.4% compared to traditional methods, and enables early warning 22.3 h in advance, effectively solving the pain points of traditional diagnosis. On this basis, an adaptive teaching system is designed from three aspects: reconstruction of teaching content, integration of virtual and real modes, and construction of resource platforms. In pilot applications, the pass rate of the fault diagnosis experimental assessment increases from 78% to 92%, the average project completion quality score reaches 85, and the monthly downloads of teaching resources exceeded 5 000, significantly improving students' practical abilities. This research provides a practical reference for the upgrading of vehicle diagnostic technology and the cultivation of professional talents.

Key words: intelligent connected vehicles; vehicle fault diagnosis; multi-source heterogeneous data fusion; AI prediction model