Automobiles have become the main means of transportation for human beings, and their failures in the process of operation are directly related to the life and property safety of drivers. Therefore, automobile fault diagnosis and early warning technologies have become urgent problems in the current academic world. The premise of real-time accurate diagnosis and early warning of automobiles is to obtain high-quality information data in real time, but the automobile operating environment is complex and changeable, resulting in the measured information data under the influence of multiple factors, such as equipment performance and signal interference. There is an unpredictable measurement error, which greatly affects the reliability of fault diagnosis and early warning systems. In this paper, on the basis of studying the structure and operation characteristics of automobiles, we design a method that can be used for real-time diagnosis and early warning of automobile faults; through the study of fractional-order calculus theory, we establish a mathematical model of information data fusion based on fractional-order differential operators. By providing high-quality information data to automobile fault diagnosis and early warning systems, real-time and accurate diagnosis and early warning functions for automobile faults can be realized. The feasibility and effectiveness of the method were verified through an experiment applying the technology in automobile fault diagnosis and early warning. The research results are of great significance for promoting the development of the automobile industry and ensuring the safety of drivers' lives and property.

翻译：汽车已成为人类的主要交通工具，其运行过程中的故障直接关系到驾驶员的生命财产安全。因此，汽车故障诊断与预警技术成为当前学术界亟待解决的问题。实现对汽车实时准确诊断与预警的前提是实时获取高质量的信息数据，但汽车运行环境复杂多变，导致所测信息数据受设备性能、信号干扰等多因素影响，存在不可预知的测量误差，极大影响了故障诊断与预警系统的可靠性。本文在研究汽车结构与运行特性的基础上，设计了一种可用于汽车故障实时诊断与预警的方法；通过对分数阶微积分理论的研究，建立了基于分数阶微分算子的信息数据融合数学模型。通过为汽车故障诊断与预警系统提供高质量的信息数据，可实现汽车故障的实时、准确诊断与预警功能。通过将该技术应用于汽车故障诊断与预警的实验，验证了该方法的可行性与有效性。研究成果对推动汽车产业发展、保障驾驶员生命财产安全具有重要意义。