  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.
