The information exchange methods currently used in the field of vehicle
inspection are not in harmony with the social information construction
environment, which hinders the interaction and flow of information. On
the other hand, because a large amount of data collected by different
types of sensors must be transmitted to edge computing nodes, dynamic
vehicle detection will lead to network congestion and affect the
calculation of edge computing nodes. Therefore, optimizing the data
transmission between vehicles and edge computing nodes is a new
challenge, which needs to be solved in the practical application of
dynamic vehicle detection architecture based on edge computing. In this
paper, we have considered the data requirements of dynamic vehicle
detection in different environments, analyzed the optimization
objectives and constraints, and proposed deviation detection and greedy
algorithms to solve the problem of MILP solving the problem of long time
and insufficient practical application, and evaluated the performance of
the algorithm through simulation experiments using SUMO, a traffic flow
simulation tool, and PreScan, a vehicle simulation test software. The
results show that greedy algorithm is more suitable for practical
application than deviation detection algorithm. The implementation of
this system can improve the efficiency of vehicle detection and
management, and has good application value.