AI driving risk analysis method based on multi-dimensional data, server and storage medium

Abstract

The invention relates to the artificial intelligence technology, and discloses an AI driving risk analysis method based on multi-dimensional data, and the method comprises the steps: enabling an analysis server to obtain a to-be-analyzed image, sent by a terminal, of a target user; calling one or more pre-trained face recognition models to recognize the to-be-analyzed picture so as to recognize face risk characteristics of a preset type; extracting user data corresponding to a target user from a predetermined database, and extracting structural feature data of the preset type from the user data; and splicing the preset type of face risk features and the preset type of structural feature data into a multi-dimensional comprehensive risk feature, and inputting the comprehensive risk feature into a pre-trained driving risk analysis model to output a comprehensive risk analysis value. The invention further discloses an analysis server and a computer storage medium. According to the invention, the accuracy and objectivity of driving risk analysis can be improved.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to an AI driving risk analysis method based on multidimensional data, an analysis server and a computer-readable storage medium. Background technique [0002] In recent years, my country's transportation industry has developed vigorously, and the number of residents' car ownership has increased significantly, followed by frequent road traffic accidents, such as vehicle rear-end collisions, rollovers, etc. Most of these traffic accidents are due to the danger of drivers Caused by driving behavior, such as excessive speed, fatigue driving, etc. [0003] It is of great significance to accurately and objectively analyze the driver's driving risk in advance. For example, an auto insurance company can determine the risk level of auto insurance based on the accurate analysis value of driving risk; the traffic control department can determine the driver's The duratio...

AI Technical Summary

Problems solved by technology

[0004] Although there are solutions on the market for analyzing the driver's driving risk, the existing solutions are usually developed based on the inertial thinking of those skilled in the art, which leads to the fact that the existing solutions are usually based on the driver's age, Structural characteristics such as gender, loss rate in previous years, and accident situations are used to analyze driving risk. The accuracy of the analysis results is usually low and mistakes are often made

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