Vehicle driving safety state real-time monitoring and early warning method based on machine vision and big data analysis

Abstract

The invention discloses an automobile driving safety state real-time monitoring and early warning method based on machine vision and big data analysis, and the method comprises the steps: carrying out the statistical marking of tires on an automobile, and carrying out the damage detection, wear detection, deformation monitoring and tire pressure detection of each tire; counting the damage danger coefficient, the wear danger coefficient, the deformation danger coefficient and the tire burst danger coefficient corresponding to each tire on the automobile according to the detection result, and further calculating the driving state comprehensive danger coefficient corresponding to each tire on the automobile comprehensively, so the safety monitoring of the automobile tires is realized, the monitoring index range is perfected, the defect of single monitoring index of the current automobile tire safety monitoring mode is overcome, the reliability of the monitoring result is improved, the monitoring level of the automobile driving safety is improved, the occurrence of automobile traffic accidents caused by potential safety hazards existing in the driving process of the automobile tire is greatly reduced, and the automobile driving safety is further guaranteed.

Description

technical field [0001] The invention belongs to the technical field of vehicle driving monitoring, in particular to a real-time monitoring and early warning method for vehicle driving safety status based on machine vision and big data analysis. Background technique [0002] With the improvement of people's living standards, cars have become an indispensable means of transportation in people's daily life. With the rapid increase of ownership, the traffic is more and more congested, which makes traffic accidents more frequent. In order to reduce the occurrence of traffic accidents, this requires us to pay attention to and strengthen the detection of the driving state of the car. Therefore, the detection and management of the driving state of the car has become an important guarantee for the harmony and safety of the family and the whole society. [0003] The current causes of automobile traffic accidents are not only caused by the driver's bad driving habits, but also caused ...

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Problems solved by technology

[0004] In order to make up for the above deficiencies, the present invention proposes a real-time monitoring and early warning method for automobile driving safety status based on machine vision and big data analysis. By performing damage detection, wear detection, deformation monitoring, and tire pressure detection on the tires on the automobile, the statistics of automobile tire The corresponding damage risk coefficient, wear risk coefficient, deformation risk coefficient and tire blowout risk coefficient, and then comprehensively calculate the comprehensive risk coefficient of the driving state of the car tire, which effectively overcomes the lack of a single monitoring index in the current car tire safety monitoring method

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