A traffic violation analysis method based on knowledge graph and blockchain

Abstract

A method for analyzing traffic violations based on knowledge graphs and blockchains, comprising the following steps: after a violation occurs, obtain vehicle information according to the violation license plate and associate the knowledge graph, and match the possible driver of the vehicle from the knowledge graph; illegal driving The driver goes to the traffic control department to accept punishment, judges the suspected behavior of the violating driver, quickly queries the driver and vehicle violation information through the knowledge map, and predicts the occurrence of violations, which plays the role of early warning of violations; through blockchain technology Store, send and receive the driver's personal knowledge map information, increase data security, save the establishment of an information storage center, play a role in disaster recovery, and also make the information in the blockchain open and transparent, reducing the number of drivers Illegal activities such as buying and selling points and top packages.

Description

technical field [0001] The invention belongs to the field of artificial intelligence, and in particular relates to a traffic violation analysis method based on a knowledge map and a block chain. Background technique [0002] With the rapid development of the modern transportation industry, more and more people choose to travel by private car, but this has also led to an increase in vehicle violations. [0003] Today's vehicle violation analysis methods mainly rely on video surveillance technology. Monitoring equipment on the road, vehicle recorders, monitoring equipment in houses near the violation scene, and shooting by witnesses can greatly improve the effectiveness of this type of technology. After the illegal behavior occurs, it is only necessary to conduct forensic analysis on various collected information, and the correct judgment on the illegal behavior can be easily obtained. [0004] However, the basic data layer of the traditional traffic violation processing syst...

AI Technical Summary

Problems solved by technology

[0004] However, the basic data layer of the traditional traffic violation processing system mainly relies on the basic databases established by different government departments such as traffic and public security. Finally, the result of the violation is obtained, and after the violation result is processed, the violating driver needs to go to the designated bank to pay the fine

[0005] Although the road monitoring system is becoming more and more perfect, the monitoring coverage area is gradually expanding, and the image clarity of monitoring equipment is also improving, but it is inevitable that there will be situations that cannot be solved by video monitoring: there will be situations where monitoring or monitoring cannot be installed due to remote or special geographical location. In areas with a small number, the clarity of monitoring may also be insufficient. This will cause the monitoring terminal to only be able to obtain photos of violating vehicles, but cannot obtain photos of the driver corresponding to the violation. When dealing with clear traffic violations, the driver can use the violation Loopholes in the processing process can avoid point deduction by buying and selling driver’s license points, and even escape legal sanctions, and this type of technology can only detect and analyze traffic violations that have occurred, and cannot predict violations. early warning

[0006] Due to the limited channels of data collection and unreliable sources in the traditional traffic violation processing system, the data collection rate of the basic database is low, the database structure is not uniform, and the data is difficult to verify and share with each other. When dealing with violations, various departments need to cooperate with each other, greatly Reduce the efficiency of handling violations; and in the process of data transmission, data is easily leaked or tampered with, and data security is insufficient

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