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Suspect vehicle identification method based on big data analysis of bayonet passing vehicles

A passing-vehicle data and big data technology is applied in the field of suspect vehicle identification based on the big data analysis of bayonet passing vehicles, which can solve the problem of time-consuming and labor-intensive manual query matching.

Active Publication Date: 2021-02-02
千方捷通科技股份有限公司
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  • Abstract
  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

In this case, in order to find the suspect vehicle, manual query matching is time-consuming and laborious

Method used

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  • Suspect vehicle identification method based on big data analysis of bayonet passing vehicles
  • Suspect vehicle identification method based on big data analysis of bayonet passing vehicles

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Embodiment Construction

[0021] The specific implementation and effect of the method of the present invention will be described in detail below in conjunction with specific examples. It should be noted that the technical features or combinations of technical features described in the following embodiments should not be regarded as isolated, and they can be combined with each other to achieve better technical effects.

[0022] The present invention will be described in detail below in conjunction with specific embodiments.

[0023] 1. If figure 1 Build the Hadoop big data platform for distributed storage as shown;

[0024] 2. Collect passing vehicle data information at each bayonet, identify all passing vehicles through image recognition algorithms, and obtain vehicle license plate number, color, vehicle brand and other information;

[0025] 3. If figure 2 The checkpoints adjacent to the selected crime location are denoted as C={C1, C2, C3,...};

[0026] 4. Extract the vehicle passing record of ch...

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Abstract

The invention discloses a method for identifying a suspect vehicle based on big data analysis of bayonet passing vehicles. The method greatly reduces the process of searching for suspicious vehicles and improves the accuracy of identification. It includes the following steps: Based on the national motor vehicle inspection and deployment control system, collect real-time passing data information of each bayonet installed and deployed in the city and use the HBASE distributed database to organize and store the data in a distributed manner; On the same day and after the incident, the passing records of equipment numbers near the bayonet at the incident site are grouped to obtain the number of passing vehicles of each vehicle before the incident, on the day of the incident and after the incident; After the incident, the number of passing vehicles of each vehicle was analyzed and filtered according to the actual case conditions to obtain records of vehicles that frequently appeared before and on the day of the incident but did not appear after the incident. These vehicles were identified as suspect vehicles.

Description

technical field [0001] The invention belongs to the field of big data analysis, and in particular relates to a method for identifying a suspected vehicle based on big data analysis of bayonet passing vehicles. Background technique [0002] With the improvement of social living standards and the rapid development of highway intelligent monitoring technology, many cities are now building "safe cities" and "smart cities". It can be found that a large number of checkpoints have been installed and deployed in many places in the city, gradually forming an invisible protection network. This network can detect the trajectory of motor vehicles in real time, and identify the vehicle license plate number, body color, vehicle brand and other information. [0003] With the popularization of motor vehicles, there are more and more car-related cases. However, most of these cases, especially the car-related cases that occurred at night, made it more difficult for the public security depart...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G08G1/017
CPCG08G1/017
Inventor 廖世奇李宗杰张恩亮刘荣星
Owner 千方捷通科技股份有限公司
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