Hadoop-based recognition method for fake-licensed car

A recognition method and a technology for decked vehicles, which are applied in road vehicle traffic control systems, traffic control systems, structured data retrieval, etc., can solve problems such as time-consuming, poor stability, and insufficient storage space, and achieve reduction in matching times, High operating efficiency and speed-up ratio, effective recognition effect

Active Publication Date: 2014-09-10
HANGZHOU DIANZI UNIV
View PDF4 Cites 22 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Traditional computer systems can no longer meet the needs
In addition, the traffic information flow has the characteristics of high dimensionality, time-space correlation, etc., which makes data analysis and processing more complicated.
The traditional method is

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Hadoop-based recognition method for fake-licensed car
  • Hadoop-based recognition method for fake-licensed car
  • Hadoop-based recognition method for fake-licensed car

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0018] The specific implementation of the Hadoop-based method for identifying license plates provided by the present invention is mainly divided into 4 steps, such as figure 1 As shown, the architecture based on Hadoop cluster is as follows figure 2 Shown:

[0019] For the convenience of description, the relevant symbols are defined as follows:

[0020] P i : The i-th (i=1, 2,...,n) thread.

[0021] O={o 0 ,...,o i ,...,o n-1 }(0≤i≤n-1): collection of passing record objects.

[0022] D={d 0 ,...,d i ,...,d m-1 }(0≤i≤m-1): monitoring point collection.

[0023] T={t 0 ,...,t i ,...,t q-1 }(0≤i≤q-1): a collection of timestamps.

[0024] H: License plate number.

[0025] T: Passing time.

[0026] Dist(d i ,d j ): Monitoring point d i And d j The shortest path between.

[0027] S ij : Appear at the same time at the location number d i And d j Set of vehicle passing records.

[0028] TD H i,j = T i -t j :The same license plate H passes through monitoring point d i And d j Time difference.

[00...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a hadoop-based recognition method for a fake-licensed car. The hadoop-based recognition method for the fake-licensed car is characterized in that the input is massive process records. The method comprises the following steps: transferring valid passing car records subjected to dimension reduction into HBase of a Hadoop cluster; acquiring the passing record of a car that has the same license and appears in any two monitoring points from HBase through Hive; grouping and sequencing according to the license number and passing time; initializing a weighted graph that adopts the monitoring points as a vertex set, and the space between every two monitoring points as the edge weight value; calculating the shortest path between every two monitoring points; combining every two monitoring points and processing by block; creating a plurality of threads; concurrently submitting Hive tasks to recognize the fake-licensed car under the principle of the fake-licensed car and according to the combination of every two monitoring points subjected to block processing; acquiring the final suspectable fake-licensed car through correction factors. Compared with the non-optimization method under the traditional environment, the hadoop-based recognition method has the advantages that the running efficiency and speed-up ratio are raised, and the fake-licensed car can be effectively recognized.

Description

technical field [0001] The invention belongs to the technical field of massive spatio-temporal data mining, and in particular relates to a Hadoop-based identification method for decked vehicles. Background technique [0002] Fake license plates refer to vehicles that use license plate numbers of other vehicles to evade punishment. Because the fake license plates can avoid serious consequences and pay fees, which pose a great threat to people's lives and property and public safety, they have always been the focus of the traffic supervision department and are strictly prohibited by the state. Therefore, actively discovering and identifying fake cars in massive traffic data is not only of great significance to the public security traffic police's active warning and post-event investigation, but also to protect the interests of real car owners and victims of fake car accidents. [0003] However, with the rapid growth of traffic information flow, its data volume has reached TB l...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F17/30G08G1/017
CPCG06F16/2458G06F16/284G08G1/017
Inventor 俞东进平利强李万清邹绍芳窦文生方炜
Owner HANGZHOU DIANZI UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap