Hadoop-based recognition method for fake-licensed car

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...

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 affected by factors such as computing power and storage capacity when processing massive traffic flow data, showing many shortcomings such as insufficient storage space, poor stability, and long time consumption, and cannot effectively identify licensed vehicles

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