Large-scale vehicle-passing data oriented accompanying vehicle identification method

Abstract

The invention relates to the technical field of data mining, and discloses a large-scale vehicle-passing data oriented accompanying vehicle identification method. The large-scale vehicle-passing data oriented accompanying vehicle identification method comprises the steps of converting the vehicle-passing data into accompanying data and uploading the accompanying data to HDFS; and mining the accompanying data by using a Spark based matrix pruning parallel frequent item set mining algorithm to obtain an accompanying vehicle set. Compared with the prior art, the large-scale vehicle-passing data oriented accompanying vehicle identification method improves the identification efficiency of an accompanying vehicle and can reduce the identification cost of the accompanying vehicle.

Description

technical field [0001] The invention relates to the technical field of data mining, in particular to an identification method for accompanying vehicles facing large-scale passing data. Background technique [0002] Accompanying vehicle identification is a business requirement of the public security department for the retrieval of massive passing vehicle data information in the bayonet system. Its main purpose is to find out the combination of vehicles that may commit crimes together in the traffic road network. Generally speaking, the accompanying vehicle mainly refers to, in the case of a pre-specified license plate number, the time difference between the vehicle passing through the same checkpoint and the vehicle with the license plate number is found to be less than a certain time threshold and the number of occurrences is at least greater than the pre-specified number of times. other vehicles with a certain threshold number of times; or in the case of no pre-given licens...

AI Technical Summary

Problems solved by technology

Generally speaking, for the bayonet database system with billions of passing vehicle data, limited by the computing power of the bayonet database system, traditional database technology has been unable to support the accompanying vehicle identification business with large data volume and complex calculations

Although the computing power can be temporarily improved to meet business needs through system hardware upgrades, the cost of system upgrades is often very expensive, and due to the rapid growth of the data volume of checkpoint passing vehicles, the overall system hardware needs to be upgraded every 1-3 years on average. Once, it can be seen that the cost performance of meeting the accompanying vehicle business needs in this way is extremely low

Images