Mobile suspected risk crowd classification method based on K-means clusters

A technology of k-means clustering and classification method, which can be applied to services, instruments, characters, and pattern recognition based on location information, and can solve problems such as cumbersome implementation of risk investigation.

Active Publication Date: 2016-11-23
ZHEJIANG UNIV OF TECH
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  • Abstract
  • Description
  • Claims
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Problems solved by technology

[0005] The technical problems mainly solved by the present invention are: aiming at the cumbersome implementation of individual risk screening, using mobile data to realize the approximate positioning of individuals, analyzing the difference in characteristics among different groups of people, and combining with the idea of ​​K-means clustering based on characteristics, a method based on K-means clustering method for mobile suspected risk population classification

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  • Mobile suspected risk crowd classification method based on K-means clusters
  • Mobile suspected risk crowd classification method based on K-means clusters
  • Mobile suspected risk crowd classification method based on K-means clusters

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Embodiment Construction

[0039] In order to enable those skilled in the art to better understand the solution of the present invention, the present invention will be further described below in conjunction with the accompanying drawings. Apparently, the described embodiments are only a part of the present invention, not all of them. Based on the implementation manners in the present invention, all other implementation manners obtained by persons of ordinary skill in the art without innovative efforts shall fall within the protection scope of the present invention.

[0040] Figure 8 An embodiment of the K-means clustering-based mobile suspected risk group classification implemented by the method in the present invention. This embodiment collects about 700 million pieces of mobile data on February 5, 2015. Use MongoDB database to realize big data analysis and processing, including mobile data import module, passed individual list generation module, individual time series information generation module a...

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Abstract

The invention discloses a mobile suspected risk crowd classification method based on K-means clusters. The method comprises the steps that mobile data is acquired, and mobile information including IMSI, StartTime, CGI, LocLng, LocLat and the like is reserved; an assigned place is selected, and a list of individual IMSIs passing by the place within a specific time period is obtained according to CGI inquiry; a number of pieces of mobile information within the time period are obtained by inquiring into each individual IMSI, and a group of time sequence points are generated according to the time sequence; time sequence point information is analyzed, and the number of times of passing by the place and longest staying time of the individuals are selected and obtained to serve as classification features; the obtained features are used as K-means feature clusters for dividing a permanent crowd, a passing-by crowd and a back-and-forth staying crowd; the back-and-forth staying crowd, namely the suspected risk crowd is output in the form of the IMSI list.

Description

technical field [0001] The invention relates to the field of mobile data cluster analysis, in particular to a method for classifying mobile suspected risk groups based on K-means clustering under a mobile big data platform. Background technique [0002] In recent years, with the rapid development of mobile communications, the ownership rate of mobile devices among the population is increasing, and the accuracy of base station signal reception is also becoming more and more accurate. Through the base station receiving letters, a large amount of mobile data can be obtained every day. The geographical location of the receiving base station can roughly calibrate the location of the mobile device at the time of receiving the letter, and then calibrate the range of activities of the mobile device holder. [0003] Due to the obvious characteristics of human activities, for a certain designated place, through the analysis of the time an individual stays at the place and the number o...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62H04W4/02
CPCH04W4/02G06F18/2413G06F18/24G06F18/2415
Inventor 王卫红杨洁陈小柱
Owner ZHEJIANG UNIV OF TECH
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