Trajectory law analysis method for realizing information visualization

Abstract

The invention discloses a trajectory law analysis method for realizing information visualization. The method comprises the following steps of S1, obtaining a trajectory; S2, dividing all tracks into ntrack clusters; S3, judging whether each track cluster meets a condition or not and updating a cluster center; S4, dividing the cluster centers of the n trajectory clusters into m cluster centers; S5, gathering m cluster centers, and splitting all tracks into m track clusters; s6, calculating the track number p of each track cluster; s7, sorting all the tracks according to time, and counting theskipping times q of each cluster track; and S8, outputting the obtained information and carrying out visualization processing. According to the method, the behavior rule characteristics implied by themassive tracks are displayed on the map by using limited circles and straight lines, so that intuitive and visual display of the behavior rule characteristics implied by the massive tracks is realized, the security analysis efficiency is improved, and the work of security workers is facilitated.

Description

technical field [0001] The invention relates to the fields of security and information technology, in particular to a method for analyzing trajectory rules for realizing information visualization. Background technique [0002] At present, a variety of monitoring devices in the security system will collect a large amount of behavior trajectory data, which includes data such as face, MAC, IMSI, and IMEI; all trajectory data belonging to the same feature imply the behavioral characteristics of this feature , such as information such as the main activity route and main activity location, this type of information plays an important role in scenarios such as personnel positioning and habit analysis. For a large amount of trajectory data given a feature, the existing technology cannot efficiently analyze the trajectory. The hidden law information also cannot realize the intuitive and visual display of the analyzed law information, causing troubles to the work of security staff, and...

AI Technical Summary

Problems solved by technology

[0002] At present, a variety of monitoring devices in the security system will collect a large amount of behavior trajectory data, which includes data such as face, MAC, IMSI, and IMEI; all trajectory data belonging to the same feature imply the behavioral characteristics of this feature , such as information such as the main activity route and main activity location, this type of information plays an important role in scenarios such as personnel positioning and habit analysis. For a large amount of trajectory data given a feature, the existing technology cannot efficiently analyze the trajectory. The hidden law information also cannot realize the intuitive and visual display of the analyzed law information, causing troubles to the work of security staff, and the efficiency of security analysis is low

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