A distributed implementation method for analyzing the law of user wifi activity trajectories

Abstract

The present invention proposes a distributed implementation method for analyzing the law of the user's WiFi activity track. According to the activity track of the MAC, the resident area of ​​​​the MAC can be obtained, and the time of the activity track can be analyzed to predict which time period the target object is likely to be. In which areas it appears, you can also query the set of MACs that have similar activity trajectories to the MAC. It can semanticize the excavated trajectories to find out some commuting patterns of users, such as commuting routes; it can semanticize the excavated areas of interest to find out the user's work area and residential area.

Description

technical field [0001] The invention relates to the field of information technology, in particular to a distributed implementation method for analyzing the law of user WiFi activity tracks. Background technique [0002] In the prior art, when a user holds a WiFi-enabled smart terminal and passes through the WiFi coverage area, the WiFi collection device will collect the WiFi data of the user terminal, mainly including MAC address (Media Access Control, physical address), scanning time and scanning device latitude and longitude. Therefore, the data in the general WiFi scanning list is mainly composed of scanning time, scanned AP (Access Point, wireless network access point) or MAC address, signal strength, and corresponding SSID (ServiceSet Identifier, service set identifier). It does not include the coordinate information of the displayed trajectory. [0003] In addition, on the one hand, WiFi data cannot accurately record the user's actual geographic coordinates like GPS;...

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Problems solved by technology

[0003] In addition, on the one hand, WiFi data cannot accurately record the user's actual geographical coordinates like GPS; on the other hand, the location points recorded by WiFi data are relatively sparse, and cannot have continuous location points like GPS

Therefore, the traditional method of processing GPS trajectory data cannot be completely copied to WiFi trajectory data

[0004] Generally, most of the WiFi connected by the user is sent by a fixed AP, and the coordinates of the corresponding WiFi can be obtained through the deployment position of the AP. At the same time, most of the existing mining algorithms focus on the spatial law of trajectories, and seldom consider the user's activity rules in different time scales (weeks, time periods) from time and space. Semantics of regions have important reference value

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