User behavior identification equipment and method

Abstract

The invention provides user behavior identification equipment, which comprises a position data receiving unit, a data pre-processing unit, a characteristic vector extraction unit and a user behavior identification unit, wherein the position data receiving unit receives user position data and sorts the user position data in a time sequence to obtain time-sequence-based user position data; the data pre-processing unit pre-processes the time-sequence-based user position data; the characteristic vector extraction unit extracts characteristic vectors which are used for identifying the activity type of users according to the pre-processed user position data; and the user behavior identification unit identifies the activity type of the users according to the characteristic vectors which are extracted by the characteristic vector extraction unit to obtain the behavior characteristics of the users. The invention also provides a user behavior identification method. By the invention, the deep behavior characteristics of the users can be obtained, so a behavior identification result of each user is accurate and rich.

Description

technical field [0001] The invention relates to the field of data analysis, in particular to a user behavior recognition device and method based on location information. Background technique [0002] With the rapid development and popularization of positioning technology, whether it is the global satellite positioning system or the mobile phone positioning technology based on the wireless cellular network, people can more efficiently recognize the surrounding geographical environment. These location information can not only be used for positioning, navigation and providing some location-based services, but also can be used to express the historical behavior of users in geographic space. For example, connecting a user's isolated location points into a route in chronological order can express the user's past historical trajectory. The accumulation of multiple historical trajectories can be used to reflect the user's life rules and behavior characteristics. Furthermore, from ...

AI Technical Summary

Problems solved by technology

[0020] However, this method simply performs simple classification and statistics on the hashed user location data according to the distribution, and the user statistical results based on geographical distribution cannot represent the real behavior of users, so the results cannot provide enough information for the region. Recommend user points of interest

Through this classification and statistical method, the real intention and behavior of users cannot be accurately expressed, and there is great uncertainty

Also, analysis in this shallow sense cannot provide enough information for other users, nor can it provide good advice for urban planning

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