User environment classification method based on low sampling rate sensing information

Abstract

The invention provides a user environment classification method based on low sampling rate sensing information; the method uses the following units: a data acquisition module, a data processing module, a position of interest (POI) detection module and a POI classification module; the method comprises the following steps: using a low sampling rate to collect intelligent mobile phone data and updating the data to a database, carrying out denoising, time synchronization and data alignment for the data, and using a verification algorithm based on stay points to extract the POIs in the data; using a cluster algorithm with noises and based on density to cluster similar POIs; carrying out POI analysis and classification based on information fusion environment classification framework. The method can effectively collect real world information, and can use the low sampling rate model effectiveness to express high noise time domain reality data according to the existing reference cluster method, thus solving the limited battery electric quantity disadvantages in intelligent mobile phone high sampling rate data acquisition, and improving the POI environment type distinguishing accuracy.

Description

technical field [0001] The invention relates to the analysis of sensing information interest points, in particular to a user environment classification method based on low sampling rate sensing information. Background technique [0002] With the emergence and update of various smart phones and similar smart mobile devices, batch after batch of numerous and ubiquitous sensing devices have been put into daily life in large quantities, and they generate various massive sensing information, prompting people to Analyze big data. Based on this type of information, people conduct research on user points of interest (POI, Position of Interest), traffic patterns, mobility, etc., thereby forming a proprietary model for specific users, which can better understand user lifestyles and in turn improve user experience. life experience. The research community, including social scientists, is showing increasing interest in this type of research because it affects people's lives in many way...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problems of high sampling rate limitation on the time and data volume of smart mobile devices, as well as the low accuracy of user point of interest detection and environmental type classification, the purpose of the present invention is to provide an ultra-low sampling rate data collection framework , a user environment classification method based on low sampling rate sensor information and information fusion is proposed

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