CN105740347A - GPS based user information acquisition and behavior analysis method - Google Patents
GPS based user information acquisition and behavior analysis method Download PDFInfo
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- CN105740347A CN105740347A CN201610049218.5A CN201610049218A CN105740347A CN 105740347 A CN105740347 A CN 105740347A CN 201610049218 A CN201610049218 A CN 201610049218A CN 105740347 A CN105740347 A CN 105740347A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
Abstract
The present invention relates to GPS position service technologies. The present invention provides a GPS based user information acquisition and behavior analysis method. The method comprises the steps that firstly, a system calls a corresponding interface of a third-party map API interface document and sets an interface parameter, and acquires position information of a user and a duration when the user stays in a position, and stores the position information and the duration into a database; secondly, the system performs clustering analysis on the information stored in the database, and if the user stays at any position for more than a preset number of times or longer than a preset duration, the position is marked as a frequently-visited position of the user; and finally, the system pushes service information near the frequently-visited position to the user. According to the method provided by the present invention, daily movement track information of the user is acquired and analyzed based on the GPS function, and according to the acquired user behavior track, the behavior rule and frequently used service of the user are mined out, so that the services are more effectively recommended to the user, and a push force is applied to performing related recommendation on later-developed related applications that are embedded into the function. The GPS based user information acquisition and behavior analysis method is applicable.
Description
Technical field
The present invention relates to GPS location service technology, particularly to user profile collection based on GPS and behavior analysis method.
Background technology
In recent years, along with the development of space data collection technology, location Based service causes the extensive concern of industry, and
Present considerable commercial promise, bring huge help for education, medical treatment, first aid etc. simultaneously.
Nowadays the intelligent terminal of the overwhelming majority can be with GPS function, and application based on location-based service can utilize intelligent terminal's
GPS function collects the positional information of user, especially, for given user, by its position on one group of continuous time point
Put after " series connection " get up, be formed for his the action trail data within this time period.At a large number of users position and action trail
The behind of data, conceals abundant spatial structural form and user behavior rule information, by carrying out deep to these information
Excavate and utilize, the daily behavior rule of individual consumer and the Common behavior feature of group of subscribers can not only be found, it is also possible to grasping
Its social networks information, this has very important meaning for induced service, application recommendation, friend recommendation, safety monitoring.
Behavior characteristics and the demand analysis of user can be obtained further, such as Behavior preference, the consuming capacity by individual's trajectory analysis
Deng, also can be analyzed colony's track excavating by the method utilizing data mining, such as city hot spot region information and colony
Behavior characteristics.It addition, introduce text-processing correlation technique, thus realize dividing of city based on user behavior track actual functional capability
District identifies.
Summary of the invention
The technical problem to be solved, it is simply that provide a kind of user profile collection based on GPS and behavior analysis method with
Realize effective according to user behavior track to user's Push Service.
The present invention solves described technical problem, employed technical scheme comprise that, user profile collection based on GPS and behavior analysis side
Method, comprises the following steps:
Step 1, system are called the corresponding interface of third party's map api interface document and arrange interface parameters, gather the position of user
Confidence breath and user present position stay time, and store to data base；
Step 2, system carry out cluster analysis to database stores information, if user stops preset times and duration at an arbitrary position
Above, it is calculated as user and often goes to position；
Step 3, system push the information on services often gone near position to user.
Concrete, in described step 2, system uses, and database stores information is gathered by meanshift clustering method
Alanysis.
Concrete, in described step 3, information on services at least includes neighbouring hotel positional information, neighbouring food and drink positional information, attached
Nearly park information, neighbouring campus information, neighbouring bank information and transport information.
Concrete, in described step 3, the kind of system Push Service information is by user's sets itself.
Concrete, in described step 2, location place type is often gone in system detection, and according to location type near user pushes
Similar type Locale information, at least includes similar site's positional information, consumption information and transport information.
The invention has the beneficial effects as follows: the present invention passes through based on movement track daily to the user information gathering of GPS function and analysis,
According to collecting user behavior track, excavate user behavior rule and conventional service, thus more effectively take to user
Business is recommended, and the related application for developing in the future embeds this function and carries out associated recommendation and play the effect of promotion.
Accompanying drawing explanation
Fig. 1 is present invention user profile based on GPS collection and method flow diagram in behavior analysis method embodiment.
Below in conjunction with the detailed description of the invention of embodiment, the foregoing of the present invention is described in further detail again.But should not
This is interpreted as, and the scope of the above-mentioned theme of the present invention is only limitted to Examples below.Without departing from the idea case in the present invention described above,
The various replacements made according to ordinary skill knowledge and customary means or change, all should be included within the scope of the invention.
Detailed description of the invention
Below in conjunction with the accompanying drawings and embodiment describe in detail technical scheme:
The problem that the present invention is directed to the most effectively carry out service recommendation based on user behavior track data in prior art, it is provided that
A kind of user profile collection based on GPS and behavior analysis method, first, system calls third party's map api interface document
Corresponding interface also arranges interface parameters, gathers positional information and the user present position stay time of user, and stores to data base；
Secondly, system carries out cluster analysis to database stores information, if user stops more than preset times and duration at an arbitrary position,
It is calculated as user and often goes to position；Finally, system pushes the information on services often gone near position to user.The present invention is by based on GPS
Function movement track daily to user information gathering and analysis, according to collecting user behavior track, excavate user behavior rule
And commonly use service, thus more effectively carry out service recommendation to user, the related application for developing in the future embeds this function and carries out
Associated recommendation plays the effect of promotion.
Embodiment
The user profile collection of the intelligent terminal based on GPS function that this example proposes and behavior analysis method, for user behavior rail
Mark is analyzed, and is typically summarized as " data acquisition-cluster-analytical behavior-recommendation service ", concrete as it is shown in figure 1, step includes:
First, this method determines the method using Android application as enforcement；
Secondly, determine that application development process, concrete enforcement include:
(1), the system architecture of design application；
(2), the building of Android development environment, including installing JDK, configure JAVA environment variable, Android SDK be installed
And Eclipse, Eclipse configures ADT, Eclipse configures DK；
(3) embed map API, call relevant interface and obtain customer position information and be stored in data base；
(4) create and configure simulator AVD, run AVD and test.
Installing above-mentioned application in this example on intelligent terminal, during use, calling third party's map can be Baidu's map or high moral ground
Figure or the corresponding interface of api interface document of other maps APP also arrange interface parameters, gather positional information and the user of user
Present position stay time, and store to data base；Then use meanshift cluster that database stores information is clustered
Analyze, generate its daily resident area, and binding time feature provides the semantic interpretation of result, such as: user is the most resident
Time the longest place is working space, and the longest place of residence time in evening is family.Spatial information is utilized to assist result solution
Read: the daily dwell point of user first cluster obtained and the point of interest (such as park, school, bank, hotel etc.) of its periphery
It is associated, and then according to the trip track of user, these dwell point " is connected ", as " gone out under school's unit
Class " etc., with the semantic deciphering of this track that realizes user is gone on a journey.
User can arrange some conventional positions, such as, company, family etc. by application.How to distinguish user present position
Whether be often to go to address, can by user by application sets itself dwell times and duration, if dwell times and/or the time of staying
More than preset times and/or preset duration, then it is assumed that this position of user is that user often goes to position.System pushes to user and often goes to position
Neighbouring information on services.Information on services at least include neighbouring hotel positional information, neighbouring food and drink positional information, neighbouring park information,
Neighbouring campus information, neighbouring bank information and transport information.Some information on services is that user is unwanted, then system in this example
The kind of Push Service information is by user's sets itself.The pushed information type that user can need from row filter.As in company, use
Family generally has only to system and carries out food and beverage sevice or carryout service propelling movement, then now, and user selects the clothes that can only select the type
Business pushed information.
System is obtained by map APP and often removes the location type of position, e.g. bar, western-style restaurant, Chinese Restaurant, bakery etc.,
As a example by bar, if system detects when user often goes the location type of position to be bar, then push to user and often go near bar
Other bars, and provide the positional information in other bars, consumption information and transport information.
It addition, in nowadays life, share-car has been the mode of transportation that most of working clan selects, can how to select suitably
Partner carries out share-car？The user profile collection of the intelligent terminal based on GPS function that this example provides is effective with behavior analysis method
Solve this problem.According to above-mentioned user profile collection and behavior analysis method, system can effectively learn that user is from family
To the movement track data of unit, when system detects that around other users go to work what track was consistent with user, then carried out as user
Friend recommendation, informs that user has suitable share-car partner.Effectively solve the problem that user finds share-car partner difficulty.
In sum, by the intelligent terminal with GPS function, application can be embedded map API and obtain customer position information,
Meanwhile, use clustering method can obtain daily behavior rule and individual's preference of user, location Based service is had
Huge impetus.
Claims (5)
1. user profile collection based on GPS and behavior analysis method, it is characterised in that comprise the following steps:
Step 1, system are called the corresponding interface of third party's map api interface document and arrange interface parameters, gather the position of user
Confidence breath and user present position stay time, and store to data base；
Step 2, system carry out cluster analysis to database stores information, if user stops preset times and duration at an arbitrary position
Above, it is calculated as user and often goes to position；
Step 3, system push the information on services often gone near position to user.
User profile collection based on GPS the most according to claim 1 and behavior analysis method, it is characterised in that described
In step 2, system uses, and meanshift clustering method carries out cluster analysis to database stores information.
User profile collection based on GPS the most according to claim 1 and behavior analysis method, it is characterised in that described
In step 3, information on services at least include neighbouring hotel positional information, neighbouring food and drink positional information, neighbouring park information, near
Campus information, neighbouring bank information and transport information.
User profile collection based on GPS the most according to claim 3 and behavior analysis method, it is characterised in that described
In step 3, the kind of system Push Service information is by user's sets itself.
User profile collection based on GPS the most according to claim 1 and behavior analysis method, it is characterised in that described
In step 2, location place type is often gone in system detection, and pushes neighbouring similar type Locale information according to location type to user,
At least include similar site's positional information, consumption information and transport information.
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Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106570722A (en) * | 2016-10-31 | 2017-04-19 | 上海斐讯数据通信技术有限公司 | Intelligent recommendation system and intelligent recommendation method |
| CN106934696A (en) * | 2017-03-15 | 2017-07-07 | 深圳大学 | A kind of Products Show method and system based on user's geographical location information |
| CN107239796A (en) * | 2017-05-19 | 2017-10-10 | 四川长虹电器股份有限公司 | The System and method for that TV belongs to attribute is distinguished based on usage behavior |
| CN107277124A (en) * | 2017-06-12 | 2017-10-20 | 北京望远传媒有限公司 | A kind of method and system to user's pushed information |
| CN107657007A (en) * | 2017-09-22 | 2018-02-02 | 广东欧珀移动通信有限公司 | Information-pushing method, device, terminal, readable storage medium storing program for executing and system |
| CN107820214A (en) * | 2017-09-28 | 2018-03-20 | 长安大学 | A kind of user trajectory analysis system based on time suboptimal control |
| CN108921403A (en) * | 2018-06-15 | 2018-11-30 | 杭州后博科技有限公司 | It is ridden when a kind of shared bicycle is without usage record recognition methods and system |
| CN109214055A (en) * | 2018-08-06 | 2019-01-15 | 中国南方电网有限责任公司 | A kind of PSD-BPA card parameter check method based on formula driving |
| CN109511085A (en) * | 2018-10-29 | 2019-03-22 | 中国矿业大学 | A kind of UWB fingerprint positioning method based on MeanShift and weighting k nearest neighbor algorithm |
| CN110516017A (en) * | 2019-08-02 | 2019-11-29 | Oppo广东移动通信有限公司 | Location information processing method, device, electronic equipment and storage medium based on terminal device |
| CN111078818A (en) * | 2019-12-27 | 2020-04-28 | 同盾(广州)科技有限公司 | Address analysis method and device, electronic equipment and storage medium |
| CN111242723A (en) * | 2020-01-02 | 2020-06-05 | 平安科技（深圳）有限公司 | User child and child condition judgment method, server and computer readable storage medium |
| CN111667127A (en) * | 2019-03-05 | 2020-09-15 | 杭州海康威视系统技术有限公司 | Intelligent supervision method and device and electronic equipment |
| CN112131481A (en) * | 2020-10-03 | 2020-12-25 | 北京一点网聚科技有限公司 | Recommendation method and device |
| CN112232845A (en) * | 2019-07-15 | 2021-01-15 | 中国移动通信集团重庆有限公司 | Method and device for predicting user behavior preference based on user position |
| CN114333881A (en) * | 2022-03-09 | 2022-04-12 | 深圳市迪斯声学有限公司 | Audio transmission noise reduction method, device, equipment and medium based on environment self-adaptation |
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Cited By (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106570722A (en) * | 2016-10-31 | 2017-04-19 | 上海斐讯数据通信技术有限公司 | Intelligent recommendation system and intelligent recommendation method |
| CN106934696A (en) * | 2017-03-15 | 2017-07-07 | 深圳大学 | A kind of Products Show method and system based on user's geographical location information |
| CN107239796A (en) * | 2017-05-19 | 2017-10-10 | 四川长虹电器股份有限公司 | The System and method for that TV belongs to attribute is distinguished based on usage behavior |
| CN107239796B (en) * | 2017-05-19 | 2020-06-30 | 四川长虹电器股份有限公司 | System and method for distinguishing television attribution attributes based on using behaviors |
| CN107277124A (en) * | 2017-06-12 | 2017-10-20 | 北京望远传媒有限公司 | A kind of method and system to user's pushed information |
| CN107657007A (en) * | 2017-09-22 | 2018-02-02 | 广东欧珀移动通信有限公司 | Information-pushing method, device, terminal, readable storage medium storing program for executing and system |
| CN107820214A (en) * | 2017-09-28 | 2018-03-20 | 长安大学 | A kind of user trajectory analysis system based on time suboptimal control |
| CN108921403A (en) * | 2018-06-15 | 2018-11-30 | 杭州后博科技有限公司 | It is ridden when a kind of shared bicycle is without usage record recognition methods and system |
| CN109214055A (en) * | 2018-08-06 | 2019-01-15 | 中国南方电网有限责任公司 | A kind of PSD-BPA card parameter check method based on formula driving |
| CN109214055B (en) * | 2018-08-06 | 2022-12-02 | 中国南方电网有限责任公司 | Formula-driven PSD-BPA card parameter checking method |
| CN109511085A (en) * | 2018-10-29 | 2019-03-22 | 中国矿业大学 | A kind of UWB fingerprint positioning method based on MeanShift and weighting k nearest neighbor algorithm |
| CN109511085B (en) * | 2018-10-29 | 2020-09-22 | 中国矿业大学 | UWB fingerprint positioning method based on MeanShift and weighted k nearest neighbor algorithm |
| CN111667127A (en) * | 2019-03-05 | 2020-09-15 | 杭州海康威视系统技术有限公司 | Intelligent supervision method and device and electronic equipment |
| CN111667127B (en) * | 2019-03-05 | 2023-04-18 | 杭州海康威视系统技术有限公司 | Intelligent supervision method and device and electronic equipment |
| CN112232845B (en) * | 2019-07-15 | 2023-12-19 | 中国移动通信集团重庆有限公司 | Method and device for predicting user behavior preference based on user position |
| CN112232845A (en) * | 2019-07-15 | 2021-01-15 | 中国移动通信集团重庆有限公司 | Method and device for predicting user behavior preference based on user position |
| CN110516017A (en) * | 2019-08-02 | 2019-11-29 | Oppo广东移动通信有限公司 | Location information processing method, device, electronic equipment and storage medium based on terminal device |
| CN110516017B (en) * | 2019-08-02 | 2022-05-20 | Oppo广东移动通信有限公司 | Location information processing method and device based on terminal equipment, electronic equipment and storage medium |
| CN111078818A (en) * | 2019-12-27 | 2020-04-28 | 同盾(广州)科技有限公司 | Address analysis method and device, electronic equipment and storage medium |
| CN111242723A (en) * | 2020-01-02 | 2020-06-05 | 平安科技（深圳）有限公司 | User child and child condition judgment method, server and computer readable storage medium |
| CN112131481A (en) * | 2020-10-03 | 2020-12-25 | 北京一点网聚科技有限公司 | Recommendation method and device |
| CN114333881A (en) * | 2022-03-09 | 2022-04-12 | 深圳市迪斯声学有限公司 | Audio transmission noise reduction method, device, equipment and medium based on environment self-adaptation |
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