CN110443637A - User's Shopping Behaviors analysis method, device and storage medium - Google Patents
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Abstract
This application discloses a kind of user's Shopping Behaviors analysis method, device and storage mediums.The method: user's Shopping Behaviors analytical equipment receives the behavior monitoring data of the user；The behavior monitoring data are analyzed, the first Shopping Behaviors data of the user are obtained；Receive the second Shopping Behaviors data for the user that user terminal is sent；The first Shopping Behaviors data and the second Shopping Behaviors data are analyzed, user's portrait is established and/or generate warning information.Including by the above-mentioned means, the application can be realized the on-line off-line data interchange of user's Shopping Behaviors, carry out user's Shopping Behaviors analysis that can be more accurate.
Description
Technical field
This application involves behavioural analysis fields, do shopping and go more particularly to a kind of user's Shopping Behaviors analysis method, user
For analytical equipment and storage medium.
Background technique
With the development of internet technology, " new retail " mode settled accounts on shopping line under line is suggested, and unmanned supermarket is
It is one of.In recent years various regions unmanned supermarket, more families opens for business successively, and customer verifies identity using mobile terminal barcode scanning into shop, has bought
The mode self-help settlement for using Alipay and wechat to pay the bill after commodity can complete transaction without waiter and cashier,
It is more convenient to do shopping.
Summary of the invention
The application is mainly solving the technical problems that provide a kind of user's Shopping Behaviors analysis method, user's Shopping Behaviors point
Analysis apparatus and storage medium can be realized the on-line off-line data interchange of user's Shopping Behaviors, precisely analyze user's Shopping Behaviors.
In order to solve the above technical problems, the technical solution that the application uses is: providing a kind of user's Shopping Behaviors point
Analysis method, this method comprises: receiving the behavior monitoring data of the user；The behavior monitoring data are analyzed, the use is obtained
The first Shopping Behaviors data at family；Receive the second Shopping Behaviors data for the user that user terminal is sent；Analyze described
One Shopping Behaviors data and the second Shopping Behaviors data establish user's portrait and/or generate warning information.
In order to solve the above technical problems, another technical solution that the application uses is: providing a kind of user's Shopping Behaviors
Analytical equipment, including processor, memory and telecommunication circuit, the processor couple the memory, telecommunication circuit, are working
When execute instruction, to cooperate the memory, telecommunication circuit to realize above-mentioned user's Shopping Behaviors analysis method.
In order to solve the above technical problems, another technical solution that the application uses is: a kind of storage medium is provided, wherein
It is stored with computer program, computer program, which is performed, realizes above-mentioned user's Shopping Behaviors analysis method.
The beneficial effect of the application is: analyzing the behavior monitoring data to user, obtains the first Shopping Behaviors number of user
According to indicating Shopping Behaviors under the line of user；The the second Shopping Behaviors data for receiving the user that user terminal is sent, indicate user's
Online shopping behavior；Comprehensive analysis the first, second Shopping Behaviors data, to accurately analyze the Shopping Behaviors of user.
Detailed description of the invention
Fig. 1 is the first embodiment of user Shopping Behaviors analysis method of the application based on user's Shopping Behaviors analytical equipment
Flow diagram；
Fig. 2 is the second embodiment of user Shopping Behaviors analysis method of the application based on user's Shopping Behaviors analytical equipment
Flow diagram；
Fig. 3 is the 3rd embodiment of user Shopping Behaviors analysis method of the application based on user's Shopping Behaviors analytical equipment
Flow diagram；
Fig. 4 is the structural schematic diagram of an embodiment of the application user's Shopping Behaviors analysis system；
Fig. 5 is an interaction schematic diagram of the application user's Shopping Behaviors analysis method；
Fig. 6 is the structural schematic diagram of one embodiment of the application user's Shopping Behaviors analytical equipment.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present application, technical solutions in the embodiments of the present application carries out clear, complete
Site preparation description, it is clear that described embodiment is only a part of the embodiment of the application, instead of all the embodiments.It is based on
Embodiment in the application, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall in the protection scope of this application.
Referenced herein " embodiment " is it is meant that a particular feature, structure, or characteristic described can wrap in conjunction with the embodiments
It is contained at least one embodiment of the application.Each position in the description occur the phrase might not each mean it is identical
Embodiment, nor the independent or alternative embodiment with other embodiments mutual exclusion.Those skilled in the art explicitly and
Implicitly understand, embodiment described herein can be combined with other embodiments.
In addition, it should be noted that, term " first ", " second ", " third " in the embodiment of the present application are only used for describing
Purpose is not understood to indicate or imply relative importance or implicitly indicates the quantity of indicated technical characteristic.As a result,
Define " first ", " second ", " third " feature can explicitly or implicitly include at least one of the features.In addition, this
Apply for the term " includes " and " having " and their any deformations in embodiment, it is intended that cover and non-exclusive include.
The retail mode of customer self-help shopping and clearing in market, is received and is promoted by various shopping places, such as
In recent years there is " unmanned supermarket " this new retail mode.Customer is not necessarily to settle accounts in cashier, and shopping can be completed.
Present inventor does not play data under its line effectively and adopts by the discovery that studies for a long period of time, current unmanned supermarket
Due value is pushed on collection and line.In order to solve the above problem, the application provides following examples.
Referring to Fig. 1, user Shopping Behaviors analysis method first of the application based on user's Shopping Behaviors analytical equipment is real
Example is applied, is included the following steps:
S101: the behavior monitoring data of user are received.
User's Shopping Behaviors analytical equipment receives the behavior monitoring data from the collected user of photographic device, with subsequent
Analyze user's Shopping Behaviors in behavior monitoring data.
Specifically, shopping place is provided at least one photographic device, for acquiring the prison of the user action behavior in market
User's Shopping Behaviors analytical equipment is sent to after control data.
Wherein, it is such as that WIFI is connect that user's Shopping Behaviors analytical equipment, which can be with photographic device through wireless network,
It is also possible to connect by cable, can also be and connected by transmission cable etc., specific connection type is herein with no restrictions.
S102: analysis behavior monitoring data obtain the first Shopping Behaviors data of user.
Received behavior monitoring data are the number that photographic device is monitored the user in shopping place and obtains
According to can be analyzed according to the data and know user in the Shopping Behaviors of supermarket, then obtain the first Shopping Behaviors data of the user.
Wherein, the first Shopping Behaviors data of user include user in the arrival region of shopping place, shelf and stop
It is long, it checks merchandise news quantity and time, duration of doing shopping, the merchandise news taken out, put down from shelf is based on identification from shelf
The first shopping list that the upper merchandise news taken out, put down is formed.
Wherein, analysis behavior monitoring data can be the movement using user in algorithm identification monitoring data, Activity recognition
Algorithm for example can be iDT algorithm, deep learning algorithm etc., and deep learning algorithm can automatically extract feature, avoid well
Blindness and otherness during artificial design features.One kind of deep learning algorithm --- convolutional neural networks algorithm,
By the convolution operation to input data, feature is successively extracted, to carry out identification classification to image, monitoring can be accurately identified
The human action of data.According to the movement of the user in analysis behavior monitoring data and user's stay time is calculated, can be obtained
Above-mentioned first Shopping Behaviors data.
S103: the second Shopping Behaviors data for the user that user terminal is sent are received.
User's Shopping Behaviors analytical equipment real-time reception the second Shopping Behaviors data, the second Shopping Behaviors data are to pass through use
What family terminal obtained.
Wherein, the second Shopping Behaviors data include user terminal addition/deletion commodity information and user eventually by
Second shopping list of shopping terminals clearing.
Specifically, user terminal has barcode scanning function, and user obtains commodity using the bar code of user terminal items scanning
The information such as price, quantity and be added in the virtual shopping cart on user terminal, to carry out the clearing of commodity.In addition, may be used also
To carry out the delete operation of commodity in virtual shopping cart, such as to have added purchase more one or more or some for some commodity amount
Commodity are temporarily not desired to have purchased, and can delete the commodity abandoned from virtual shopping cart.
In a specific application scenarios, shopping program can be installed on user terminal, such as can be shopping APP,
Or the insertion shopping small routine in third-party application, it will intend to purchase by the barcode scanning function barcode scanning commodity bar code in shopping program
Virtual shopping cart is added in the commodity bought.
S104: the first Shopping Behaviors data of analysis and the second Shopping Behaviors data establish user's portrait and/or generate early warning
Information.
User's Shopping Behaviors analytical equipment analyzes the first Shopping Behaviors data and the second Shopping Behaviors data and right
Than establishing user's portrait based on the analysis results, generating warning information according to comparing result.
Specifically, user's Shopping Behaviors analytical equipment comprehensive analysis user adds/deletes the merchandise news and use of shopping cart
The information that family checks commodity duration in shopping place, takes out/put back to commodity can get user to certain commodity or certain product
Fancy grade, desirability and level of consumption of board etc. establish user's portrait based on the analysis results.
Meanwhile comparing the first shopping list of the user and type of merchandize, quantity and the specification in the second shopping list are
It is no consistent, judge whether user has drain junction to calculate, accidentally clearing or the case where more clearing, if any generating warning information, then to prompt work
It is checked as personnel.
By the above-mentioned means, comprehensive analysis adds the Shopping Behaviors of purchase and clearing commodity and user purchasing from user self-help
Object field in behavior expression, can get more accurately user draw a portrait.Meanwhile comparing the shopping list of user's clearing and from monitoring
User's shopping list that data identify, realizes the intercommunication of on-line off-line data, it is possible to prevente effectively from self-help shopping may go out
The case where existing drain junction is calculated, accidentally settles accounts or settle accounts more, ensures the interests of shopping place and user.
In one embodiment, it before analyzing the first Shopping Behaviors data and the second Shopping Behaviors data, needs user
The first Shopping Behaviors data and the second Shopping Behaviors data associate, that is, confirm the first Shopping Behaviors data and second shopping
Behavioral data is to belong to same user, can be identical in the first Shopping Behaviors data and the second Shopping Behaviors data by judging
Feature is matched.
For example, the time of purchase commodity can be added using user terminal scan product barcode by user, behavior the is determined
It is same people that the same commodity of scanning and time difference identified in one monitoring data, which are less than the user of threshold value, it is, of course, also possible to
Judged in conjunction with other common traits, such as brand, model of user terminal etc., to improve matched accuracy.
In another embodiment, the first Shopping Behaviors data and of the user can also be associated with by recognition of face
Two Shopping Behaviors data, e.g. user's login account of the shopping program on shopping terminals and the face characteristic information of user are tied up
It is fixed, to realize the first Shopping Behaviors data and the second Shopping Behaviors data correlation.Before S101, user arrives shopping field for the first time
When institute's self-help shopping, user bound login account and the face characteristic information of user are needed.Referring to Fig. 2, the application is based on using
The flow diagram of user's Shopping Behaviors analysis method second embodiment of family Shopping Behaviors analytical equipment, the present embodiment is with base
Based on user's Shopping Behaviors analysis method first embodiment of user's Shopping Behaviors analytical equipment.
S201: it receives user terminal and passes through the user login information for scanning the two-dimensional code and logging in shopping program, user logs in letter
Breath includes the position encoded of user's login account and two dimensional code.
User's Shopping Behaviors analytical equipment receives the position encoded user information comprising user's login account and two dimensional code,
Position when can know user's barcode scanning according to two dimensional code position encoded, can determine user's according to user's login account
Identity on line.
Specifically, shopping place is provided at least one two dimensional code in inlet, such as can be 1,2,5 etc.,
Particular number can be configured according to shopping place scale, volume of the flow of passengers etc., it is not limited here.Two can also be arranged in shopping place
Code is tieed up, the quantity of two dimensional code, specific location, size herein with no restrictions, are easy discovery with user and user are whole in shopping place
End identification is advisable, when user forgets to scan the two-dimensional code nearby in shopping place when inlet is scanned the two-dimensional code.This reality
It applies in example, inlet can not have to setting gate inhibition, and user can be scanned in inlet or shopping place according to oneself selection and step on
Record, user are freely accessible to realize the self-service purchase of opening, it can be achieved that more people while entering shopping place and doing shopping the shopping place in
Object improves the shopping experience of user.
Wherein, two dimensional code specifically carries shopping program for entering shopping program after user terminal scanning in two dimensional code
Jump address and position encoded parameter, user terminal identification two dimensional code jumps to the specified shopping of the specified H5 page or pull-up
When the shopping program is not present in program or user terminal, jumps application market and download the shopping program.
Into after shopping program, user logs in shopping program using personal information, in the present embodiment, can be user's use
Third party's account authorization logs in, and in other embodiments, is also possible to user's input/register account number password login, can also be
It fills in cell-phone number and identifying code is logged in.Wherein, it can simplify login process using third party's account authorization login, quickly obtain
User information is taken, the shopping experience of user is improved.
It is illustrated so that wechat does shopping small routine as an example, but the application is without being limited thereto.User opens micro- on user terminal
Letter, sweeps functionality scan two dimensional code using sweeping, and page jump pops up authorization wechat Account Logon to specified shopping small routine
Request frame, user clicks after allowing, the openid and interface for calling user's specifying information that wechat platform returns to user
Voucher is called, calls voucher to obtain user information from wechat platform using interface, that is, completes the login of wechat account authorization.User
Log-on message includes position encoded from information, openid, two dimensional codes such as user's head portrait, the pet name and the areas that wechat platform obtains.
User's login account can be directly use from wechat acquisition user information as login account, such as use openid as
The login account of the user, interface call voucher as login password, can also be user's Shopping Behaviors analytical equipment according to from
The user information that wechat account is got generates an own account, and binds own account and wechat account and closed with forming mapping
System.
Further, user login information further includes the sweep time scanned the two-dimensional code, it is to be understood that scanning two dimension
The time when sweep time of code specifically refers to user terminal identification two dimensional code and jumps to shopping program.
S202: the first face monitoring data with position encoded associated face identification camera device acquisition is received.
User's Shopping Behaviors analytical equipment receives the first face monitoring data of the user, the first face monitoring data be with
The monitoring data comprising user's face characteristic information of the photographic device acquisition of two-dimentional code position corresponding position.
Specifically, the corresponding position of two dimensional code is provided with face identification camera device, and face identification camera device has
Coding identical or associated with two dimensional code coding, therefore, user's Shopping Behaviors analytical equipment can will scan the two dimensional code and step on
User's login account of record has been associated with the collected first face monitoring data of the face identification camera device of corresponding position
Come.
In one embodiment, face identification camera device and obtain the photographic device of user behavior monitoring data can be with
Two kinds of photographic devices are different, face identification camera device acquires the relevant monitoring data of user's face, photographic device acquisition
The relevant monitoring data of user action behavior, the acquisition of division of labor monitoring data.
In another embodiment, face identification camera device and obtain user behavior monitoring data photographic device also
It can be with same photographic device.First face monitoring data and user behavior monitoring data can be the same photographic device acquisition
The same monitoring data, be also possible to the monitoring data in different time periods of same photographic device acquisition, can also be more
The different angle or monitoring data in different time periods of a photographic device acquisition.
In yet another embodiment, the first face monitoring data, which can also be, is obtained by the camera of user terminal.
Specifically, it after user terminal opens shopping program, obtains user and authorizes unlatching front camera, naturally it is also possible to be postposition camera shooting
Head, user can carry out the switching of front and rear camera according to actual needs, by user terminal shoot user human face photo or
After person records face video, it is sent to user's Shopping Behaviors analytical equipment, user's Shopping Behaviors analytical equipment can be directly linked
First face monitoring data and user's login account.
S203: the first face monitoring data of analysis extracts user's face characteristic information of user.
User's Shopping Behaviors analytical equipment analyzes the first face monitoring data, obtains the face in face monitoring data and mentions
Take out user's face characteristic information of user.
Specifically, according to the time of scanning input two dimensional code, it is corresponding to extract same time in the first face monitoring data
User's face characteristic information is also possible to extract the use of a period before and after the barcode scanning time in the first face monitoring data
Family face characteristic information, for example 30 divide 15 seconds when the scanning input time is 14, then when extracting 14 30 divide 14 seconds to 14 when 30 divide
16 seconds this periods user's face characteristic information, naturally it is also possible to be 2 seconds, 3 seconds before and after the barcode scanning time, it is not limited here.
Face characteristic information is extracted using face characteristic extraction algorithm, face characteristic extraction algorithm for example can be depth
Practise algorithm, eigenfaces (Eigenface), linear discriminant analysis (LDA, Linear Discriminant Analysis), office
Portion's binary pattern (LBP, Local binary patterns) etc..
Further, user's Shopping Behaviors analytical equipment analyze face characteristic in the first monitoring data it is whether clear, it is obvious,
Have unobstructed etc., if user face blocks or angle is bad can not extract user's face characteristic, generates trouble in human face recognition
Prompt be sent to the user terminal, with remind user remove shelter or adjustment face's angle, resurvey the first monitoring data.
S204: user bound face characteristic information and user's login account.
According to the incidence relation of the first face monitoring data and user's login account, binds face characteristic information and stepped on user
It records account and saves to User Information Database, so that user's Shopping Behaviors analytical equipment can be mapped according to face characteristic information
Face characteristic information is mapped to user's login account or user's login account.
By taking wechat does shopping small routine as an example, after user licenses wechat Account Logon wechat shopping small routine, obtain
The openid that wechat returns, since openid is mark of the wechat account to unique corresponding user identity of shopping small routine, and
It will not change, therefore bind openid and user's face characteristic information, that is, form face characteristic information and user's login account
Stable incidence relation will not change because the other users information such as user's pet name, head portrait in wechat account change, convenient for using
The management at family and the record of user self-help Shopping Behaviors.
In the present embodiment, user need to be only scanned the two-dimensional code when first time entering shopping place self-help shopping into shopping
Program simultaneously logs in user bound face characteristic information, subsequent when again or even repeatedly entering the shopping place, no longer needs to scanning two
Code and binding face characteristic information are tieed up, user's Shopping Behaviors analytical equipment can be directly according to user's face in face monitoring data
Characteristic information obtains user's logon account.
After user bound face characteristic information and user's logon account, user's Shopping Behaviors analytical equipment can be according to user people
Face characteristic information identifies corresponding user from face monitoring data in real time, can be realized to user in shopping place with
The acquisition of track and user's Shopping Behaviors.Referring to Fig. 4, Fig. 4 is that the application is purchased based on the user of user's Shopping Behaviors analytical equipment
The flow diagram of object behavior analysis method 3rd embodiment, the present embodiment are with the use based on user's Shopping Behaviors analytical equipment
Based on the Shopping Behaviors analysis method first embodiment to second embodiment of family, details are not described herein for identical step.
S301: user's Shopping Behaviors analytical equipment receives user terminal and passes through the user for scanning the two-dimensional code and logging in shopping program
Log-on message, user login information include the position encoded of user's login account and two dimensional code.
S302: the first face monitoring data with position encoded associated face identification camera device acquisition is received.
S303: the first face monitoring data of analysis extracts user's face characteristic information of user.
S304: user bound face characteristic information and user's login account.
S305: the second face monitoring data of at least one face identification camera device acquisition is received.
The second face prison that user's at least one face identification camera device of Shopping Behaviors analytical equipment real-time reception is sent
Data are controlled, user can be traced in the second face monitoring data in the route track of shopping place.
At least one face identification camera device is provided in shopping place, specifically, face imaging identification device can be with
Setting one, be also possible to it is multiple, such as shopping place it is smaller, only be arranged a face identification camera device can collect
In shopping place in the case where face monitoring data, a face identification camera device is set, the first face monitors at this time
Data and the second face monitoring data are same face identification camera devices.In addition, shopping place is, for example, to surpass in large-scale quotient, it can
Multiple face identification devices are set to ensure that any region in shopping place can collect face monitoring data.
Face imaging identification device acquires the second face monitoring data in purchase place in real time and is sent to user's shopping row
For analytical equipment, user's Shopping Behaviors analytical equipment receives the second face monitoring data and saves, and saves the second face prison
The term of validity of control data for example can be 1 hour or 2 hours or 24 hours etc., specifically herein with no restrictions, surpass
It spends term of validity and deletes the second face monitoring data, discharge the memory space of user's Shopping Behaviors analytical equipment.
S306: according to user's face characteristic information, the target user in the second monitoring data is identified.
User's Shopping Behaviors analytical equipment receives the second face monitoring data and saves, and is identified according to face characteristic information
The target user of second face monitoring data.
S307: according to the second face monitoring data, region and the physical characteristic information of target user are obtained.
After identifying the target user in the second face monitoring data according to user's face characteristic information, continue to obtain target
User region and physical characteristic information.
Specifically, the region of target user is obtained, specifically for example can be by where face identification camera device
Position encoded determining target user where region.After identifying target user, clothing color, the clothing of target user are extracted
The physical characteristic informations such as style, the accessories worn and user's body characteristics simultaneously save.
S308: region, physical characteristic information and the user's login account of association user.
According to face characteristic information and the binding relationship association user login account of user's login account and its area at place
Domain, physical characteristic information.
S309: the behavior monitoring data of user are received.
S310: obtaining in behavior monitoring data that region is identical and physical characteristic information similarity is greater than the mesh of threshold value
Mark the first Shopping Behaviors data of user.
Region and physical characteristic information where target user in the second face monitoring data identify behavior monitoring number
The user that same area and physical characteristic information similarity in are greater than threshold value is target user.
After determining the target user in behavior monitoring data, the first shopping row of target user in behavior monitoring data is obtained
For data.First Shopping Behaviors data may include that user takes out commodity behavior, puts back to commodity behavior, when stop before shelf
Between, check time of end article, check expression and first shopping list of end article etc..
In a specific application scenarios, the shelf of shopping place have unique shelf to encode, the quotient put on shelf
It is fixed that grade, which is set, and fixed area of the same commodity on shelf has corresponding commodity code, shelf coding, commodity code
It is associated with the coding of behavior photographic device.According to user region in behavior monitoring data, the goods where user can be determined
Frame.It is identified in behavior monitoring data on the slave shelf of user according to user's Shopping Behaviors analytical equipment usage behavior recognizer
It takes out commodity behavior and puts back to commodity behavior, specifically which commodity that target user takes out or puts back to can be determined.User
Shopping Behaviors analytical equipment is that the commodity that target user takes out generate the first shopping list, when commodity are returned to goods by identification user
When frame, which is deleted from the first shopping list.
In another application scenarios, the commodity for identifying that user takes out in direct subordinate act monitoring data, example can also be
Commodity packaging, the size feature that end article is such as taken out according to user, are compared, to obtain mesh in merchandising database
Merchandise news is marked, and is added into the first shopping list.When identifying that commodity are returned to shelf by user, by the commodity from the
It is deleted in one shopping list.
It is of course also possible to which user's Shopping Behaviors are acted and the product features of commodity code and identification of the commodity on shelf
In conjunction with to ensure the accuracy of merchandise news in the first shopping list.
Further, user's Shopping Behaviors analytical equipment can also calculate target user in behavior monitoring data, user
Residence time before shelf, the time for checking end article.The residence time of user and check that the commodity time can reflect use
Desirability and favorable rating of the family to commodity.
Optionally, user's Shopping Behaviors analytical equipment can also identify that user checks target quotient in the second face monitoring data
Expression when product, for example, expression be it is happy, pleasantly surprised, frown.And by user's expression and the first Shopping Behaviors data correlation.
S311: the first Shopping Behaviors data of association and user's login account.
User's Shopping Behaviors analytical equipment is according to the first Shopping Behaviors of above-mentioned mutual incidence relation associated objects user
Data and user's login account compare the first Shopping Behaviors data and the second Shopping Behaviors data with comprehensive analysis.
It is understood that user's Shopping Behaviors analytical equipment receives the collected monitoring data of photographic device real-time Transmission
And save, the behavior of user can be analyzed in time in this way, when user enters shopping place but does not scan the two-dimensional code login, Ye Jiwei
When binding face characteristic information and user and logging in account, user's Shopping Behaviors analytical equipment identify not Chu the second face monitoring data with
The user in behavior monitoring data can mark the user to be, for example, " new user 1 " and save its relevant monitoring data, to
When scanning input two dimensional code logs in, then transfers the associated monitoring data of preservation and analyzed and be associated with.
S312: the second Shopping Behaviors data for the user that user terminal is sent are received.
Wherein, the second Shopping Behaviors data include addition/deletion merchandise news and final clearing in user's login account
Commodity the second shopping list, the merchandise news of addition is that user is obtained by user terminal scan product barcode.
S313: the first Shopping Behaviors data of analysis and the second Shopping Behaviors data establish user's portrait and/or generate early warning
Information.
User's Shopping Behaviors analytical equipment comparative analysis analyzes the first of acquisition in photographic device behavior monitoring data
Shopping Behaviors data and carry out the second Shopping Behaviors data that user terminal user independently acquires, establishes according to the analysis and contrast of the results
User's portrait and/or generation warning information.
Specifically, shopping place is, for example, that exit is provided with region of leaving the theatre, leave the theatre region can not gating lock, user
Completing can be directly off convenient and efficient after self-help shopping is settled accounts.User can certainly be confirmed in region setting door lock of leaving the theatre
Settle accounts it is errorless after door lock automatically open user and leave shopping place.
When user's Shopping Behaviors analytical equipment has been arrived at or will have been supported according to user's face characteristic information confirmation user
Up to it is default leave the theatre region when, analyze the first Shopping Behaviors data and the second Shopping Behaviors data.
On the one hand, user's Shopping Behaviors analytical equipment compares the commodity kind in first shopping list and the second shopping list
Whether class, quantity and specification are consistent, judge whether user has drain junction to calculate, miss clearing or the case where settling accounts more, such as exist inconsistent
The case where, user's Shopping Behaviors analytical equipment generate warning information, with prompt staff confirm the user it is whether wrong clearing/
The case where drain junction is calculated/is settled accounts more.
On the other hand, user's Shopping Behaviors analytical equipment based on residence time of the user before shelf, check end article
Time, the merchandise news taken out/put back to, the merchandise news in addition/deletion virtual shopping cart, the merchandise news etc. of clearing,
The hobby for analyzing user establishes user's portrait of the user.
S314: it generates and suggests with the matched shopping of user portrait and send the shopping to suggest to user terminal.
It can also include generating the shopping suggestion of user (to can be the quotient for recommending user after generating user's portrait
Items record and order, or can be the type of merchandise catalogue and order for recommending user) and suggest shopping to be pushed to user
Terminal, shopping is suggested can also be including the preferential activity etc. of commodity.Facilitate user terminal can be by the favorite commodity of user in this way
Or the commodity often bought are pushed to user, increase the attraction to user, and can be by user's lower Shopping Behaviors correlation online
The program (i.e. on line) that passes through on user terminal of data (commodity etc. that Shopping Behaviors, shopping are suggested, recommended to user) push
To user, on-line off-line data is allowed to be got through, to promote the consumption of user.
By the above-mentioned means, comprehensive analysis adds the Shopping Behaviors of purchase and clearing commodity and user purchasing from user self-help
Object field in behavior expression, accurately collect single user, shopping preferences can get more accurate user's portrait.
Using the Shopping Behaviors of user terminal acquisition user, the intercommunication of on-line off-line data is realized, also helps and passes through user terminal
Related advisory is pushed to user, promote the consumption of user and improves user experience.Meanwhile compare user clearing shopping list and
The user's shopping list identified from monitoring data, it is possible to prevente effectively from the drain junction that is likely to occur of self-help shopping calculate, accidentally clearing or
The case where more clearing, ensures the interests of shopping place and user.
It should be noted that photographic device or face identification camera device acquire monitoring data and fill to user behavior analysis
Setting transmission user monitoring data can be real-time perfoming, therefore in each embodiment of the application, the sequence between each step is only
The stated order of each embodiment in the application is not limited to the execution step of each embodiment of the application, wherein the step of not
Influence technique scheme can be exchanged in the case where realizing.
Referring to Fig. 4, user's Shopping Behaviors analysis system 10 includes user's Shopping Behaviors point in embodiments herein
Analysis apparatus 11, photographic device 12 and user terminal 13.
User's Shopping Behaviors analytical equipment 11 receives behavior monitoring data and the user's end for the user that photographic device 12 is sent
The second Shopping Behaviors data that end 13 is sent, it is comprehensive after analysis behavior monitoring data obtain the first Shopping Behaviors data of the user
After closing the first Shopping Behaviors data of analysis and the second Shopping Behaviors data, user's portrait is established for the user and/or generates early warning
Information.
Wherein, shopping program is opened and logged in user terminal 13 by the two dimensional code being arranged in scanning shopping place, passes through
The commodity bar code for the shopping program scanning that user logs in obtains the second Shopping Behaviors data of user.In the present embodiment, user
Terminal 13 can indicate mobile phone, smart phone, computer, laptop, tablet computer, digit broadcasting receiver, a
Personal digital assistant (PDA, Personal Digital Assistant), portable media player (PMP, Portable
Media Player) and/or navigation equipment.
User's Shopping Behaviors analytical equipment 11 needs before analyzing the first Shopping Behaviors data and the second Shopping Behaviors data
The first Shopping Behaviors data of user and the second Shopping Behaviors data are associated, that is, confirm the first Shopping Behaviors data and
Two Shopping Behaviors data are to belong to same user.Such as can by obtain user's face characteristic information and physical characteristic information into
The association of row the first Shopping Behaviors data and the second Shopping Behaviors data.
In one embodiment, user's Shopping Behaviors analysis system 10 can also include face identification device 14, need
Illustrating, photographic device 12 and face identification camera device 14 can be independent two kinds of photographic devices in the present embodiment,
It can be same photographic device, such as can be depth camera.
The acquisition of face identification camera device 14 monitors number comprising the face of user's face characteristic information and physical characteristic information
According to being sent to user's Shopping Behaviors analytical equipment 11.The face characteristic information of the extraction user of user's Shopping Behaviors analytical equipment 11
And physical characteristic information, user bound face characteristic information and the user's login account obtained from user terminal 13, according to face
Characteristic information positions region locating for user.User's Shopping Behaviors analytical equipment 11 is according to locating for physical characteristic information and user
Region, determines the user in Shopping Behaviors monitoring data, and analysis obtains first shopping of the user in Shopping Behaviors data
Behavioral data is established according to the analysis and contrast of the results to compare the first Shopping Behaviors data of analysis and the second Shopping Behaviors data
User's portrait and/or generation warning information.In some embodiments, it establishes after user's portrait, the analysis of user's Shopping Behaviors
Device 11, which can also be generated, is sent to the user terminal 13 with the matched shopping suggestion of user's portrait.
In yet another embodiment, user's Shopping Behaviors analytical equipment 11, which analyzes the result obtained, can pass through picture
It is shown to staff, so that staff knows the Shopping Behaviors of the user in current shopping place simple and clearly.
Such as user's Shopping Behaviors analysis system 10 can also include monitor (not shown), monitor connects user's shopping
Behavioural analysis device 11 shows the purchase analysis of user as a result, for example marking the year of user on the monitored picture of monitor
Rheological properties is other, membership grade/integral, adds up do shopping number, the cumulative consumption amount of money and shopping preferences etc., can be with emphasis marked articles
Abnormal user is settled accounts, to prompt staff to carry out confirmation verification.
It is (including the user's Shopping Behaviors of each device in the application user's Shopping Behaviors analysis system 10 referring to Fig. 5, Fig. 5
Analytical equipment 11, photographic device 12, user terminal 13 and face identification camera device 14) realize user's Shopping Behaviors analysis method
One interaction schematic diagram.System shown in Fig. 4 realizes that the process of user's Shopping Behaviors analysis method may include following step:
101: user terminal scans the two-dimensional code pull-up shopping program, obtains user login information.
102: user terminal sends user login information to user's Shopping Behaviors analytical equipment.
103: the first face monitoring information of face identification camera device transmission acquisition to user's Shopping Behaviors analytical equipment.
104: user's Shopping Behaviors analytical equipment extracts user's face characteristic information in the first face monitoring information, binding
User's face characteristic information and user's logon account.
105: the second face monitoring data of face identification camera device transmission acquisition to user's Shopping Behaviors analytical equipment.
106: user's Shopping Behaviors analytical equipment identifies the second face monitoring information target according to user's face characteristic information
User obtains region, physical characteristic information and the user's login account of target user.
107: photographic device sends user behavior monitoring data to user's Shopping Behaviors analytical equipment.
108: user's Shopping Behaviors analytical equipment is identical according to region and physical characteristic information similarity is greater than threshold
The condition of value identifies the target user in behavior monitoring data, obtains the first Shopping Behaviors data of target user.
109: user's Shopping Behaviors analytical equipment is associated with the first Shopping Behaviors data and user's login account.
110: the second user Shopping Behaviors data that user terminal transmission obtains to user's Shopping Behaviors analytical equipment.
111: user's Shopping Behaviors analytical equipment analyzes the first Shopping Behaviors data and the second Shopping Behaviors data, establishes and uses
Family portrait and/or generation warning information.
112: user's Shopping Behaviors analytical equipment is generated suggests with the matched shopping of user's portrait.
113: user's Shopping Behaviors analytical equipment, which sends shopping, suggests arriving user terminal.
The above-mentioned Shopping Behaviors analysis method based on shopping cart is generally realized by user's Shopping Behaviors analytical equipment, thus sheet
Application also proposes a kind of user's Shopping Behaviors analytical equipment.It is the analysis of the application user's Shopping Behaviors referring specifically to Fig. 6, Fig. 6
The structural schematic diagram of one embodiment of device.In this embodiment, which includes processor 601, deposits
Reservoir 602 and telecommunication circuit 603, processor 601 couple memory 602 and telecommunication circuit 603, execute instruction at work, with
Cooperation memory 602 and telecommunication circuit 603 realize above-mentioned user's Shopping Behaviors analysis method, specific work process and the above method
It is consistent in embodiment, therefore details are not described herein, please refers to the explanation of the above corresponding method step in detail.
The application provides a kind of storage medium also for realizing above-mentioned user's Shopping Behaviors analysis method, storage medium tool
Body can for USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM,
Random Access Memory), magnetic or disk etc. can store the medium of computer program, or may be storage
There is the server of the computer program, which can be sent to the computer program of storage other equipment operation, Huo Zheye
It can be with the computer program of the self-operating storage.The storage medium can be the combination of multiple entities from physical entity,
Such as multiple servers, server add memory or memory to add the multiple combinations mode such as mobile hard disk.
Above is only an example of the present application, it is not intended to limit the scope of the patents of the application, it is all to utilize this Shen
Please equivalent structure or equivalent flow shift made by specification and accompanying drawing content, be applied directly or indirectly in other relevant skills
Art field similarly includes in the scope of patent protection of the application.
Claims (10)
1. a kind of user's Shopping Behaviors analysis method, which is characterized in that the described method includes:
Receive the behavior monitoring data of the user；
The behavior monitoring data are analyzed, the first Shopping Behaviors data of the user are obtained；
Receive the second Shopping Behaviors data for the user that user terminal is sent；
The first Shopping Behaviors data and the second Shopping Behaviors data are analyzed, user's portrait is established and/or generate early warning
Information.
2. user's Shopping Behaviors analysis method according to claim 1, which is characterized in that the row for receiving the user
To include: before monitoring data
It receives user terminal and passes through the user login information for scanning the two-dimensional code and logging in shopping program, the user login information includes
User's login account and the two dimensional code it is position encoded；
Receive the first face monitoring data with the position encoded associated face identification camera device acquisition；
The first face monitoring data is analyzed, user's face characteristic information of the user is extracted；
Bind user's face characteristic information and user's login account.
3. user's Shopping Behaviors analysis method according to claim 2, which is characterized in that the user login information is into one
Step includes scanning the sweep time of the two dimensional code；
Analysis the first face monitoring data, comprising:
The first face monitoring data in the sweep time is analyzed, user's face characteristic information of the user is extracted.
4. user's Shopping Behaviors analysis method according to claim 2, which is characterized in that analysis first shopping
Before behavioral data and the second Shopping Behaviors data, further include:
Receive the second face monitoring data of at least one face identification camera device acquisition；
According to user's face characteristic information, the user in second monitoring data is identified；
According to the second face monitoring data, region and the physical characteristic information of the user are obtained；
It is associated with the region, the physical characteristic information and user's login account；
The analysis behavior monitoring data, obtain the first Shopping Behaviors data of the user, comprising:
According to the region is identical and the physical characteristic information similarity is greater than the condition of threshold value, the row is identified
For the user in monitoring data；
According to the behavior monitoring data, the first Shopping Behaviors data of the user, the first Shopping Behaviors data are obtained
The first shopping list including the commodity finally taken out；
It is associated with the first Shopping Behaviors data and user's login account.
5. user's Shopping Behaviors analysis method according to claim 4, which is characterized in that described to obtain the of the user
One Shopping Behaviors data, comprising:
Identify that the Shopping Behaviors of user described in the behavior monitoring data, the Shopping Behaviors include taking out commodity behavior, putting
Return commodity behavior；
Based on the region and the Shopping Behaviors, the first Shopping Behaviors data of the user are obtained.
6. user's Shopping Behaviors analysis method according to claim 4, which is characterized in that
The second Shopping Behaviors data include addition/deletion merchandise news and finally settling accounts in user's login account
Second shopping list of commodity, the merchandise news of the addition are that the user passes through the user terminal scan product barcode
It obtains.
7. user's Shopping Behaviors analysis method according to claim 6, which is characterized in that analysis first shopping
Behavioral data and the second Shopping Behaviors data, generating warning information includes:
Confirm that the user has arrived at default region of leaving the theatre according to user's face characteristic information；
Judge whether first shopping list and second shopping list are consistent；
If inconsistent, warning information is generated, to prompt staff to confirm whether the user misses clearing/drain junction and calculate/tie more
Calculate commodity.
8. -7 any user's Shopping Behaviors analysis method according to claim 1, which is characterized in that the analysis described the
One Shopping Behaviors data and the second Shopping Behaviors data are established after user's portrait, comprising:
It generates and suggests with the matched shopping of user portrait；
The shopping is sent to suggest to user terminal.
9. a kind of user's Shopping Behaviors analytical equipment, which is characterized in that user's Shopping Behaviors analytical equipment include processor,
Memory and telecommunication circuit, the processor couple the memory, telecommunication circuit, execute instruction at work, to cooperate
State memory, telecommunication circuit realizes the described in any item user's Shopping Behaviors analysis methods of claim 1-8.
10. a kind of storage medium, which is characterized in that the storage medium is stored with computer program, the computer program quilt
It is realized when execution such as the step of any one of claim 1-8 the method.
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| CN112561648B (en) * | 2020-12-17 | 2022-07-29 | 浙江信网真科技股份有限公司 | Commodity pushing method and device for dynamically generating preference group |
| CN112669095A (en) * | 2021-03-15 | 2021-04-16 | 北京焦点新干线信息技术有限公司 | Client portrait construction method and device, electronic equipment and computer storage medium |
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