CN107169797A - Intelligent shopping guide method, system, shared server and intelligent shopping guide robot - Google Patents
Intelligent shopping guide method, system, shared server and intelligent shopping guide robot Download PDFInfo
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Abstract
The disclosure provides a kind of intelligent shopping guide method, system, shared server and intelligent shopping guide robot, and the intelligent shopping guide method includes：Classified according to customer data, model the Data Model of Customer Information for obtaining including multiple customer types；Classified according to shop data and garment data, model the garment data model for obtaining including multiple types of garments；By carrying out classification analysis to purchasing history, matched with reference to the Data Model of Customer Information and the garment data model, obtain the clothes suggested design for a certain customer type.This method is more intelligent, and more personalized, hommization clothes suggested design is provided for client.
Description
Technical field
This disclosure relates to technical field of intelligent equipment, in particular to a kind of intelligent shopping guide method, system, shared clothes
Be engaged in device and intelligent shopping guide robot.
Background technology
With the development of science and technology people step up to the demand of intelligent automation service in the modern life, some services
Class place (such as market or supermarket) supporting smart machine also arises at the historic moment.
At present, there is shopping guide robot in part market or supermarket, can realize that market region route is shown, cruising refers to
Draw, simple speech dialogue function, meet the primary demand of consumer.For example, being shown with simple route and commodity presentation
The shopping guide robot of function.
Consumer demand is more and personalized stronger, and higher requirement is proposed to shopping guide robot, letter is not only needed
Single route is shown and simple commodity are presented, in addition it is also necessary to provide more intelligent, the personalized clothes for buying suggestion and profound level
Business experience.Therefore, also there is the part that has much room for improvement in technical scheme of the prior art.
It should be noted that information is only used for strengthening the reason of background of this disclosure disclosed in above-mentioned background section
Solution, therefore can include not constituting the information to prior art known to persons of ordinary skill in the art.
The content of the invention
The purpose of the disclosure is to provide a kind of intelligent shopping guide method, system, shared server and intelligent shopping guide robot,
And then one or more problem caused by limitation and defect due to correlation technique is at least overcome to a certain extent.
Other characteristics and advantage of the disclosure will be apparent from by following detailed description, or partially by this public affairs
The practice opened and acquistion.
According to an aspect of this disclosure there is provided a kind of intelligent shopping guide method, including：
Classified according to customer data, model the Data Model of Customer Information for obtaining including multiple customer types；
Classified according to shop data and garment data, model the garment data mould for obtaining including multiple types of garments
Type；
By carrying out classification analysis to purchasing history, carried out with reference to the Data Model of Customer Information and the garment data model
Matching, obtains the clothes suggested design for a certain customer type.
In a kind of exemplary embodiment of the disclosure, the customer data also includes physical characteristic；
Facial image is gathered by imaging sensor, and the facial image analyze to obtain the customer data,
Wherein described customer data includes age characteristics, sex character, features of skin colors and psychological characteristics；
The customer data for obtaining including the physical characteristic is scanned and analyzed to human body by 3-D scanning sensor.
In a kind of exemplary embodiment of the disclosure, carrying out analysis to the facial image includes：
It is trained based on the facial image, obtains faceform；
Age, sex, the colour of skin and psychology are detected and trained respectively based on the faceform, the client is obtained
Data model, multiple customer class are included wherein according to age, sex, the colour of skin and psychology divide in the Data Model of Customer Information
Type.
In a kind of exemplary embodiment of the disclosure, in addition to：
The Data Model of Customer Information is uploaded and shared server is stored；
New facial image is collected, and uploads to the shared server；
The new facial image carries out human face region detection through the Data Model of Customer Information in the shared server
And feature extraction, obtain age characteristics corresponding with the new face, sex character, features of skin colors and psychological characteristics.
In a kind of exemplary embodiment of the disclosure, in addition to：By recommending successfully and recommending failure to what is deposited
Record is analyzed, and is matched with reference to the Data Model of Customer Information and the garment data model, obtains being directed to a certain client
The clothes suggested design of type, this guidance scheme is provided or according to the clothes suggested design according to the clothes suggested design
Fitting effects figure is generated with described field guidance scheme and strolls shop route map.
According to another aspect of the present disclosure, a kind of intelligent shopping guide system is also provided, including：
Customer modeling module, for being classified according to customer data, modeling the client for obtaining including multiple customer types
Data model；
Clothes modeling module, obtains including multiple clothes for according to shop data and garment data being classified, being modeled
Fill the garment data model of type；
Matching module is analyzed, for by carrying out classification analysis to purchasing history, with reference to the Data Model of Customer Information and institute
State garment data model to be matched, obtain the clothes suggested design for a certain customer type.
In a kind of exemplary embodiment of the disclosure, in addition to：
Memory module, for storing the customer data, the shop data and the garment data；
Input module, for gathering facial image；
Output module, for the clothes suggested design to be showed in the form of image or voice.
In the another further aspect of the disclosure, a kind of shared server is also provided, including：
Customer modeling module, for being classified according to customer data, modeling the client for obtaining including multiple customer types
Data model；
Clothes modeling module, obtains including multiple clothes for according to shop data and garment data being classified, being modeled
Fill the garment data model of type；
Matching module is analyzed, for by carrying out classification analysis to purchasing history, with reference to the Data Model of Customer Information and institute
State garment data model to be matched, obtain the clothes suggested design for a certain customer type；
Memory module, for storing the customer data, the shop data and the garment data.
At the another aspect of the disclosure, a kind of intelligent shopping guide robot, including above-described intelligent shopping guide system are also provided
System and display screen, the display screen are multi-section display screen；
The multi-section display screen includes fitting effects viewing area, this recommendation viewing area and analysis and suggestion area, described to try on
Effect viewing area is used to show the picture or video for the fitting effects for recommending clothes for customer type, and described field is recommended aobvious
Show that area is used for the store location for being shown in this purchase institute recommended clothes, the analysis and suggestion area is used for display recommendation
Analysis and reason.
In the intelligent shopping guide method of some embodiments of the disclosure, respectively to customer data, shop data and garment data
Carry out classification model construction and obtain Data Model of Customer Information and clothes data model, by carrying out analysis combination customer data to purchasing history
Model and clothes data model are matched, and obtain clothes suggested design.
It should be appreciated that the general description of the above and detailed description hereinafter are only exemplary and explanatory, not
The disclosure can be limited.
Brief description of the drawings
Accompanying drawing herein is merged in specification and constitutes the part of this specification, shows the implementation for meeting the disclosure
Example, and be used to together with specification to explain the principle of the disclosure.It should be evident that drawings in the following description are only the disclosure
Some embodiments, for those of ordinary skill in the art, on the premise of not paying creative work, can also basis
These accompanying drawings obtain other accompanying drawings.
Fig. 1 shows a kind of flow chart of intelligent shopping guide method in disclosure exemplary embodiment.
Fig. 2 shows the flow chart analyzed in disclosure exemplary embodiment facial image.
Fig. 3 shows under line on customer data modeling and line that subsequent applications model is trained and obtains user characteristics
Schematic diagram.
Fig. 4 shows a kind of schematic diagram of intelligent shopping guide system in disclosure exemplary embodiment.
Fig. 5 shows the schematic diagram of another intelligent shopping guide system in disclosure exemplary embodiment.
Fig. 6 shows a kind of schematic diagram of shared server in disclosure exemplary embodiment.
Fig. 7 shows a kind of schematic diagram of terminal in disclosure exemplary embodiment.
Fig. 8 shows a kind of schematic diagram of intelligent shopping guide robot in disclosure exemplary embodiment.
Fig. 9 shows a kind of configuration diagram of intelligent shopping guide robot in disclosure exemplary embodiment.
Embodiment
Example embodiment is described more fully with referring now to accompanying drawing.However, example embodiment can be with a variety of shapes
Formula is implemented, and is not understood as limited to example set forth herein；On the contrary, thesing embodiments are provided so that the disclosure will more
Fully and completely, and by the design of example embodiment those skilled in the art is comprehensively conveyed to.Accompanying drawing is only the disclosure
Schematic illustrations, be not necessarily drawn to scale.Identical reference represents same or similar part in figure, thus
Repetition thereof will be omitted.
Implement in addition, described feature, structure or characteristic can be combined in any suitable manner one or more
In mode.In the following description there is provided many details so as to provide fully understanding for embodiment of this disclosure.So
And, it will be appreciated by persons skilled in the art that the technical scheme of the disclosure can be put into practice and one in the specific detail is omitted
Or more, or can be using other methods, constituent element, device, step etc..In other cases, it is not shown in detail or describes
Known features, method, device, realization, material are operated to avoid that a presumptuous guest usurps the role of the host so that each side of the disclosure becomes mould
Paste.
Some block diagrams shown in accompanying drawing are functional entitys, not necessarily must be with physically or logically independent entity phase
Correspondence.These functional entitys can be realized using software form, or in one or more hardware modules or integrated circuit in fact
These existing functional entitys, or realize that these functions are real in heterogeneous networks and/or processor device and/or microcontroller device
Body.
In purchase, some are not related to during the commodity of oneself state or situation consumer, for example, buy a book, shopping guide
Robot only need to can provide corresponding best books according to the content of title and book；And can be related to consumer certainly for some
The purchase action of body state or situation, for example, buy clothes, and shopping guide robot also needs to consider some essential informations of consumer
(height, body weight etc.) can just provide the suggested design for more conforming to consumer psychology.
Fig. 1 shows a kind of flow chart of intelligent shopping guide method in disclosure exemplary embodiment.
As shown in figure 1, in step slo, being classified according to customer data, modeling what is obtained comprising multiple customer types
Data Model of Customer Information.
The characteristic information that customer data in the disclosure is included is relatively broad, in certain embodiments, and customer data includes
Age characteristics, sex character, features of skin colors and psychological characteristics, in further embodiments, customer data is except including above-mentioned spy
Outside levying, physical characteristic can also be included.And can be divided into multiple customer class from multiple dimensions by client according to these features
Client, for example, client can be divided into youth, middle age, old age etc. according to age characteristics, can be divided into by type according to sex character
Client, can be divided into the colour of skin is partially white, the colour of skin yellow, colour of skin partially is moderate etc., according to psychological special by women and male according to features of skin colors
Client can be divided into happiness, calmness, sadness etc. by levying, and it is moderate that client can be divided into partially fat, partially thin, build according to physical characteristic
Deng.Therefore multiple client's submodels can be included in Data Model of Customer Information.
In one exemplary embodiment, the feature of description Data Model of Customer Information includes：Sex, age, the colour of skin, psychology, body
Type etc., needs to obtain by different acquisition means for characteristic information different in customer data, wherein, sex, age, skin
Four features such as color, psychology can be by being analyzed the facial image that imaging sensor is collected, and physical characteristic can
To be scanned by 3-D scanning sensor to human body and analysis is obtained.
In one exemplary embodiment, to facial image carry out analyze obtain user portrait can be by model training twice
Obtain, Fig. 2 shows the flow chart analyzed in disclosure exemplary embodiment facial image.
As shown in Fig. 2 in step S101, being trained based on facial image, faceform is obtained.
As shown in Fig. 2 in step s 102, being detected respectively to age, sex, the colour of skin and psychology based on faceform
And training, Data Model of Customer Information is obtained, division bag is carried out according to age, sex, the colour of skin and psychology wherein in Data Model of Customer Information
Containing multiple customer types.
Carry out needing enough sample datas during model training, firstly, it is necessary to carry face mark with enough
The sample database of note, then, extracts the Haar features of these samples, and enter using machine learning algorithms such as Adaboost respectively
Row training, finally gives faceform.
Obtain after faceform, (glad, tranquil, sad also is detected to sex-screening, age detection, Face Detection, psychology
Wound) model be trained, training of the process with faceform, it is still desirable to prepare sufficient sample data and be trained, and
The features such as LBP (Local Binary Pattern, local binary patterns), Haar are extracted, are instructed using machine learning algorithm
Practice.LBP is a kind of operator for being used for describing image local textural characteristics, and it has rotational invariance and gray scale consistency etc. significantly
Advantage.
Haar features are to be designed what is obtained according to the area grayscale contrastive feature of image, and Haar characteristic values are defined as white
The difference of color area pixel value sum and black region pixel value sum.Haar characteristic values reflect the grey scale change feelings of image
Condition.For example：Some features of face can simply be described by rectangular characteristic, such as：Eyes are deeper than cheek color, bridge of the nose both sides
Deeper than bridge of the nose color, face is deeper etc. than ambient color.But rectangular characteristic is only to some simple graphic structures, such as edge,
Line segment is more sensitive, so the structure of particular orientation (level, vertical, diagonal) can only be described.
AdaBoost algorithms are a kind of adaptive Boosting algorithms, and basic thought is to choose some Weak Classifiers, group
Synthesize strong classifier.Weak Classifier form is as follows：
Wherein, x is sample, and f is the Haar features extracted, and θ is fixed threshold, can be set manually, and p is inequality direction
Polarity.
But the classification performance of single Weak Classifier is very poor, it is necessary to which AdaBoost algorithms are weighted the multiple weak typings of combination
Device is strengthened, and step is as follows：
1) sample image (x1, y1) is given, (x2, y2), wherein (x3, y3) ..., y values represent positive negative sample, and+1 is positive sample
This, -1 is negative sample.
2) initialization sample weight,M and l are positive and negative sample number respectively.
3) for t=1 ..., T
1. weights are normalized
2. the minimum Weak Classifier of weighted error is selected
3. weights are updated
If classification is correct, ei=0, otherwise ei=1,
4. the strong classifier finally given is：
In Data Model of Customer Information modeling process, not only consider external some features of client, for example sex, the age,
Colour of skin etc., it is also contemplated that the inherent characteristic information of client, such as psychological condition is glad or sadness so that the knot of recommendation
Fruit more hommization, more likely close to expectations of customer.
In one exemplary embodiment, can also be further by Data Model of Customer Information after obtaining Data Model of Customer Information
Pass and store shared server, for example, it may be cloud server.New facial image is gathered using imaging sensor, and
New facial image is uploaded into shared server, so, new facial image is through the Data Model of Customer Information in shared server
Carry out human face region detection and feature extraction, obtain age characteristics corresponding with new face, sex character, features of skin colors and
Psychological characteristics.By the way that Data Model of Customer Information is uploaded into shared server, after collecting new facial image every time, in addition it is also necessary to
Facial image is also uploaded to shared server can just calculate corresponding characteristic information, be conducive to the quick place of image
Reason, reduces the excessive calculating pressure caused of terminal processes data volume.
Based on above-mentioned, Fig. 3 shows under line on customer data modeling and line that subsequent applications model is trained and obtained
The schematic diagram of user characteristics.
As shown in figure 1, in step S20, being classified according to shop data and garment data, modeling and obtain comprising many
The garment data model of individual types of garments.
In one exemplary embodiment, clothes are classified according to different dimensions, general Main Basiss customer data mould
The criteria for classifying of type is classified to clothes, in addition, is further divided according to customer priorities, such as according to product
Board, style, price etc. are divided, and garment data model is divided into multiple clothes submodels, and matching and client's progress are carried out to facilitate
Selection.
In a further exemplary embodiment, except it is simple clothes are classified in addition to, in addition it is also necessary to reference to Ben Chang shops
Distributed intelligence is classified to clothes, for example second floor women's dress, three buildings men's clothing etc..In addition, shop data can also be entered except floor
One step is specific to action message in stand number, and shop etc., but the disclosure is not limited to this.
As shown in figure 1, in step s 30, by carrying out classification analysis to purchasing history, with reference to Data Model of Customer Information kimonos
Dress data model is matched, and obtains the clothes suggested design for a certain customer type.
In the exemplary embodiment, purchasing history is the purchase information of the historic customer of record, the age of such as client, skin
Color, build, preference, purchasing power and the record for buying success or failure.Analyzed by the purchasing history to client, in knot
Close customer data and clothes Data Matching provides corresponding clothes suggested design and more conforms to expectations of customer, recommend success rate also can
It is higher.
In the exemplary embodiment, except being recommended according to purchasing history, it can also be entered according to others' record information
Row is recommended, and can include but is not limited to following methods：
By to deposited recommend successfully and recommend failure record analyze, with reference to Data Model of Customer Information and clothes number
Matched according to model, obtain the clothes suggested design for a certain customer type.
Below by taking client A as an example, client A purchasing history is inquired about first, to obtain client A is more likely to which wind bought
Lattice, the clothes in which price, so as to provide corresponding clothes suggested design, properer purchase suggestion is provided to client.
If the purchasing history for not finding client A is analyzed purchasing history, with reference to client A age, sex, the colour of skin, psychology, body
The characteristic informations such as type, find the purchasing history for the client B that characteristic information is closer to from purchasing history, can equally give client
Properer purchase suggestion is provided.If with reference to characteristic informations such as client A age, sex, the colour of skin, psychology, builds, do not had yet
There is the purchasing history for finding and being closer to, then search and recommend the record information of success or failure to provide clothes recommendation, and in client
The evaluation or reflection provided to clothes suggested design is recorded as success or failure, and this recommendation storage is arrived into shared service
Device.Such as client checks that the time of a certain bar clothes suggested design is longer, is defined as recommending successfully, if client checks a certain
The time of bar clothes suggested design is shorter, is defined as recommending failure.Judge to recommend the foundation of success or failure there are other
With reference to the disclosure is not specifically limited.
In one exemplary embodiment, except providing clothes suggested design, provided yet further still according to clothes suggested design
This boot scheme, that is, give client's offer the corresponding guidance scheme in this further combined with shop data, it is convenient
Client implements to buy according to suggested design.
In one exemplary embodiment, the intelligent shopping guide method also includes according to clothes suggested design and this boot scheme
Generate fitting effects figure and stroll shop line map, allow client more intuitively to be accounted for suggested design.
In summary, intelligent shopping guide method disclosed in disclosure embodiment, respectively to customer data, shop data kimonos
Dress data carry out classification model construction and obtain Data Model of Customer Information and clothes data model, and visitor is combined by carrying out analysis to purchasing history
User data model and clothes data model are matched, and obtain clothes suggested design.This method can be used for solving existing push away
The problem of recommending scheme not enough intelligence and be personalized, by being matched to Data Model of Customer Information and clothes data model, there is provided more
Added with targetedly suggested design, can effectively it improve customer satisfaction.
Fig. 4 shows a kind of schematic diagram of intelligent shopping guide system in disclosure exemplary embodiment.As shown in figure 4, the intelligence
Purchase guiding system 100 includes customer modeling module 110, clothes modeling module 120 and analysis matching module 130.
Customer modeling module 110 is used to according to customer data be classified, model the visitor for obtaining including multiple customer types
User data model.Clothes modeling module 120 is obtained comprising many for being classified according to shop data and garment data, being modeled
The garment data model of individual types of garments.Analyzing matching module 130 is used for by carrying out classification analysis to purchasing history, with reference to visitor
User data model and clothes data model are matched, and obtain the clothes suggested design for a certain customer type.
The intelligent shopping guide system carries out classification model construction to customer data, shop data and garment data respectively and obtains client's number
According to model and clothes data model, by being carried out to purchasing history, analysis combines Data Model of Customer Information and clothes data model is carried out
Matching, obtains clothes suggested design.The problem of this method can be used for solving existing suggested design not enough intelligence and being personalized,
By being matched Data Model of Customer Information and clothes data model there is provided more targetedly suggested design, can effectively it carry
Rise CSAT.
Fig. 5 also illustrates the schematic diagram of another intelligent shopping guide system in disclosure exemplary embodiment.As shown in figure 5, should
Intelligent shopping guide system 100 includes customer modeling module 110, clothes modeling module 120, analysis matching module 130, memory module
140th, input module 150 and output module 160.
Customer modeling module 110 is used to according to customer data be classified, model the visitor for obtaining including multiple customer types
User data model.Clothes modeling module 120 is obtained comprising many for being classified according to shop data and garment data, being modeled
The garment data model of individual types of garments.Analyzing matching module 130 is used for by carrying out classification analysis to purchasing history, with reference to visitor
User data model and clothes data model are matched, and obtain the clothes suggested design for a certain customer type.Memory module
140 are used to store customer data, shop data and garment data.Input module 150 is used to gather facial image, output module
160 are used to show clothes suggested design in the form of image or voice.
In one exemplary embodiment, the information such as facial image of the collection of input module 150 client, memory module 140 is also
For the initial data of collection to be pre-processed, it is converted into after structural data according still further to customer data, garment data and shop
Paving data are stored.Memory module 140 is used to store customer data, the age of such as client, the colour of skin, sex, build, purchase
Power, preference and the record to the lead referral success or failure, to carry out analysis matching according to these data of storage.Shop
Spreading data includes stand number, action message etc., and garment data includes classification, price, style, color, size etc..Memory module
140, which are additionally operable to storage one, guides language database, and guiding language is exactly the voice for playing clothes suggested design.
In one exemplary embodiment, the clothes suggested design obtained can be through output module 160 with image or voice
Form is presented to client, can be in following at least one forms：
Image is shown by display screen, or is printed as paper document and is shown, is come out also or by speech play.
Fig. 6 shows a kind of schematic diagram of shared server in disclosure exemplary embodiment.The shared server 600 includes
Customer modeling module 610, clothes modeling module 620, analysis matching module 630 and memory module 640.
Customer modeling module 610 is used to according to customer data be classified, model the visitor for obtaining including multiple customer types
User data model.Clothes modeling module 620 is obtained comprising many for being classified according to shop data and garment data, being modeled
The garment data model of individual types of garments.Analyzing matching module 630 is used for by carrying out classification analysis to purchasing history, with reference to visitor
User data model and clothes data model are matched, and obtain the clothes suggested design for a certain customer type.Memory module
640 are used to store customer data, shop data and garment data.
Fig. 7 shows a kind of schematic diagram of terminal in disclosure exemplary embodiment, the terminal 700 include input module 710,
Output module 720 and the communication module 730 communicated with shared server 600 shown in Fig. 6.Input module 710 include but
Camera, scanner, microphone, Fingerprint Identification Unit etc. are not limited to, for gathering customer data.Output module 720 is included but not
It is limited to display screen, printer, speech player etc..The information that communication module 730 is used to gather input module 710 is sent to altogether
Server 600 is enjoyed, is also used for receiving the clothes suggested design of shared server 600 and is showed in the form of voice or image
Come.
Fig. 8 shows a kind of schematic diagram of intelligent shopping guide robot, the intelligent shopping guide machine in disclosure exemplary embodiment
People 800 in addition to hardware and outward appearance including having general robot more, in addition to the intelligent shopping guide system in above-described embodiment
System and display screen 810.
In one exemplary embodiment, the display screen 810 is multi-section display screen, including fitting effects viewing area 811, Ben Chang
Recommend viewing area 812 and analysis and suggestion area 813, fitting effects viewing area 811 is used to show recommends clothes for customer type
Fitting effects picture or video, this recommends viewing area 812 to be used for the shop for being shown in this clothes that purchase is recommended
Bunk is put, and analysis and suggestion area 813 is used to show the analysis recommended and reason.
In one exemplary embodiment, the intelligent shopping guide robot 800 also has printer 820, for by fitting effects
Scheme and stroll shop route map and print by printer to take viewing away for client.In addition, the intelligent shopping guide robot 800 also has
Speech player, for clothes suggested design to be come out with speech play.
In one exemplary embodiment, Fig. 9 shows the configuration diagram of the intelligent shopping guide robot.The intelligent robot can
It is arranged on so that components of system as directed is included into memory module, modeling unit and analysis matching module in shared server, modeling unit is used
In realizing the monitoring of Data Model of Customer Information and clothes data model, and only retain the input module of hardware device part, output mould
Block, realizes the input of customer data information and the output of suggested design.
In addition, the detail for each step for being modeled and matching in above-mentioned intelligent shopping guide system, corresponding
It is described in detail in embodiment, therefore here is omitted.Although moreover, describing this in the accompanying drawings with particular order
Each step of method in open, still, this does not require that or implied must perform these steps according to the particular order,
Or the step having to carry out shown in whole could realize desired result.It is additional or alternative, it is convenient to omit some steps,
Multiple steps are merged into a step execution, and/or a step is decomposed into execution of multiple steps etc..
Those skilled in the art will readily occur to its of the disclosure after considering specification and putting into practice invention disclosed herein
Its embodiment.The application is intended to any modification, purposes or the adaptations of the disclosure, these modifications, purposes or
Person's adaptations follow the general principle of the disclosure and including the undocumented common knowledge in the art of the disclosure
Or conventional techniques.Description and embodiments are considered only as exemplary, and the true scope of the disclosure and spirit are by appended
Claim is pointed out.
Claims (9)
1. a kind of intelligent shopping guide method, it is characterised in that including：
Classified according to customer data, model the Data Model of Customer Information for obtaining including multiple customer types；
Classified according to shop data and garment data, model the garment data model for obtaining including multiple types of garments；
By carrying out classification analysis to purchasing history, with reference to the Data Model of Customer Information and garment data model progress
Match somebody with somebody, obtain the clothes suggested design for a certain customer type.
2. intelligent shopping guide method according to claim 1, it is characterised in that the customer data also includes physical characteristic；
Facial image is gathered by imaging sensor, and the facial image analyze to obtain the customer data, wherein
The customer data includes age characteristics, sex character, features of skin colors and psychological characteristics；
The customer data for obtaining including the physical characteristic is scanned and analyzed to human body by 3-D scanning sensor.
3. intelligent shopping guide method according to claim 2, it is characterised in that carrying out analysis to the facial image includes：
It is trained based on the facial image, obtains faceform；
Age, sex, the colour of skin and psychology are detected and trained respectively based on the faceform, the customer data is obtained
Model, multiple customer types are included wherein according to age, sex, the colour of skin and psychology divide in the Data Model of Customer Information.
4. intelligent shopping guide method according to claim 2, it is characterised in that also include：
The Data Model of Customer Information is uploaded and shared server is stored；
New facial image is collected, and uploads to the shared server；
The new facial image through in the shared server the Data Model of Customer Information carry out human face region detection and
Feature extraction, obtains age characteristics corresponding with the new face, sex character, features of skin colors and psychological characteristics.
5. intelligent shopping guide method according to claim 1, it is characterised in that also include：Pass through the recommendation success to having deposited
Analyzed with the record for recommending to fail, matched, obtained with reference to the Data Model of Customer Information and the garment data model
For the clothes suggested design of a certain customer type, this guidance scheme is provided or according to described according to the clothes suggested design
Clothes suggested design and described field guidance scheme generate fitting effects figure and stroll shop route map.
6. a kind of intelligent shopping guide system, it is characterised in that including：
Customer modeling module, for being classified according to customer data, modeling the customer data for obtaining including multiple customer types
Model；
Clothes modeling module, obtains including multiple clothings for according to shop data and garment data being classified, being modeled
The garment data model of type；
Matching module is analyzed, for by carrying out classification analysis to purchasing history, with reference to the Data Model of Customer Information and the clothes
Dress data model is matched, and obtains the clothes suggested design for a certain customer type.
7. intelligent shopping guide system according to claim 6, it is characterised in that also include：
Memory module, for storing the customer data, the shop data and the garment data；
Input module, for gathering facial image；
Output module, for the clothes suggested design to be showed in the form of image or voice.
8. a kind of shared server, it is characterised in that including：
Customer modeling module, for being classified according to customer data, modeling the customer data for obtaining including multiple customer types
Model；
Clothes modeling module, obtains including multiple clothings for according to shop data and garment data being classified, being modeled
The garment data model of type；
Matching module is analyzed, for by carrying out classification analysis to purchasing history, with reference to the Data Model of Customer Information and the clothes
Dress data model is matched, and obtains the clothes suggested design for a certain customer type；
Memory module, for storing the customer data, the shop data and the garment data.
9. a kind of intelligent shopping guide robot, it is characterised in that including the intelligent shopping guide system any one of claim 6-7
And display screen, the display screen is multi-section display screen；
The multi-section display screen includes fitting effects viewing area, this recommendation viewing area and analysis and suggestion area, the fitting effects
Viewing area is used to show the picture or video for the fitting effects for recommending clothes for customer type, and viewing area is recommended in described field
Store location for being shown in this purchase institute recommended clothes, the analysis and suggestion area is used to show dividing for recommendation
Analysis and reason.
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| CN107702706A (en) * | 2017-09-20 | 2018-02-16 | 广东欧珀移动通信有限公司 | Determining method of path, device, storage medium and mobile terminal |
| CN108182411A (en) * | 2017-12-29 | 2018-06-19 | 深圳云天励飞技术有限公司 | Data processing system and device |
| CN108197980A (en) * | 2017-12-27 | 2018-06-22 | 中国科学院上海高等研究院 | Illustration generation method/system, storage medium and the terminal of personalized shopper |
| CN108596646A (en) * | 2018-03-15 | 2018-09-28 | 南京航空航天大学 | A kind of garment coordination recommendation method of fusion face character analysis |
| CN108748218A (en) * | 2018-08-27 | 2018-11-06 | 浙江华嘉驰智能科技有限公司 | New retail intelligent shopping guide robot and shopping guide method based on machine vision Yu AR technologies |
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| CN108197980A (en) * | 2017-12-27 | 2018-06-22 | 中国科学院上海高等研究院 | Illustration generation method/system, storage medium and the terminal of personalized shopper |
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| CN108596646A (en) * | 2018-03-15 | 2018-09-28 | 南京航空航天大学 | A kind of garment coordination recommendation method of fusion face character analysis |
| CN108596646B (en) * | 2018-03-15 | 2021-07-27 | 南京航空航天大学 | Garment matching recommendation method integrating face attribute analysis |
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| CN110706014A (en) * | 2018-07-10 | 2020-01-17 | 杭州海康威视系统技术有限公司 | Shopping mall store recommendation method, device and system |
| CN109165992A (en) * | 2018-07-16 | 2019-01-08 | 北京旷视科技有限公司 | A kind of intelligent shopping guide method, apparatus, system and computer storage medium |
| CN109165997A (en) * | 2018-07-19 | 2019-01-08 | 阿里巴巴集团控股有限公司 | It does shopping under a kind of line the generation method and device of recommendation |
| CN108748218A (en) * | 2018-08-27 | 2018-11-06 | 浙江华嘉驰智能科技有限公司 | New retail intelligent shopping guide robot and shopping guide method based on machine vision Yu AR technologies |
| CN109598578A (en) * | 2018-11-09 | 2019-04-09 | 深圳壹账通智能科技有限公司 | The method for pushing and device of business object data, storage medium, computer equipment |
| CN109816441A (en) * | 2018-12-29 | 2019-05-28 | 江苏云天励飞技术有限公司 | Tactful method for pushing, system and relevant apparatus |
| CN109816441B (en) * | 2018-12-29 | 2021-05-11 | 江苏云天励飞技术有限公司 | Policy pushing method, system and related device |
| CN110223114A (en) * | 2019-05-29 | 2019-09-10 | 恩亿科(北京)数据科技有限公司 | The method and apparatus for managing the characteristic information of user |
| CN110348955A (en) * | 2019-06-26 | 2019-10-18 | 秒针信息技术有限公司 | User wears the display methods for taking image and device, storage medium, electronic device |
| CN110689374A (en) * | 2019-09-27 | 2020-01-14 | 广州云从人工智能技术有限公司 | Commodity shopping guide management method, system, medium and equipment |
| CN110674760A (en) * | 2019-09-27 | 2020-01-10 | 广州云从人工智能技术有限公司 | Interactive guiding method, device, equipment and medium based on mobile robot |
| CN110941774A (en) * | 2019-12-05 | 2020-03-31 | 深圳前海达闼云端智能科技有限公司 | Service recommendation method |
| CN111311357A (en) * | 2020-01-20 | 2020-06-19 | 昊居科技有限公司 | House transaction information management method and system |
| CN111311357B (en) * | 2020-01-20 | 2024-05-03 | 昊居科技有限公司 | House transaction information management method and system |
| CN111967942A (en) * | 2020-08-20 | 2020-11-20 | 渭南双盈未来科技有限公司 | Intelligent shopping method |
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