CN108846724A - Data analysing method and system - Google Patents
Data analysing method and system Download PDFInfo
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- CN108846724A CN108846724A CN201810573614.7A CN201810573614A CN108846724A CN 108846724 A CN108846724 A CN 108846724A CN 201810573614 A CN201810573614 A CN 201810573614A CN 108846724 A CN108846724 A CN 108846724A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0282—Rating or review of business operators or products
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0621—Item configuration or customization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/161—Detection; Localisation; Normalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/174—Facial expression recognition
- G06V40/175—Static expression
Abstract
Present disclose provides a kind of data analysing method and systems, are related to data processing field.This method includes：Face datection is carried out to the video data of acquisition and obtains face image data；The attribute information that face character analyzes to obtain client in retail shop is carried out to face image data；The commodity that the lead referral into retail shop is determined based on the attribute information of client, improve the accuracy of commercial product recommending.In addition, the disclosure can also determine client's smile value according to client's expression information, according to the corresponding relationship of client's smile value and satisfaction, the shopping satisfaction of client in retail shop is determined, to optimize retail shop's service quality according to Customer Shopping satisfaction.On the other hand, the disclosure can also analyze at least one in retail shop's transaction data and turnover data with volume of the flow of passengers data, retail shop's business condition be determined, to carry out marketing optimization according to retail shop's business condition.
Description
Technical field
This disclosure relates to data processing field more particularly to a kind of data analysing method and system.
Background technique
Recently as the development of deep learning and face technology, Face datection, identification, attributive analysis are gradually breached
Bottleneck in accuracy rate is able to apply in actual business scenario.But in the prior art, the customer in retail shop can't be done
To accurate Recommendations, so that Customer Shopping experience is bad.
Summary of the invention
The disclosure technical problem to be solved is to provide a kind of data analysing method and system, can be improved commodity and pushes away
The accuracy recommended.
On the one hand according to the disclosure, a kind of data analysing method is proposed, including：Face inspection is carried out to the video data of acquisition
Measure face image data；The attribute information that face character analyzes to obtain client in retail shop is carried out to face image data；Base
The commodity of the lead referral into retail shop are determined in the attribute information of client.
Optionally, this method further includes：Expression Recognition is carried out to face image data and obtains client's expression information；According to visitor
Family expression information determines client's smile value；According to the corresponding relationship of client's smile value and satisfaction, the purchase of client in retail shop is determined
Object satisfaction.
Optionally, this method further includes：Pedestrian detection is carried out to face image data to analyze to obtain volume of the flow of passengers data；By quotient
At least one in paving transaction data and turnover data is analyzed with volume of the flow of passengers data, determines retail shop's business condition.
Optionally, determine that the commodity of lead referral include into retail shop based on the attribute information of client：Obtain client on line
Attribute information and Recommendations the first mapping relations；It is determined according to the attribute information of client in the first mapping relations and retail shop
The commodity of lead referral into retail shop.
Optionally, determine that the commodity of lead referral include into retail shop based on the attribute information of client：According to objective in retail shop
Consumer record determines commercial product recommending list under line under the line at family；Determine that with client in retail shop, there is phase according to the first mapping relations
Commercial product recommending list on the line recommended with the Xian Shang customers of attribute information；To commercial product recommending on commercial product recommending list under line and line
Commodity in list carry out comprehensive score；The commodity of the lead referral into retail shop are determined according to commodity comprehensive score.
Optionally, this method further includes：According to consumer record under the line of client in retail shop, the related quotient for having purchased commodity is determined
Product；Using dependent merchandise as the commodity of the lead referral into retail shop.
Optionally, this method further includes：Identify the honored guest VIP client in face image data；The attribute of VIP client is believed
Breath is stored in retail shop's VIP attribute database.
Optionally, this method further includes：Based on retail shop's salesman's image data base, salesman's figure in face image data is rejected
As data.
According to another aspect of the present disclosure, it is also proposed that a kind of data analysis system, including：Face datection unit, for pair
The video data of acquisition carries out Face datection and obtains face image data；Attributive analysis unit, for face image data into
Pedestrian's face attributive analysis obtains the attribute information of client in retail shop；Commercial product recommending unit, it is true for the attribute information based on client
Orient the commodity of lead referral in retail shop.
Optionally, which further includes：Expression Recognition unit obtains visitor for carrying out Expression Recognition to face image data
Family expression information；Smile value determination unit, for determining client's smile value according to client's expression information；Satisfaction feedback unit,
For the corresponding relationship according to client's smile value and satisfaction, the shopping satisfaction of client in retail shop is determined.
Optionally, which further includes：Guest flow statistics unit, for carrying out pedestrian detection analysis to face image data
Obtain volume of the flow of passengers data；Volume of the flow of passengers analytical unit, for by retail shop's transaction data and turnover data at least one of with visitor
Data on flows is analyzed, and determines retail shop's business condition.
Optionally, the first mapping that commercial product recommending unit is also used to obtain the attribute information and Recommendations of client on line is closed
System determines the commodity of the lead referral into retail shop according to the attribute information of client in the first mapping relations and retail shop.
Optionally, commercial product recommending unit is also used to consumer record under the line according to client in retail shop and determines commercial product recommending under line
List is determined according to the first mapping relations on the line that there are the Xian Shang customers of same alike result information to recommend with client in retail shop
Commercial product recommending list carries out comprehensive score to the commodity in commercial product recommending list on commercial product recommending list under line and line, according to quotient
Product comprehensive score determines the commodity of the lead referral into retail shop.
Optionally, commercial product recommending unit is also used to consumer record under the line according to client in retail shop, and commodity have been purchased in determination
Dependent merchandise, using dependent merchandise as the commodity of the lead referral into retail shop.
Optionally, which further includes：VIP client's recognition unit, the honored guest VIP in face image data is objective for identification
Family；Attribute storage unit, for the attribute information of VIP client to be stored in retail shop's VIP attribute database.
Optionally, which further includes：Data culling unit rejects face for being based on retail shop salesman image data base
Salesman's image data in image data.
According to another aspect of the present disclosure, it is also proposed that a kind of data analysis system, including：Memory；And it is coupled to and deposits
The processor of reservoir, processor are configured as the data analysing method for example above-mentioned based on the instruction execution for being stored in memory.
According to another aspect of the present disclosure, it is also proposed that a kind of computer readable storage medium is stored thereon with computer journey
The step of sequence instruction, which realizes above-mentioned data analysing method when being executed by processor.
Compared with prior art, the embodiment of the present disclosure analyzes the attribute information for determining client in retail shop by face character,
And the commodity of the lead referral into retail shop are determined based on the attribute information of client, improve the accuracy of commercial product recommending.
By the detailed description referring to the drawings to the exemplary embodiment of the disclosure, the other feature of the disclosure and its
Advantage will become apparent.
Detailed description of the invention
The attached drawing for constituting part of specification describes embodiment of the disclosure, and together with the description for solving
Release the principle of the disclosure.
The disclosure can be more clearly understood according to following detailed description referring to attached drawing, wherein：
Fig. 1 is the flow diagram of one embodiment of disclosure data analysing method.
Fig. 2 is the flow diagram of another embodiment of disclosure data analysing method.
Fig. 3 is the flow diagram of the further embodiment of disclosure data analysing method.
Fig. 4 is the flow diagram of another embodiment of disclosure data analysing method.
Fig. 5 is the flow diagram of another embodiment of disclosure data analysing method.
Fig. 6 is the flow diagram of another embodiment of disclosure data analysing method.
Fig. 7 is the structural schematic diagram of one embodiment of disclosure data analysis system.
Fig. 8 is the structural schematic diagram of another embodiment of disclosure data analysis system.
Fig. 9 is the structural schematic diagram of the further embodiment of disclosure data analysis system.
Figure 10 is the structural schematic diagram of another embodiment of disclosure data analysis system.
Figure 11 is the structural schematic diagram of another embodiment of disclosure data analysis system.
Figure 12 is the structural schematic diagram of another embodiment of disclosure data analysis system.
Figure 13 is the structural schematic diagram of another embodiment of disclosure data analysis system.
Specific embodiment
The various exemplary embodiments of the disclosure are described in detail now with reference to attached drawing.It should be noted that：Unless in addition having
Body explanation, the unlimited system of component and the positioned opposite of step, numerical expression and the numerical value otherwise illustrated in these embodiments is originally
Scope of disclosure.
Simultaneously, it should be appreciated that for ease of description, the size of various pieces shown in attached drawing is not according to reality
Proportionate relationship draw.
Be to the description only actually of at least one exemplary embodiment below it is illustrative, never as to the disclosure
And its application or any restrictions used.
Technology, method and apparatus known to person of ordinary skill in the relevant may be not discussed in detail, but suitable
In the case of, the technology, method and apparatus should be considered as authorizing part of specification.
It is shown here and discuss all examples in, any occurrence should be construed as merely illustratively, without
It is as limitation.Therefore, the other examples of exemplary embodiment can have different values.
It should be noted that：Similar label and letter indicate similar terms in following attached drawing, therefore, once a certain Xiang Yi
It is defined in a attached drawing, then in subsequent attached drawing does not need that it is further discussed.
For the purposes, technical schemes and advantages of the disclosure are more clearly understood, below in conjunction with specific embodiment, and reference
The disclosure is further described in attached drawing.
Fig. 1 is the flow diagram of one embodiment of disclosure data analysing method.
In step 110, Face datection is carried out to the video data of acquisition and obtains face image data.Wherein it is possible in quotient
The entrance of paving or internal installation camera, can acquire the video data comprising client by camera, to video data into
Row Face datection, available face image data.
In step 120, the attribute information that face character analyzes to obtain client in retail shop is carried out to face image data.Example
Such as, it can analyze to obtain the gender of client by face character analytical technology, the age, expression, race, face value, whether wear eye
Eyeball, sunglasses, the attribute informations such as whether wear moustache.
In step 130, the commodity of the lead referral into retail shop are determined based on the attribute information of client.For example, for wearing ink
The client of mirror can recommend the moulding accessories such as sunglasses that can repair Hu knife etc. to its recommendation for the client to wear moustache to it.Really
Orient lead referral commodity after, recommendation information can be shown on retail shop's display screen, so as to retail shop attendant to visitor
Family carries out commercial product recommending.
In this embodiment, the attribute information for determining client in retail shop, and the category based on client are analyzed by face character
Property information determine into retail shop lead referral commodity, improve the accuracy of commercial product recommending.
Fig. 2 is the flow diagram of another embodiment of disclosure data analysing method.
In step 210, Face datection is carried out to the video data of acquisition and obtains face image data.
In step 220, the attribute information that face character analyzes to obtain client in retail shop is carried out to face image data.
In step 230, the attribute information of client and the first mapping relations of Recommendations on line are obtained.Wherein it is possible to logical
Cross database on line and obtain online trading information, and according to gender, the age, expression, race, face value, whether wear eyes, ink
Client on line is divided into different customers by mirror, the attribute informations such as whether wear moustache, and the consumer record of client on bonding wire can
To determine the commodity recommended to the customers, and then it can determine that the mapping of the attribute information and Recommendations of client on line is closed
System.
In step 240, the lead referral into retail shop is determined according to the attribute information of client in the first mapping relations and retail shop
Commodity.For example, by being recommended to the merchandise news that there are the Xian Shang customers of same alike result information to recommend with client in retail shop
Client in retail shop.
In this embodiment, by the attribute information combination online trading data of client in retail shop, can more accurately to
The suitable commodity of lead referral under line improve Customer Shopping experience.
Fig. 3 is the flow diagram of the further embodiment of disclosure data analysing method.
In step 310, Face datection is carried out to the video data of acquisition and obtains face image data.
In step 320, the attribute information that face character analyzes to obtain client in retail shop is carried out to face image data.Its
In, which can be VIP (Very Important People, honored guest) client, after obtaining face image data, identification
VIP client in the face image data.The attribute information of VIP client is stored in retail shop's VIP attribute database, so as to subsequent
Retail shop promotes client's consumption experience to VIP customization service plan.
In step 330, commercial product recommending list under line is determined according to consumer record under the line of client in retail shop.It wherein, if should
Client is consumed in the retail shop, can obtain consumer record under the line of the client, according under Client line in consumer record it is objective
The merchandise news that family has been bought determines suitable Recommendations information using proposed algorithm.
In step 340, the visitor on the line with client in retail shop with same alike result information is determined according to the first mapping relations
Commercial product recommending list on the line that family group recommends.For example, the Xian Shang customers that there is same alike result information with client in retail shop are found,
Then according to the first mapping relations of the attribute information of client on line and Recommendations, the commodity that will recommend to the Xian Shang customers
Recommendation list output.
In step 350, comprehensive score is carried out to the commodity in commercial product recommending list on commercial product recommending list under line and line.Example
Such as, for commercial product recommending list on line, comment of the client to commodity is checked, overall merit is higher, and commodity score is higher；For line
Upper commercial product recommending list checks repurchase rate, satisfaction of commodity etc., obtains the scoring of commodity, then carries out synthesis to commodity and comments
Point.
In step 360, the commodity of the lead referral into retail shop are determined according to commodity comprehensive score.For example, the highest that will score
Preceding several commodity be shown to the display in shop, or shop attendant is transmitted directly to, so as to lead referral commodity.
In this embodiment, the attribute information of client in retail shop and consumer record in consumer record under line and line are mutually tied
It closes, determines the commodity of the lead referral into retail shop, can be improved the accuracy of commercial product recommending.In addition, it is directed to VIP client, it can be with
The attribute information of the VIP client is stored in retail shop's VIP attribute database, so that subsequent retail shop is to VIP customization service side
Case promotes client's consumption experience.
, can also be according to consumer record under the line of client in retail shop in another embodiment of the disclosure, determination has been purchased
The dependent merchandise of commodity, using dependent merchandise as the commodity of the lead referral into retail shop.For example, the visitor such as bull's machine
Family, the probability for buying the commodity such as earphone, mobile phone shell, tempering film again is larger, can be by commodity such as earphone, mobile phone shell, tempering films
As the dependent merchandise for having purchased commodity.For buying the client of host computer, the commodity such as display, keyboard, mouse are bought again
Probability is larger, can be using commodity such as display, keyboard, mouses as the dependent merchandise for having purchased commodity.
In this embodiment, client in retail shop has been purchased to the related products recommendation of commodity to client, has improved commercial product recommending
Accuracy rate, promoted retail shop sell possibility.
Fig. 4 is the flow diagram of another embodiment of disclosure data analysing method.
In step 420, Face datection is carried out to the video data of acquisition and obtains face image data.
In step 430, Expression Recognition is carried out to face image data and obtains client's expression information.Such as the face according to client
Portion's expression shape change and human eye variation etc. determine client's expression information.
In step 430, client's smile value is determined according to client's expression information.For example, client by smiling face to indignation or by
Attention focuses on the corresponding different smile values such as dispersion attention.For example, client is very happy, client's smile value, which can be set, is
100, client is very angry, and it is 0 etc. that client's smile value, which can be set,.
In step 440, according to the corresponding relationship of client's smile value and satisfaction, determine that the shopping of client in retail shop is satisfied
Degree, to optimize retail shop's service quality according to Customer Shopping satisfaction.Wherein it is possible to which smile value to be divided into different sections, example
Such as, 8 0-100,60-80,40-60,0-40 etc., wherein 80-100 correspondence is felt quite pleased, the corresponding satisfaction of 60-80, and 40-60 is corresponding not
Satisfied, 0-40 correspondence is very dissatisfied.It will be understood by those of skill in the art that be only used for illustrating herein, it can be according to practical feelings
Condition is arranged different smile values and corresponds to different satisfactions.
In the embodiment, customer satisfaction data library can be recorded in the shopping satisfaction of client, so as to retail shop according to
Shopping satisfaction information makes Optimal improvements to follow-up sales means, or retrains to attendant, quotient under increase line
Integrity service quality is spread, Customer Shopping experience is promoted.
Fig. 5 is the flow diagram of another embodiment of disclosure data analysing method.
In step 510, Face datection is carried out to the video data of acquisition and obtains face image data.
In general headquarters 520, pedestrian detection is carried out to face image data and analyzes to obtain volume of the flow of passengers data.For example, by face figure
As data input deep learning network, pedestrian detection frame, the corresponding frame of each pedestrian are obtained, wherein the i.e. corresponding number of frame number.
In one embodiment, in order to avoid the quantity of error statistics retail shop interior employee, face number is constructed for employee
It is then rejected according to library when counting passenger flow by salesman's automatic identification, is not involved in the statistics of quantity.
In step 530, at least one in retail shop's transaction data and turnover data is analyzed with volume of the flow of passengers data,
Retail shop's business condition is determined, to carry out marketing optimization according to retail shop's business condition.For example, if certain day volume of the flow of passengers occur abruptly increase or
When die-offing, focus incident, commodity sales promotion and passenger flow on the day of selective analysis in shop are poured into.Guidance businessman holds profitable
Activity, avoid negative benefit event, seek more bus's flow and interests for retail shop.
In this embodiment, volume of the flow of passengers data are made into analysis to system in conjunction with daily sales, day trade transaction data are worked as
Feedback, makes businessman make more preferable decision, can seek more bus's flow and interests for retail shop.
Fig. 6 is the flow diagram of another embodiment of disclosure data analysing method.
In step 610, the picture number in the video data and retail shop's salesman's image data base of retail shop's camera acquisition is obtained
According to.
In step 620, Face datection is carried out to video data and obtains face image data, and rejected in face image data
Salesman's image data.
If subsequent execution commercial product recommending, thens follow the steps 630, if analysis Customer Shopping satisfaction, thens follow the steps 640,
If counting to the volume of the flow of passengers, 650 are thened follow the steps.
In step 630, VIP client is identified.Wherein it is possible to the VIP client image information that shop saves first is obtained, then base
The VIP customer data in face image data is identified in face recognition technology.
In step 631, face character is carried out to the VIP customer image data of acquisition and analyzes to obtain VIP client in retail shop
Attribute information.
In step 632, the attribute information of VIP client is combined into determination with on-line off-line transaction data and is pushed away to VIP client
The commodity recommended, and retail shop's VIP attribute database is recorded in the attribute information of VIP client, to the category of the VIP client in database
Property information carry out continuous updating.
In step 640, Expression Recognition is carried out to the face image data for rejecting salesman's image and obtains client's expression information.
In step 641, client's smile value is determined according to client's expression information.
In step 642, according to the corresponding relationship of client's smile value and satisfaction, determine that the shopping of client in retail shop is satisfied
Degree.
In step 650, pedestrian detection is carried out to the face image data for rejecting salesman's image and analyzes to obtain volume of the flow of passengers data.
It in step 651, will be analyzed in retail shop's transaction data and turnover data with volume of the flow of passengers data, determine that retail shop is sought
Industry situation.
In this embodiment, pedestrian detection, facial expression recognition, Face datection, recognition of face and face character point are utilized
The technologies such as analysis, analyze video data, to carry out VIP client's identification, accurate commercial product recommending, customer purchase Satisfaction index
Analysis and passenger flow statistics etc., and retail shop is fed back information to, it so that retail shop carries out marketing decision-making, improves service quality, promotes visitor
Family shopping experience.
Fig. 7 is the structural schematic diagram of one embodiment of disclosure data analysis system.The data analysis system includes people
Face detection unit 710, attributive analysis unit 720 and commercial product recommending unit 730.
Face datection unit 710 is used to carry out Face datection to the video data of acquisition to obtain face image data.Wherein,
The video data comprising client, face can be acquired by camera in the entrance or internal installation camera of retail shop
Detection unit 710 carries out Face datection, available face image data to video data.
Attributive analysis unit 720 is used to carry out the attribute that face character analyzes to obtain client in retail shop to face image data
Information.For example, by face character analytical technology can analyze to obtain the gender of client, the age, expression, race, face value, whether
With eyes, sunglasses, the attribute informations such as whether wear moustache.
Commercial product recommending unit 730 is used to determine the commodity of the lead referral into retail shop based on the attribute information of client.For example,
For the client of wear dark glasses, the moulding accessories such as sunglasses can be recommended to it, for the client to wear moustache, can be repaired recklessly to its recommendation
Knife etc..It determines to after the commodity of lead referral, recommendation information can be shown on retail shop's display screen, so as to the service people of retail shop
Member carries out commercial product recommending to client.
In this embodiment, the attribute information for determining client in retail shop, and the category based on client are analyzed by face character
Property information determine into retail shop lead referral commodity, improve the accuracy of commercial product recommending.
In another embodiment of the disclosure, commercial product recommending unit 730 is also used to obtain the attribute information of client on line
With the first mapping relations of Recommendations, determined according to the attribute information of client in the first mapping relations and retail shop objective into retail shop
The commodity that family is recommended.Wherein it is possible to by with client in retail shop have same alike result information Xian Shang customers recommend commodity
Information recommendation is to the client in retail shop.
In this embodiment, by the attribute information combination online trading data of client in retail shop, can more accurately to
The suitable commodity of lead referral under line improve Customer Shopping experience.
In another embodiment of the disclosure, commercial product recommending unit 730 is also used to disappear under the line according to client in retail shop
Take record and determine commercial product recommending list under line, determines that with client in retail shop, there is same alike result information according to the first mapping relations
Xian Shang customers recommend line on commercial product recommending list, to the quotient in commercial product recommending list on commercial product recommending list under line and line
Product carry out comprehensive score, and the commodity of the lead referral into retail shop are determined according to commodity comprehensive score.For example, being pushed away for commodity on line
List is recommended, checks comment of the client to commodity, overall merit is higher, and commodity score is higher；For commercial product recommending list on line, look into
Repurchase rate, the satisfaction etc. for seeing commodity, obtain the scoring of commodity, then carry out comprehensive score to commodity.To score it is highest before
Several commodity are shown to the display in shop, or are transmitted directly to shop attendant, so as to lead referral commodity.
In this embodiment, the attribute information of client in retail shop and consumer record in consumer record under line and line are mutually tied
It closes, determines the commodity of the lead referral into retail shop, can be improved the accuracy of commercial product recommending.
It, can be as shown in figure 8, including VIP client's recognition unit 810 and attribute in another embodiment of the disclosure
Storage unit 820.
The VIP client in face image data, attribute storage unit 820 are used for VIP client's recognition unit 810 for identification
The attribute information of VIP client is stored in retail shop's VIP attribute database, so that subsequent retail shop is to VIP customization service plan,
Promote client's consumption experience.For example, commercial product recommending unit 730 is when using attribute information Recommendations, for VIP client, root
According to the attribute information of VIP client, and on-line off-line sales data is combined, determines the merchandise news recommended.
In another embodiment of the disclosure, commercial product recommending unit 730 is also used to disappear under the line according to client in retail shop
Take record, the dependent merchandise for having purchased commodity is determined, using dependent merchandise as the commodity of the lead referral into retail shop.For example, such as
For the client of bull's machine, the probability for buying the commodity such as earphone, mobile phone shell, tempering film again is larger, can be by earphone, mobile phone
The commodity such as shell, tempering film are as the dependent merchandise for having purchased commodity.For buying the client of host computer, display, key are bought again
The probability of the commodity such as disk, mouse is larger, can be using commodity such as display, keyboard, mouses as the dependent merchandise for having purchased commodity.
In this embodiment, client in retail shop has been purchased to the related products recommendation of commodity to client, has improved commercial product recommending
Accuracy rate, promoted retail shop sell possibility.
Fig. 9 is the structural schematic diagram of the further embodiment of disclosure data analysis system.The data analysis system includes
Face datection unit 910, Expression Recognition unit 920, smile value determination unit 930 and satisfaction feedback unit 940.
Face datection unit 910 is used to carry out Face datection to the video data of acquisition to obtain face image data.
Expression Recognition unit 920 is used to carry out Expression Recognition to face image data to obtain client's expression information.Such as root
Client's expression information is determined according to the countenance variation and human eye variation etc. of client.
Smile value determination unit 930 is used to determine client's smile value according to client's expression information.For example, client by smiling face to
Indignation focuses on the corresponding different smile values such as dispersion attention by attention.Such as, client is very happy, and it is micro- that client can be set
Laughing at value is 100, and client is very angry, and it is 0 etc. that client's smile value, which can be set,.
Satisfaction feedback unit 940 is used for the corresponding relationship according to client's smile value and satisfaction, determines client in retail shop
Shopping satisfaction, so as to according to Customer Shopping satisfaction optimize retail shop's service quality.Wherein it is possible to which smile value is divided into difference
Section, for example, 8 0-100,60-80,40-60,0-40 etc., wherein 80-100 correspondence is felt quite pleased, the corresponding satisfaction of 60-80,
40-60 is corresponding dissatisfied, and 0-40 correspondence is very dissatisfied.It, can be with it will be understood by those of skill in the art that be only used for illustrating herein
Different smile values is set according to the actual situation and corresponds to different satisfactions.
In the embodiment, customer satisfaction data library can be recorded in the shopping satisfaction of client, so as to retail shop according to
Shopping satisfaction information makes Optimal improvements to follow-up sales means, or retrains to attendant, quotient under increase line
Integrity service quality is spread, Customer Shopping experience is promoted.
Figure 10 is the structural schematic diagram of another embodiment of disclosure data analysis system.The data analysis system includes
Face datection unit 1010, guest flow statistics unit 1020 and volume of the flow of passengers analytical unit 1030.
Face datection unit 1010 is used to carry out Face datection to the video data of acquisition to obtain face image data.
Guest flow statistics unit 1020 is used to carry out pedestrian detection to face image data to analyze to obtain volume of the flow of passengers data.Example
Such as, face image data is inputted into deep learning network, obtains pedestrian detection frame, the corresponding frame of each pedestrian, wherein frame number
Correspond to number.
Volume of the flow of passengers analytical unit 1030 be used for by retail shop's transaction data and turnover data at least one of and the volume of the flow of passengers
Data are analyzed, and determine retail shop's business condition.For example, if when there is abruptly increase or die-offs in certain day volume of the flow of passengers, on the day of selective analysis
Focus incident, commodity sales promotion and passenger flow in shop are poured into.Guidance businessman holds profitable activity, avoids negative benefit thing
Part seeks more bus's flow and interests for retail shop.
It in one embodiment, can also include that data are rejected in order to avoid the quantity of error statistics retail shop interior employee
Unit 1011 rejects salesman's image data in face image data for being based on retail shop salesman image data base.
In this embodiment, volume of the flow of passengers data are made into analysis to system in conjunction with daily sales, day trade transaction data are worked as
Feedback, makes businessman make more preferable decision, can seek more bus's flow and interests for retail shop.In addition, due to by retail shop's salesman's number
According to deletion, it is thus possible to enough improve the accuracy of data statistics, analysis.
Figure 11 is the structural schematic diagram of another embodiment of disclosure data analysis system.The system includes Face datection
Unit 1110, data culling unit 1120, VIP client's recognition unit 1130, attributive analysis unit 1131, commercial product recommending unit
1132, attribute storage unit 1133, Expression Recognition unit 1140, smile value determination unit 1141, satisfaction feedback unit 1142,
Guest flow statistics unit 1150 and volume of the flow of passengers analytical unit 1151.
Face datection unit 1110 is used to carry out Face datection to video data to obtain face image data.
Data culling unit 1120 is used to reject salesman's image data in face image data.
The VIP client for identification of VIP client's recognition unit 1130.Wherein it is possible to first obtain VIP client's figure that shop saves
As information, it is then based on face recognition technology and identifies VIP customer data in face image data.
Attributive analysis unit 1131 is used to carry out face character to the VIP customer image data of acquisition to analyze to obtain in retail shop
The attribute information of VIP client.
Commercial product recommending unit 1132 be used to on-line off-line transaction data combine the attribute information of VIP client determination to
The commodity of VIP lead referral.
Attribute storage unit 1133 is used to the attribute information of VIP client retail shop's VIP attribute database is recorded.
Expression Recognition unit 1140 is used to carry out Expression Recognition to the face image data for rejecting salesman's image to obtain client
Expression information.
Smile value determination unit 1141 is used to determine client's smile value according to client's expression information.
Satisfaction feedback unit 1142 is used for the corresponding relationship according to client's smile value and satisfaction, determines client in retail shop
Shopping satisfaction.
Guest flow statistics unit 1150 is used to carry out pedestrian detection to the face image data for rejecting salesman's image to analyze
To volume of the flow of passengers data.
Volume of the flow of passengers analytical unit 1151 in retail shop's transaction data and turnover data with volume of the flow of passengers data for will be divided
Analysis, determines retail shop's business condition.
In this embodiment, pedestrian detection, facial expression recognition, Face datection, recognition of face and face character point are utilized
The technologies such as analysis, analyze video data, to carry out VIP client's identification, accurate commercial product recommending, customer purchase Satisfaction index
Analysis and passenger flow statistics etc., and retail shop is fed back information to, it so that retail shop carries out marketing decision-making, improves service quality, promotes visitor
Family shopping experience.
Figure 12 is the structural schematic diagram of another embodiment of disclosure data analysis system.The system includes memory
1210 and processor 1220, wherein：
Memory 1210 can be disk, flash memory or other any non-volatile memory mediums.Memory is for storing figure
Instruction in embodiment corresponding to 1-6.Processor 1220 is coupled to memory 1210, can be used as one or more integrated circuits
Implement, such as microprocessor or microcontroller.The processor 1220 is for executing the instruction stored in memory.
In one embodiment, can also as shown in figure 13, which includes memory 1310 and processor 1320.
Processor 1320 is coupled to memory 1310 by BUS bus 1330.The system 1300 can also be connected by memory interface 1340
External memory 650 is connected to call external data, network or in addition can also be connected to by network interface 1360
One computer system (not shown), no longer describes in detail herein.
In this embodiment, it is instructed by memory stores data, then above-metioned instruction is handled by processor, mentioned for client
For personalized service in retail shop, the accuracy of commercial product recommending is improved, is improved service quality convenient for retail shop, promotes Customer Shopping body
It tests.
In another embodiment, a kind of computer readable storage medium, is stored thereon with computer program instructions, this refers to
The step of order realizes the method in embodiment corresponding to Fig. 1-6 when being executed by processor.It should be understood by those skilled in the art that,
Embodiment of the disclosure can provide as method, apparatus or computer program product.Therefore, complete hardware reality can be used in the disclosure
Apply the form of example, complete software embodiment or embodiment combining software and hardware aspects.Moreover, the disclosure can be used one
It is a or it is multiple wherein include computer usable program code computer can with non-transient storage medium (including but not limited to
Magnetic disk storage, CD-ROM, optical memory etc.) on the form of computer program product implemented.
The disclosure is reference according to the method for the embodiment of the present disclosure, the flow chart of equipment (system) and computer program product
And/or block diagram describes.It should be understood that each process in flowchart and/or the block diagram can be realized by computer program instructions
And/or the combination of the process and/or box in box and flowchart and/or the block diagram.It can provide these computer programs to refer to
Enable the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to generate
One machine so that by the instruction that the processor of computer or other programmable data processing devices executes generate for realizing
The device for the function of being specified in one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
So far, the disclosure is described in detail.In order to avoid covering the design of the disclosure, it is public that this field institute is not described
The some details known.Those skilled in the art as described above, completely it can be appreciated how implementing technology disclosed herein
Scheme.
Although being described in detail by some specific embodiments of the example to the disclosure, the skill of this field
Art personnel it should be understood that above example merely to be illustrated, rather than in order to limit the scope of the present disclosure.The skill of this field
Art personnel are it should be understood that can modify to above embodiments in the case where not departing from the scope of the present disclosure and spirit.This public affairs
The range opened is defined by the following claims.
Claims (18)
1. a kind of data analysing method, including：
Face datection is carried out to the video data of acquisition and obtains face image data；
The attribute information that face character analyzes to obtain client in retail shop is carried out to the face image data；
The commodity of the lead referral into retail shop are determined based on the attribute information of client.
2. data analysing method according to claim 1, further includes：
Expression Recognition is carried out to the face image data and obtains client's expression information；
Client's smile value is determined according to client's expression information；
According to the corresponding relationship of client's smile value and satisfaction, the shopping satisfaction of client in retail shop is determined.
3. data analysing method according to claim 1, further includes：
Pedestrian detection is carried out to the face image data to analyze to obtain volume of the flow of passengers data；
At least one in retail shop's transaction data and turnover data is analyzed with volume of the flow of passengers data, determines retail shop's business shape
Condition.
4. data analysing method according to claim 1 determines the lead referral into retail shop based on the attribute information of client
Commodity include：
Obtain the attribute information of client and the first mapping relations of Recommendations on line；
The commodity of the lead referral into retail shop are determined according to the attribute information of client in first mapping relations and retail shop.
5. data analysing method according to claim 4, wherein
Commercial product recommending list under line is determined according to consumer record under the line of client in retail shop；
It is determined according to first mapping relations to the Xian Shang customers recommendation with client in retail shop with same alike result information
Commercial product recommending list on line；
Comprehensive score is carried out to the commodity in commercial product recommending list on commercial product recommending list under the line and the line；
The commodity of the lead referral into retail shop are determined according to commodity comprehensive score.
6. data analysing method according to claim 5, further includes：
According to consumer record under the line of client in retail shop, the dependent merchandise for having purchased commodity is determined；
Using the dependent merchandise as the commodity of the lead referral into retail shop.
7. data analysing method according to claim 1, further includes：
Identify the honored guest VIP client in the face image data；
The attribute information of the VIP client is stored in retail shop's VIP attribute database.
8. -7 any data analysing method according to claim 1, further includes：
Based on retail shop's salesman's image data base, salesman's image data in the face image data is rejected.
9. a kind of data analysis system, including：
Face datection unit obtains face image data for carrying out Face datection to the video data of acquisition；
Attributive analysis unit, for carrying out the attribute letter that face character analyzes to obtain client in retail shop to the face image data
Breath；
Commercial product recommending unit determines the commodity of the lead referral into retail shop for the attribute information based on client.
10. data analysis system according to claim 9, further includes：
Expression Recognition unit obtains client's expression information for carrying out Expression Recognition to the face image data；
Smile value determination unit, for determining client's smile value according to client's expression information；
Satisfaction feedback unit determines the shopping of client in retail shop for the corresponding relationship according to client's smile value and satisfaction
Satisfaction.
11. data analysis system according to claim 9, further includes：
Guest flow statistics unit is analyzed to obtain volume of the flow of passengers data for carrying out pedestrian detection to the face image data；
Volume of the flow of passengers analytical unit, for carrying out at least one in retail shop's transaction data and turnover data with volume of the flow of passengers data
Analysis, determines retail shop's business condition.
12. data analysis system according to claim 9, wherein
The commercial product recommending unit is also used to obtain the first mapping relations of the attribute information and Recommendations of client on line, according to
The attribute information of client determines the commodity of the lead referral into retail shop in first mapping relations and retail shop.
13. data analysis system according to claim 12, wherein
The commercial product recommending unit is also used to consumer record under the line according to client in retail shop and determines commercial product recommending list under line, root
The quotient on the line that there are the Xian Shang customers of same alike result information to recommend with client in retail shop is determined according to first mapping relations
Product recommendation list carries out comprehensive score to the commodity in commercial product recommending list on commercial product recommending list under the line and the line,
The commodity of the lead referral into retail shop are determined according to commodity comprehensive score.
14. data analysis system according to claim 13, wherein
The commercial product recommending unit is also used to consumer record under the line according to client in retail shop, determines the related quotient for having purchased commodity
Product, using the dependent merchandise as the commodity of the lead referral into retail shop.
15. data analysis system according to claim 9, further includes：
VIP client's recognition unit, for identification the honored guest VIP client in the face image data；
Attribute storage unit, for the attribute information of the VIP client to be stored in retail shop's VIP attribute database.
16. further including according to any data analysis system of claim 9-15：
Data culling unit rejects salesman's image in the face image data for being based on retail shop salesman image data base
Data.
17. a kind of data analysis system, including：
Memory；And
It is coupled to the processor of the memory, the processor is configured to based on the instruction execution for being stored in the memory
Data analysing method as claimed in any one of claims 1 to 8.
18. a kind of computer readable storage medium, is stored thereon with computer program instructions, real when which is executed by processor
The step of existing claim 1 to 8 described in any item data analysing methods.
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