TWI629654B - Wisdom door city welcome product recommendation system and method thereof - Google Patents

Abstract

The invention provides a smart door market welcome product recommendation system. The foregoing includes a customer identification module, a biometric module, an identity feature integration module, a data analysis module, and a marketing module. The customer identification module communicates with the handheld device of the client to obtain the identity information of the customer. The biometric module measures and obtains biometric information of the client. The Identity Integration Module acquires and integrates identity information and biometric information to provide identity integration information. After obtaining the identity feature integration information, the data analysis module queries and analyzes the corresponding customer data to provide the customer interest service content. The aforementioned marketing module pushes the customer interest service content to the handheld device or pushes it to the display device located in the store.

Description

Wisdom door city welcome product recommendation system and method thereof

The invention relates to a welcome product recommendation system and a method thereof, in particular to a welcome product recommendation system and a method thereof applied to a smart store.

The traditional store service is a passive service, and this method relies on the multi-party inquiry of the experienced store personnel to further understand the customer identity and find a marketing plan suitable for the customer. Therefore, when the store personnel experience is insufficient, It is not possible to immediately recommend a suitable service plan to the customer.

In addition, when the number of customers in the store is large, because the store staff has not had enough time to ask and discuss with the customers, it is impossible to find out the service plan that meets the customer in an instant, so that the marketing success rate of the store service in the popular time period is greatly increased. Impact.

In summary, how to provide a solution to the aforementioned problems is a technical problem that needs to be solved in the field.

In order to solve the problems disclosed above, the object of the present invention is to provide a smart door market welcome product recommendation technical solution.

In order to achieve the above object, the present invention proposes a smart door market welcome product recommendation system. The foregoing includes a customer identification module, a biometric module, an identity feature integration module, a data analysis module, and a marketing module. The aforementioned customer identification module and client handheld device Make a communication connection to get the identity information of the customer. The aforementioned biometric module measures and obtains biometric information of the client. The foregoing identity feature integration module connects the customer identity module and the biometric module, and acquires and integrates identity information and biometric information to provide an identity feature integration information. The data analysis module is connected to the identity feature integration module and obtains identity feature integration information. The data analysis module integrates information according to the identity feature to query and analyze the corresponding customer data, thereby providing a customer interest service content. The aforementioned marketing module is connected to the data analysis module, and the customer interest service content is pushed to the handheld device or pushed to the display device located in the store.

In order to achieve the above object, the present invention further provides a smart door market welcome product recommendation method. The foregoing method is applied to a smart door welcoming product recommendation system and includes the following steps: first, a communication connection with a client handheld device to obtain the identity information of the client, and measuring and obtaining the biometric information of the client, Integrate identity information and biometric information to provide an identity integration information. Then, the information is integrated according to the identity feature to query and analyze the corresponding customer data, thereby providing a customer interest service content. The customer interest service content is pushed to the handheld device or pushed to the display device located in the store.

In summary, the smart product welcoming product recommendation system and method of the present invention are effective when analyzing the customer's identity information and biometric information in the store environment, and providing the customer interest service content according to the customer's demand. Improve the quality of the store service.

1‧‧‧Smart Door City Welcome Product Recommendation System

11‧‧‧Customer Identification Module

12‧‧‧Biometric Module

13‧‧‧ Identity Feature Integration Module

14‧‧‧Data Analysis Module

15‧‧‧ Marketing Module

2‧‧‧Client

21‧‧‧Handheld devices

3‧‧‧Database

4‧‧‧ display device

FIG. 1 is a schematic block diagram of a system for a smart product welcome product recommendation system according to an embodiment of the present invention.

FIG. 2 is a schematic diagram of an implementation example of a smart product welcoming product recommendation system according to the present invention.

FIG. 3 is a flowchart of a method for recommending a welcome product of a smart door in another embodiment of the present invention.

The specific embodiments are described below to illustrate the embodiments of the invention, but are not intended to limit the scope of the invention.

Please refer to FIG. 1 , which is a system block diagram of a smart door city welcome product recommendation system 1 according to an embodiment of the present invention. The foregoing system includes a customer identification module 11, a biometric module 12, an identity feature integration module 13, a data analysis module 14, and a marketing module 15.

Please refer to FIG. 2 , which is a schematic diagram of an implementation example of the aforementioned smart product welcome product recommendation system 1 . The customer identity module 11 is in communication with the handheld device 21 of the client 2 to obtain identity information of the client. The biometric module 12 measures and obtains the biometric information of the client 2. The identity integration module 13 is connected to the customer identity module 11 and the biometric module 12 to obtain and integrate identity information and biometric information to provide an identity feature integration information. The data analysis module 14 is connected to the identity feature integration module 13 and obtains identity feature integration information, and integrates information according to the identity feature to query and analyze the corresponding customer profile to provide a customer interest service content. The marketing module 15 is connected to the data analysis module 14 and pushes the customer interest service content to the handheld device 21 or to the display device 4 located in the store. The foregoing identity feature integration module 13, data analysis module 14, and marketing module 15 are software modules that operate on one or more computer devices.

Please refer to FIG. 3 , which is a flowchart of a method for recommending a welcome product of Wisdom in another embodiment of the present invention. The foregoing method is applied to a smart product welcoming product recommendation system, and Includes the following steps:

S101: Communicate with a handheld device of the client to obtain identity information of the client.

S102: Measure and obtain biometric information of the client.

S103: Acquire and integrate identity information and biometric information to provide an identity feature integration information.

S104: Integrate information according to the identity feature to query and analyze the corresponding customer data, thereby providing a customer interest service content.

S105: Push the customer interest service content to the handheld device or push it to the display device located in the store.

In another embodiment, the biometric information further includes at least one of infrared feature information, weight sensing feature information, image information, and height information. In another embodiment, the customer identification module 11 obtains the door number information of the handheld device 21 to obtain the identity information.

In another embodiment, the foregoing customer data further includes at least one of customer gender information, customer grade information, customer appointment information, and customer tariff information. In another embodiment, the foregoing identity feature integration information further includes integrating time series biometric information.

The following invention is described in the first embodiment of the Wisdom City Welcome Product Recommendation System 1, but the second embodiment of the Wisdom City Welcome Product Recommendation method also has the same or similar technical features and functions. Please refer to FIG. 2 again, the aforementioned smart door welcoming product recommendation system 1 is applied to the telecom store (except for the application level), and the aforementioned biometric sensing module can be implemented by the following components: infrared light sensing component The image sensing component, the weight sensing component, the height sensing component, the operating instructions of each sensing component, and the operation modes are as follows: Infrared light sensing elements can be active or passive light sensing elements, active Infrared is added to the passive infrared system plus the principle of light reflection. At the same time, the detection of the distance is applied. Planck's "black body radiation law" is used. When the temperature of the object is higher than absolute zero (-273.15K Celsius), infrared radiation is generated. Energy, and the light-sensing element knows the sensing data of temperature and humidity by receiving and measuring the radiation light energy of the infrared light.

The image sensing component (for example: camera) can be located in the digital signage of the store, and the counter...where, when the store is equipped with a plurality of image sensing components, the image sensor can be used to simulate the human eye to the RGB red, green and blue primary colors. Respond, capture images in static or dynamic, and analyze images to make various judgments, such as face recognition and facial expression sentiment analysis, to view the facial expressions (feeling emotions) of the advertisement before the customer draws on the digital signage, and the counter staff and When the customer interacts, the customer's emotions can be known by analyzing the customer's image, and the interpretation results are transmitted back to the back-end identity integration module.

The weight sensing component is buried under the welcome carpet at the entrance of the smart door, and one or more gravity sensing panels are set up according to the requirements. The customer can enter the door with the infrared light sensor component. Currently, several customers enter the door and are at the customer. The customer's weight value is passed back to the back-end identity integration module 12 before the welcome carpet is completed.

The height sensing component (ranging component) is located at the gate of the entrance of the smart door, and is equipped with one or more height sensing components according to requirements. When the customer passes the door of the smart door, the current time of entering the door and the height value are It is passed back to the identity integration module 12 of the back end.

The customer identity module 11 can select the identity of the client 2 (for example, the mobile door number) by each type of wireless base station, and the implementation of the WI-FI base station or the GPRS base station can be selected in this embodiment. The frequency bands and communication protocol types used by the base stations are not limited to this.

WI-FI base station number detection component uses point-to-point wireless network docking, let The customer's handheld communication device uses a customized APP to automatically access the network, thereby knowing the user's identity and performing an indoor location within the communication range.

GPRS base station number detection component: utilizes the characteristics of GPRS Always Connect. The aforementioned GPRS network architecture is based on the existing GSM network architecture, and two new network device nodes are added, namely the Serving GPRS Support Node (SGSN) and the Gateway GPRS Support Node (GGSN). The SGSN and the GGSN have a packet processing and transmission function.

In order to perform the identity check, the customer identification module 11 queries the obtained action door number through the database 3 to verify the historical credit data of the customer and the personal identity data of the customer's main file.

In another embodiment, the foregoing identity feature integration module 13 may further include a biometric timing integration component and a software module of the client identity comparison component. The biometric timing integration component continuously receives the information returned by the biometric sensor and performs timing integration with the IP and gate number registration schedule in the base station network according to the customer entry time. The customer identity comparison component quickly integrates the customer's personalized feature information and passes it back to the backend data analysis module 14.

In another embodiment, the data analysis module 14 selects a software module further including a customer behavior analysis component and a customer behavior prediction component. The foregoing customer behavior analysis component receives the personal identification number (ID) obtained by the customer identification module, performs customer data analysis, and serializes the back-end database 3 through an automated analysis process (eg, customer database, customer service system database, account) Service system database, marketing activity database...etc.). The aforementioned customer behavior prediction component has built-in machine learning algorithms to analyze customer information within these systems, and to predict the customer's propensity to consume and list related consumer items.

In another embodiment, the marketing module 15 further includes a software module that includes an association rule engine component and a promotion solution calculation component. The association rule engine component receives the R result generated by the data analysis module 14. The sales plan calculation component establishes a product mix that is hot and recommended in the current season, and gives individual customers different marketing weight coefficients, so that they can quickly provide suitable services for customers.

Finally, the welcome interface of the smart product welcome product recommendation system 1 can be used to push the information recommended by the mobile terminal to the handheld device 21 of the client 2, or the display device 4 installed in the store, or the smart service robot of the store. .

After the customer enters the telecommunications market, the smart store welcome product recommendation system 1 first senses the personal biometric factor X set of the client 2 through the biometric module 12, where X={X1 height, X2 weight, X3 systolic blood pressure index, X4 diastolic blood pressure index}; X1, X2, X3, and X4 are detected by infrared light sensing elements, and X sets are obtained. At the same time, the customer identification module 11 is used to obtain the gate number factor Y set, wherein Y={Y1 action gate number}, and the acquisition of Y1 is obtained by the WI-FI base station and the GPRS base station installed by each smart store, and then The door number obtained by Y is sent to the database 3 for inquiry to determine whether the door number is the company customer of the telecommunication service provider, and the customer's relevant credit, consumption record and personal preference of the telecommunication service provider, and the result. Y';Y'={Y'1 is the company, Y'2 door number, Y'3 door date, Y'4 age, Y'5 gender}; then return this result Y' to 2 Customer identification module 11.

After obtaining the X set and the Y′ set, the X set and the Y′ set are simultaneously input into the identity feature integration module 13 for the association weighting operation, and the obtained identity comparison result is Z, Z is the customer attribute value set, Z={ Z1, Z2, Z3}; wherein Z1 customer health value is composed of BMI health value Z11 and blood pressure health value Z12; Z2 is the customer grade value, from customer grade value Z21, customer appointment value Z22, The customer's premium value is composed of Z23; Z3 is the customer's biometric value, which consists of the customer's age value Z31 and the customer's gender value Z32.

Then, the Z is sent to the data analysis module 14, and the data analysis module 14 obtains the customer attribute indicator set Z'. Through the customer behavior analysis component, the customer attribute value set is subjected to a series of index calculations, and the operation rules of each index are as follows:

Z'1: Health indicator, number of Z'1=(Z'11+Z'12)/Z’1 indicators

Z'2: customer indicator, Z'2=(Z'21+Z'22+Z'23)/Z'2 indicator quantity

Z'3: Biometric indicator, number of Z'3=(Z'31+Z'32)/Z'3 indicators

Then, the calculated result Z'={Z'1, Z'2, Z'3} is transmitted to the marketing module 15 for the promotion scheme recommendation operation, and the marketing module 15 to the database 3 retrieves the recommended promotion scheme system set. D={D1, D2, D3,..., DN}, where N is the system code, D1 is the health cloud management system, D2 is the value-added product system, D3 is the smart home product system..., DN is the Nth A discount system that can recommend a promotion plan; the marketing module 15 then goes to the database 3 to retrieve the weight of each system promotion offer scheme wD={wD1, wD2,..., wDN}, where wD1={wD11, wD12,,. .., wD1k}, where k is the kth discount product, through the promotional program computing components, get the relevant product information for this customer R={Z'1‧wD1,Z'1‧wD2,...,Z' 1‧wDN, ..., Z'3‧wDNk}, sorts all the operation results in the R set, and transmits the R set sort result to the welcome interface (the display device 4 that presents the result to the client).

Further, if a customer OO enters the market, the system 1 obtains the X set as 165, 80, 140, 98 through the biometric module 12. Then, the customer identification module 11 obtains the Y set as {098888888888}, and sends the Y set to the database 3 for query, and obtains Y' as {Y, 699, 15, 35, female} and sends it back to the customer identification module. Group 11. At this time, the X set and the Y′ set are regarded as the input of the identity feature integration module 13 , and the identity attribute value set Z={Z1, Z2, Z3} is collected through the identity feature integration module 13; wherein the Z1 customer health value is calculated. Customer health values require height, weight, blood pressure systolic pressure, and blood pressure diastolic pressure, so Z1 = {165, 80, 140, 98}, and Z2 is the customer pitch value.

Then calculate the customer pitch value requires customer level, customer appointment and customer tariff Value, so Z2={VIP2,15,699}; Z3 is the customer biometric value. Calculating the customer characteristic value requires the customer age value and customer gender value, so Z3={35, female}, send the Z set result to the data analysis module. In step 14, the data analysis module 14 obtains the customer attribute indicator set Z', and performs a series of index calculations on the customer attribute value set through the customer behavior analysis component, and the result of each index operation is as follows:

Z'1: Health indicator, Z'1=(Z'11+Z'12)/Z'1 indicator quantity = (0.43+1)/2=0.715

Because height = 160, weight = 80, Z11 = BMI = 31.5, w(z11) = 0.43, and Z'11 BMI health index = 0.43.

Because of diastolic blood pressure = 140, w(z12)=1 can be obtained, and the Z'12 blood pressure health index=1 is output.

Z'2: customer indicator, Z'2=(Z'21+Z'22+Z'23)/Z'2 indicator quantity = (0.75+0.67+0.25)/3=0.56

Since the customer grade value is VIP2, w(z21)=0.75 is obtained, and the Z'21 customer grade index=0.75 is output.

Since the customer's expected value is 15, you can get w(z22)=0.67, and output the Z'22 customer's date index. Mark = 0.67.

Since the customer's tariff is 699, w(z23) = 0.25 is available, and the Z'23 customer tariff indicator = 0.25 is output. Z'3: biometric indicator, Z'3 = (Z'31 + Z'32) / Z'3 index number = (0.67 + 0.7) / 2 = 0.685.

Since the customer's age is 35, w(z31) = 0.66 is available, and the Z'31 customer age indicator = 0.67 is output.

Because the gender of the customer is female, we can get w(z32)=0.7 and output Z'31 customer tariff index=0.7.

Then, the obtained result Z' is aggregated into {health index, customer index, biometric index}={0.715, 0.6, 0.685}, and transmitted to the marketing module 15 for the promotion scheme recommendation operation, the marketing module 15 to the database 3 Draw a system of recommended promotion schemes D={D1, D2, D3}, D1 is a healthy cloud management system, D2 is a value-added product system, D3 is a smart home product system; marketing module 15 goes to database 3 to retrieve each The system promotion discount scheme weight value wD={wD1, wD2, wD3}, wherein the health cloud management system promotion discount scheme weight value wD1={wD11, wD12}={0.8,0.2}, the value-added product system promotion discount scheme weight value wD2 ={wD21,wD22}={0.4,0.6}, smart family Product system promotion discount scheme weight value wD3={wD31, wD32}={0.1,0.9}, and calculate Z'={0.715,0.6,0.685} and wD={wD1,wD2,...,wDN} The result set R={Z'1‧wD11,Z'1‧wD12,Z'1‧wD21,Z'1‧wD22,Z'1‧w31,Z'1‧wD32,Z'2‧wD11,Z' 2‧wD12,Z'2‧wD21,Z'2‧wD22,Z'2‧w31,Z'2‧wD32,Z'3‧wD11,Z'3‧wD12,Z'3‧wD21,Z'3‧ wD22,Z'3‧w31,Z'3‧wD32}={0.572,0.143,0.2848,0.429,0.0715,0.6435,0.48,0.12,0.24,036,0.06,0.54,0.548,0.137,0.274,0.411,0.0685, 0.6165}, sorting all the operation results in the R set, and transmitting the products of the result of the operation in the R set {D11, D22, D32} to the welcome interface (display interface of the handheld device 21, or display device 4), The results are presented to the customer.

The aforementioned set parameters are defined as follows:

X collection

X1: Estimated height detected by the height detecting component, the unit is "cm"

X2: estimated weight detected by the weight detection component in "kg"

X3: Biosystolic pressure index detected by infrared light sensing element, in mmHg

X4: Bio-shrinkage index detected by infrared light sensor, in mmHg

Y collection

Y1: the action gate number sensed by the customer identification module 11

Y collection

Y'1: Is it a company; Y/Yes; N/No

Y'2: the door number fee; five grades A, B, C, D, E are listed according to the existing program tariffs.

Y'3: The date of the door number is calculated, and the time has not yet been reached according to the number of tie-up periods;

Y'4: Age, calculate the current age of the customer based on customer data;

Y'5: gender, female; male

Z collection

Z1: Customer health value

Z11: BMI health value

Z12: blood pressure health value

Z2: is the customer pitch value

Z21: Customer rating value

Z22: Customer due date

Z23: Customer's premium value

Z3: Biometric value for the customer

Z31: Customer age value

Z32: Customer gender value

Z' collection

Z': customer attribute indicator set

Z'1: Health indicators

Z'11: BMI (Body Mass Index) Health Index

Z'12: Blood pressure health indicators

Z'2: Customer indicator

Z'21: Customer grade indicator

Z'22: Customer Expectation Indicator

Z'23: Customer Tariff Indicator

Z'3: Biometric indicators

Z'31: Customer age indicator

Z'32: Customer Gender Indicators

The detailed description of the preferred embodiments of the present invention is intended to be limited to the scope of the invention, and is not intended to limit the scope of the invention. The patent scope of this case.

Claims (10)

A smart door welcoming product recommendation system, comprising: a customer identification module, communicating with a handheld device of a client to obtain identity information of the client; a biometric module, measuring and obtaining biometric information of the client The identity integration module connects the customer identification module and the biometric module, and acquires and integrates the identity information and the biometric information to provide an identity feature integration information; a data analysis module that connects the identity The feature integration module obtains the identity feature integration information, and the data analysis module integrates information according to the identity feature to query and analyze the corresponding customer data, thereby providing a customer interest service content; and a marketing module, connecting the data The analysis module pushes the customer interest service content to the handheld device or pushes it to a display device located in the store. The smart product welcoming product recommendation system according to claim 1, wherein the biometric information further comprises at least one of infrared characteristic information, weight sensing feature information, image information, and height information. The smart store welcoming product recommendation system according to claim 1, wherein the customer identification module obtains the door number information of the handheld device to obtain the identity information. The smart product welcome product recommendation system according to claim 1, wherein the customer data further comprises at least one of customer gender information, customer grade information, customer appointment information, and customer tariff information. The smart product welcome product recommendation system according to claim 1, wherein the identity feature integration information Further includes biometric information that integrates timing. A smart door welcoming product recommendation method is applied to a smart door welcoming product recommendation system, comprising: communicating with a client handheld device to obtain identity information of the client; measuring and obtaining biometric information of the client; Acquiring and integrating the identity information and the biometric information to provide an identity feature integration information; integrating information according to the identity feature to query and analyze corresponding customer data, thereby providing a customer interest service content; and the customer interest The service content is pushed to the handheld device or pushed to a display device located in the store. The smart product welcoming product recommendation method according to claim 6, wherein the biometric information further comprises at least one of infrared characteristic information, weight sensing feature information, image information, and height information. The smart door welcoming product recommendation method according to claim 6, wherein the smart door welcoming product recommendation system obtains the door number information of the handheld device to obtain the identity information. The smart product welcome product recommendation method according to claim 6, wherein the customer data further comprises at least one of customer gender information, customer grade information, customer appointment information, and customer tariff information. The smart store welcome product recommendation method according to claim 6, wherein the identity feature integration information further comprises integrating time series biometric information.