KR20230115511A - A system for providing a customized recommendation service for the design of the sales stand and installation location of Shop-ip-Shop stores - Google Patents

Abstract

A sales counter design and installation location recommendation service system of a shop-in-shop store according to an embodiment of the present invention is a convolution artificial neural network that analyzes the sales counter shape and installation space by reflecting the spatial information of the spatial information analysis unit, the corresponding spatial image, and the product information AI analysis unit that analyzes using a (Convolution Neural Network, CNN)-based learning model; and a recommendation unit for recommending a sales counter design and installation location of the store based on the image of the original store and the sales counter template by reflecting the spatial image information analyzed by the AI analysis unit.

Description

A system for providing a customized recommendation service for the design of the sales stand and installation location of Shop-ip-Shop stores}

The present invention relates to a sales stand design and installation location recommendation service system of a shop-in-shop store, and more specifically, to consider spatial information, product information, and visitor information of an original store, and to consider product information matched to the space and commercial district. Shop-in-shop A sales stand design and installation location recommendation service system for recommending and providing a sales stand installation location and sales stand design in a store.

Shop-in-Shop refers to a business method in which products are sold by creating another shop within an offline store that sells products. Recently, along with the diversification trend of offline stores, the demand to increase the utilization of space within the store is increasing, and the number of shop-in-shop stores is increasing.

Shop-in-shop stores have the advantage of diversifying product groups and attracting new customers by adding new types of product lineups while original stores retain existing customers.

However, since a shop-in-shop store has two different stores coexisting in one space, it must be designed in consideration of correlation and synergy with the original store. In addition, since a shop-in-shop store is also an offline store located in a specific location, it is desirable to configure and market products based on spatial information such as a nearby commercial district or a floating population. Existing shop-in-shop stores were planned in an out-of-the-box way without considering synergies and spatial information with these original stores, so it is necessary to provide a solution that can compensate for this.

A technical problem to be solved through the embodiments of the present invention is a shop-in-shop-based store management method and system that designs products and branding suitable for the store space by performing AI data analysis based on shop-in-shop store space information and visitor information. is to provide

Another technical problem to be solved through the embodiments of the present invention is to analyze the sales effect and inventory status of the shop-in-shop store using the sales information of the shop-in-shop store, and based on this, shop-in-shop basis for guiding renewal and logistics management of the shop-in-shop store To provide a store management method and system.

In addition, the present invention collects and processes information such as the number of visitors and commercial districts in the merchant space, and predicts related products, recommended products, and safety inventory for each commercial district through an artificial intelligence learning algorithm. The purpose is to provide a space and product matching method and system.

In addition, the present invention considers the spatial information, product information, and visitor information of the original store, and considers the product information matched to the space and commercial district. Through an artificial intelligence learning model, the shop-in-shop store's sales counter installation location and sales counter design are recommended. It is to provide a sales stand design and installation location recommendation service system.

A shop-in-shop-based store management system according to an embodiment of the present invention for solving the above problems includes a space/product matching module that recommends products for sale in a store based on space information, product information, and visitor information, the above A product association analysis module that analyzes product sales details of a store and proposes renewal of sales products, an image analysis module that analyzes an image of an original store where the store is located in a shop-in-shop form and recommends a design and installation location of the store, and the above It may include a logistics management module that receives online orders for products sold by the store and manages delivery of products according to the online orders.

The present invention is characterized in matching spaces and products by collecting and processing information such as the number of visitors and commercial districts in the merchant space, and predicting related products, recommended products, and safety inventory for each commercial district through an artificial intelligence learning algorithm.

According to an embodiment of the present invention, it is possible to maximize customer attraction and sales of a shop-in-shop store by designing and proposing products and branding suitable for the store space of the shop-in-shop store.

In addition, shop-in-shop store operation efficiency can be improved by guiding renewal and logistics management of the shop-in-shop store based on the sales effect and inventory status of the shop-in-shop store.

In addition, a data-based one-stop solution that can automatically manage shop-in-shop store entry, marketing, and inventory management can be provided.

1 is a block diagram illustrating a store management system according to an embodiment of the present invention.
FIG. 2 is a diagram schematically illustrating the main functions and concepts of the store management system of FIG. 1 .
FIG. 3 is a diagram for explaining the function and configuration of the space/product matching module shown in FIG. 1 in more detail.
FIG. 4 is a diagram for explaining the function and configuration of the product association analysis module shown in FIG. 1 in more detail.
FIG. 5 is a diagram for explaining functions and configurations of the image analysis module shown in FIG. 1 in more detail.
6 is a diagram for explaining the function and configuration of the logistics management module shown in FIG. 1 in more detail.
7 is a diagram for explaining an example in which a store management system according to an embodiment of the present invention is implemented as a computing device.
8 is a schematic diagram of an integrated platform for space and product matching according to an embodiment of the present invention.
9 is an overview diagram illustrating business linkage between an affiliated store, a shop-in-shop service system, and product partners according to an embodiment of the present invention.

The following merely illustrates the principles of the present invention. Therefore, those skilled in the art can invent various devices that embody the principles of the present invention and fall within the concept and scope of the present invention, even though not explicitly described or shown herein. In addition, it is to be understood that all conditional terms and embodiments listed herein are, in principle, expressly intended only for the purpose of making the concept of the present invention understood, and not limited to such specifically listed embodiments and conditions. It should be.

Further, it should be understood that all detailed descriptions reciting specific embodiments, as well as principles, aspects and embodiments of the present invention, are intended to encompass structural and functional equivalents of these matters. In addition, it should be understood that such equivalents include not only currently known equivalents but also equivalents developed in the future, that is, all devices invented to perform the same function regardless of structure.

Thus, for example, the block diagrams herein are to be understood as representing conceptual views of exemplary circuits embodying the principles of the present invention. Similarly, all flowcharts, state transition diagrams, pseudo code, etc., are meant to be tangibly represented on computer readable media and represent various processes performed by a computer or processor, whether or not the computer or processor is explicitly depicted. It should be.

The functions of various elements shown in the drawings including functional blocks represented by processors or similar concepts may be provided using dedicated hardware as well as hardware capable of executing software in conjunction with appropriate software. When provided by a processor, the functionality may be provided by a single dedicated processor, a single shared processor, or a plurality of separate processors, some of which may be shared.

In addition, the explicit use of terms presented as processor, control, or similar concepts should not be construed as exclusively citing hardware capable of executing software, but without limitation, digital signal processor (DSP) hardware, ROM for storing software (ROM), random access memory (RAM) and non-volatile memory. Other hardware for the governor's use may also be included.

In the claims of this specification, components expressed as means for performing the functions described in the detailed description include, for example, a combination of circuit elements performing the functions or all types of software including firmware/microcode, etc. It is intended to include any method that performs the function of performing the function, combined with suitable circuitry for executing the software to perform the function. Since the invention defined by these claims combines the functions provided by the various enumerated means and is combined in the manner required by the claims, any means capable of providing such functions is equivalent to that discerned from this disclosure. should be understood as

The above objects, features and advantages will become more apparent through the following detailed description in conjunction with the accompanying drawings, and accordingly, those skilled in the art to which the present invention belongs can easily implement the technical idea of the present invention. There will be. In addition, in describing the present invention, if it is determined that a detailed description of a known technology related to the present invention may unnecessarily obscure the subject matter of the present invention, the detailed description will be omitted.

Hereinafter, a preferred embodiment according to the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram illustrating a store management system according to an embodiment of the present invention.

The store management system 100 according to an embodiment of the present invention collects space information 10, product information 20, and visitor information 30, and based on this, sells products suitable for a shop-in-shop store, sales stand design, and installation location , Suggests/recommends sales product renewal to the affiliated store terminal 200. In addition, the store management system 100 may receive an online order from the user terminal 300 and manage product release and inventory accordingly.

The store management system 100 may be connected to the affiliated store terminal 200 or the user terminal 300 through a wired/wireless network and communicate with each other in order to provide store management services according to an embodiment of the present invention. there is.

Here, each network is a Local Area Network (LAN), a Wide Area Network (WAN), a Value Added Network (VAN), a Personal Area Network (PAN), a mobile communication network ( It can be implemented in all types of wired/wireless networks, such as a mobile radiocommunication network) or a satellite communication network.

The affiliated store terminal 200 or the user terminal 300 may be any one individual device among a mobile phone, a smart phone, a smart pad, and a PDA (Personal Digital Assistants).

As an embodiment, the spatial information is information about the area around the shop-in-shop store, commercial district information, interior information of the original store in which the shop-in-shop store is located, information on the size and location of the original store in the shop-in-shop store, or other stores nearby the shop-in-shop store information may be included.

As an embodiment, the product information may include product name information, product group information, product description information of products that can be sold through a shop-in-shop store, or product sales detail information sold through a shop-in-shop store.

As an example, the visitor information may include information on the number of visitors to a shop-in-shop store, information on time of visit, gender information of visitors, information on visitor characteristics, or information on age groups of visitors.

The store management system 100 may include a space/product matching module 110 , a product association analysis module 120 , an image analysis module 130 , and a logistics management module 140 . However, this is an example of some configurations of the store management system 100, and the scope of the present invention is not limited thereto. For example, the store management system 100 may not include some components of the space/product matching module 110, the product association analysis module 120, the image analysis module 130, and the logistics management module 140. and may further include other configurations.

The store management system 100 is machine learning-based artificial intelligence that provides sales product suggestions optimized for shop-in-shop stores, counter design and installation location suggestions, or sales product renewal suggestions through learning of space information, product information, or visitor information. model can be included. For example, at least some of the space/product matching module 110, product association analysis module 120, image analysis module 130, or logistics management module 140 of FIG. 1 are machine learning-based artificial intelligence models. can be implemented Specific configurations and functions of the space/product matching module 110, the product association analysis module 120, the image analysis module 130, or the logistics management module 140 will be described in detail with reference to FIG. 2 and below.

FIG. 2 is a diagram schematically illustrating the main functions and concepts of the store management system of FIG. 1 .

Referring to FIG. 2, the store management system collects spatial information, product information, visitor information, sales information, or public information from affiliated store terminals, user terminals, or external databases, and then analyzes it with an AI-based matching algorithm to shop in shop We recommend products and shelf designs that are best suited to your store space and brand. For a detailed description of this function, reference is made to FIG. 3 below.

FIG. 3 is a diagram for explaining the function and configuration of the space/product matching module shown in FIG. 1 in more detail.

Referring to FIG. 3 , the space/product matching module 110 recommends or suggests a product for sale in a shop-in-shop store based on space information 10 , product information 20 , or visitor information 30 . The space/product matching module 110 recommends a product to be displayed on a sales stand using a space/product matching algorithm and updates the space/product matching algorithm by receiving feedback of product sales results through the sales stand.

As an embodiment, the space/commodity matching algorithm may be an artificial intelligence model based on machine learning. For example, the space/product matching algorithm receives space information 10, product information 20, and visitor information 30 as learning data, performs data preprocessing such as standardization or normalization on the learning data, , It may be an artificial intelligence model learned through a series of steps of extracting features from the preprocessed data and performing model learning based on the extracted features.

As an embodiment, the space/commodity matching algorithm may be an artificial intelligence model learned according to a Support Vector Machine (SVM) algorithm.

FIG. 4 is a diagram for explaining the function and configuration of the product association analysis module shown in FIG. 1 in more detail.

Referring to FIG. 4 , the product association analysis module 120 recommends or proposes renewal of products sold in shop-in-shop stores based on product sales details 21 . The product association analysis module 120 recommends renewal of products at the sales counter using the product association analysis algorithm, and updates the product association analysis algorithm by receiving feedback of a sales result of the renewed product.

As an embodiment, the product association analysis algorithm may be a machine learning-based artificial intelligence model. For example, the product association analysis algorithm receives product sales details 21 as learning data, performs data preprocessing such as standardization or normalization on the learning data, and performs model learning using the preprocessed data. It can be an artificial intelligence model learned through a series of steps.

As an embodiment, the product association analysis algorithm may be an artificial intelligence model learned according to an association analysis algorithm (Association Rule).

FIG. 5 is a diagram for explaining functions and configurations of the image analysis module shown in FIG. 1 in more detail.

Referring to FIG. 5 , the image analysis module 130 includes an AI analysis unit 131 and a sales stand template management unit 132 .

The image analysis module 130 receives a store picture registered through the affiliated store terminal 200, extracts an image from the store picture, and provides it to the AI analysis unit 131.

The AI analysis unit 131 extracts features such as an object, object color, or object distance from an image and learns an artificial intelligence model based thereon. As an embodiment, the artificial intelligence model of the AI analysis unit 131 may be a Convolution Neural Network (CNN)-based model. When the learning of the artificial intelligence model is completed, the AI analysis unit 131 recommends a sales counter design and installation location suitable for a shop-in-shop store using this. At this time, the AI analysis unit 131 provides a store photo or image and template information of the original store where the shop-in-shop store is located as input data of the artificial intelligence model, and output results of the artificial intelligence model accordingly to the affiliated store terminal ( 200) is returned.

The sales stand template manager 132 manages template information provided as input data of the AI analyzer 131 . The sales counter template management unit 132 stores a plurality of templates including the sales counter material, shape, size, or color as main design elements and selectively provides at least some of them to the AI analysis unit 131 as template information.

6 is a diagram for explaining the function and configuration of the logistics management module shown in FIG. 1 in more detail.

Referring to FIG. 6 , the logistics management module 140 includes a sales management unit 141 , a logistics management unit 142 , a delivery management unit 143 , and an order management unit 144 .

The sales management unit 141 performs product management and sales management of the shop-in-shop store. Specifically, the sales manager 141 provides product information to the affiliated store mobile POS app 50 and receives product sales information from the affiliated store mobile POS app 50 . The received product sales information is provided to the logistics management unit 142 and used for inventory management.

The logistics management unit 142 performs product warehousing management, inventory management, and order management. The logistics management unit 142 performs product inventory management based on product sales information, and proceeds with order management through a method of requesting an order to the product supplier server 70 when insufficient inventory occurs. When a new product is received from a product supplier according to an order request, the logistics management unit 142 stores and processes it, and the result is reflected in inventory management.

As an embodiment, when a product order request is made to the product provider server 70, order information and cost settlement information may be automatically notified through an SNS messenger.

The order management unit 144 performs online order management and product release management. The order management unit 144 receives an online order from the user terminal 300 and issues a product release instruction according to the received online order. When the actual product is shipped according to the product release instruction, the release information is transmitted to the delivery management unit 143 .

The delivery management unit 143 performs dispatch management, delivery management, and courier management for product delivery. The delivery management unit 143 manages dispatch and delivery for delivery of outgoing products through its own logistics system, and provides dispatch information or delivery information accordingly to the mobile delivery app 60 or the user terminal 300. . The delivery management unit 143 handles parcel delivery and manages the waybill for delivery of products through an external delivery system, and provides delivery information indicating the delivery status and the waybill to the user terminal 300 . As an embodiment, the delivery information may interwork with an external courier company's waybill management system.

According to the store management system described above, it is possible to maximize customer attraction and sales of the shop-in-shop store by designing and proposing products and branding suitable for the store space of the shop-in-shop store. In addition, shop-in-shop store operation efficiency can be improved by guiding renewal and logistics management of the shop-in-shop store based on the sales effect and inventory status of the shop-in-shop store. In addition, a data-based one-stop solution that can automatically manage shop-in-shop store entry, marketing, and inventory management can be provided.

Hereinafter, an exemplary computing device 500 in which a store management system according to various embodiments of the present disclosure is implemented will be described with reference to FIG. 7 .

7 is an exemplary hardware configuration diagram illustrating a computing device 500 .

As shown in FIG. 7, the computing device 500 loads one or more processors 510, a bus 550, a communication interface 570, and a computer program 591 executed by the processor 510. It may include a memory 530 and a storage 590 for storing the computer program 591. However, only components related to the embodiment of the present invention are shown in FIG. 7 . Therefore, those skilled in the art to which the present invention pertains can know that other general-purpose components may be further included in addition to the components shown in FIG. 7 .

The processor 510 controls the overall operation of each component of the computing device 500 . The processor 510 may include at least one of a Central Processing Unit (CPU), a Micro Processor Unit (MPU), a Micro Controller Unit (MCU), a Graphic Processing Unit (GPU), or any type of processor well known in the art. can be configured to include Also, the processor 510 may perform an operation for at least one application or program for executing a method/operation according to various embodiments of the present disclosure. Computing device 500 may include one or more processors.

Memory 530 stores various data, commands and/or information. Memory 530 may load one or more programs 591 from storage 590 to execute methods/operations according to various embodiments of the present invention. An example of the memory 530 may be RAM, but is not limited thereto.

The bus 550 provides a communication function between components of the computing device 500 . The bus 550 may be implemented as various types of buses such as an address bus, a data bus, and a control bus.

The communication interface 570 supports wired and wireless Internet communication of the computing device 500 . The communication interface 570 may support various communication methods other than Internet communication. To this end, the communication interface 570 may include a communication module well known in the art.

Storage 590 may non-temporarily store one or more computer programs 591 . The storage 590 may be a non-volatile memory such as read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, or the like, a hard disk, a removable disk, or a device well known in the art. It may be configured to include any known type of computer-readable recording medium.

Computer program 591 may include one or more instructions for implementing methods/operations according to various embodiments of the present invention. When computer program 591 is loaded into memory 530, processor 510 may execute the one or more instructions to perform methods/acts according to various embodiments of the present invention.

Meanwhile, FIG. 8 is a schematic diagram of an integrated platform for space and product matching according to an embodiment of the present invention.

In the server (Gongppu Technology) that manages the shop-in-shop store of the present invention, a space-product matching integrated platform is provided. The platform collects sales data by processing self-collected sales information of the POS machine of the shop-in-shop store. In addition, the platform provides a business collaboration platform service for businesses through a collaboration tool, enabling real-time business processing with affiliated stores and brand companies.

As a method for matching sangchums suitable for the characteristics of shop-in-shop stores, a spatial analysis algorithm (AI) that collects and processes information on the number of visitors, commercial districts, and shop-in-shop store installation spaces in the franchise space, related products and recommended products for each commercial district, Safety stock prediction includes a matching algorithm (AI) that matches spaces and products using artificial intelligence learning algorithms.

9 is an overview diagram illustrating business linkage between an affiliated store, a shop-in-shop service system, and product partners according to an embodiment of the present invention.

The head office system performs inventory demand forecasting, product ordering, normal product registration, etc. in the SCM system, and performs accounting, sales, purchases, tax invoice issuance, etc. using the ERP system. The affiliate store system receives sales settlement, data collection and product matching, product warehousing and collection management with the head office system, and generates and provides store application, sales settlement, analysis report, etc. In addition, the partner company's system shares sales settlement, product release, sales, and matching data with the head office's system, and performs product registration, sales status, inventory status, order management, and settlement management.

The method according to the present invention described above may be produced as a program to be executed on a computer and stored in a computer-readable recording medium. Examples of the computer-readable recording medium include ROM, RAM, CD-ROM, and magnetic tape. , floppy disks, and optical data storage devices.

The computer-readable recording medium is distributed to computer systems connected through a network, so that computer-readable codes can be stored and executed in a distributed manner. In addition, functional programs, codes, and code segments for implementing the method can be easily inferred by programmers in the technical field to which the present invention belongs.

In addition, although the preferred embodiments of the present invention have been shown and described above, the present invention is not limited to the specific embodiments described above, and the technical field to which the present invention belongs without departing from the gist of the present invention claimed in the claims. Of course, various modifications can be made by those skilled in the art, and these modifications should not be individually understood from the technical spirit or perspective of the present invention.

Claims (2)

In the shop design and installation location recommendation service system of a shop-in-shop store,
a spatial information analyzer including regional information, commercial district information, interior information of the original store, size and location information of the original store, or other store information in the vicinity of the store;
a product information analyzer including product name information, product group information, description information, or sales detail information of a corresponding store;
AI analysis unit that analyzes the spatial information of the spatial information analysis unit, the corresponding spatial image, and the product information by using a Convolution Neural Network (CNN)-based learning model that analyzes the sales counter shape and installation space ; and
A recommendation unit for recommending a sales counter design and installation location of the store based on the image of the original store and a sales counter template by reflecting the spatial image information analyzed by the AI analysis unit;
Shop-in-shop sales counter design and installation location recommendation service system. According to claim 1,
The artificial intelligence learning model,
To further reflect visitor information including visitor number information, visiting time zone information, visitor gender information, visitor characteristics information, or visitor age information, and to recommend installation locations and sales counter designs by reflecting product information matched to the space
Shop-in-shop sales counter design and installation location recommendation service system.