Intelligent retail business management system based on big data

Abstract

The invention discloses an intelligent retail business management system based on big data, which comprises a retail tracing and checking module, a retail business analysis module and a retail management module, and is characterized in that the retail tracing and checking module is suitable for checking and tracking commodity label information in a self-service heating beverage purchasing scene; the retail business analysis module is used for analyzing commodity selling conditions in an automatic retail hot drink selling scene, the retail management module is used for intelligently managing self-service purchased commodities, the retail tracing and checking module is electrically connected with the retail business analysis module, the retail business analysis module is electrically connected with the retail management module, and the retail management module is electrically connected with the retail management module. The retail tracing and checking module comprises a monitoring and collecting module, a label printing module and a label information association module, the monitoring and collecting module is used for monitoring and collecting pictures in the commodity shelving and selling process, the label printing module is used when the commodities are shelved, and the system has the advantages of being high in practicability and high in intelligent degree.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to an intelligent retail business management system based on big data. Background Art

[0002] When winter comes, many people like to buy self-heated drinks in convenience stores. In cold weather, providing drinks that warm both the hands and the stomach is a heartwarming move by emerging convenience stores that pay more attention to the customer shopping experience.

[0003] However, according to food safety experts, some additives in beverages will react chemically as the temperature rises. Moreover, milk tea beverages are not 100% sterile. The increase in temperature will accelerate the reproduction of bacteria. If the time is too long, it will easily cause the beverage to deteriorate. Although some convenience stores have noticed the safety issues of heated beverages, they have adopted the method of "sticker labeling" for convenience store staff to indicate the drinking safety period and other information. However, manual labeling is not only time-consuming and labor-intensive, but also the sticker information is easy to be tampered with, and it cannot bring complete trust to consumers. At the same time, the severely shortened drinking safety period caused by heating beverages has put forward higher requirements for the timing of heating and selling. Traditional manual sales can easily cause some hot-selling beverages to be out of stock or some beverages to be sold outside the drinking safety period, resulting in increased retail costs. Therefore, it is necessary to design a smart retail business management system based on big data that is highly practical and intelligent. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent retail business management system based on big data to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent retail business management system based on big data, comprising a retail tracing and viewing module, a retail business analysis module and a retail management module, wherein the retail tracing and viewing module is suitable for self-service purchase of heated beverages, and self-service viewing and tracking of product label information, the retail business analysis module is used to analyze the product sales status in the automatic retail hot drink sales scenario, and the retail management module is used to intelligently manage self-purchased products, the retail tracing and viewing module is electrically connected to the retail business analysis module, and the retail business analysis module is electrically connected to the retail management module.

[0006] According to the above technical solution, the retail traceability viewing module includes a monitoring and acquisition module, a label printing module and a label information association module. The monitoring and acquisition module is used to monitor and collect the product listing and sales process screens. The label printing module is used to print and scroll the product information association label on the product when the product is listed. The label information association module is used to integrate and associate the product information with the label QR code link.

[0007] According to the above technical solution, the retail business analysis module includes a sales heat analysis module and a stage retail evaluation module. The sales heat analysis module is used to analyze the sales heat of self-service retail goods, and the stage retail evaluation module is used to evaluate the sales situation of goods in stages during the retail process.

[0008] According to the above technical solution, the sales heat analysis module further includes a face recognition submodule, a pupil tracking submodule, a gaze judgment submodule and a sales database, the face recognition submodule is used to identify face information, the pupil tracking submodule is used to further track the pupil image in the identified face information, the gaze judgment submodule is used to judge the product categories that customers pay attention to when browsing products, and the sales database is used to statistically record the historical retail volume of products.

[0009] According to the above technical solution, the retail management module includes an advertising placement module and a product replacement identification module. The advertising placement module is used to intelligently place advertisements on self-service vending machines, and the product replacement identification module is used to make product replacement identification for products with low sales popularity, thereby providing suggestions for retail management.

[0010] According to the above technical solution, the operating method of the retail traceability viewing module includes the following steps:

[0011] Step S1: When the goods are automatically put on the shelves from the backup storage of the self-service vending machine and put into the cabinet for sale, the label printing module prints and sticks labels on the goods in the process of rolling into the cabinet;

[0012] Step S2: At the same time, in the above process, the monitoring and collection module is used to collect the whole process of goods entering the cabinet;

[0013] Step S3: After the goods are put into the cabinet, the cabinet monitoring is further used to monitor the screen during the sales period;

[0014] Step S4: Finally, through the tag information association module, the monitoring images of the product during the shelf and shelf period and the sales period and the production-related information of the product are associated with the tag link of the current product.

[0015] According to the above technical solution, the operating method of the retail business analysis module includes the following steps:

[0016] Step A: First, use the sales heat analysis module to analyze and determine the sales heat of each category of goods on the shelves of the self-service vending machine;

[0017] Step B: According to the proportion of sales popularity to the total sales popularity of all product categories in the self-service vending machine, z, and the total number of products in the self-service vending machine, C, the current product shelf base is calculated using the formula B=zC;

[0018] Step C: Divide the maximum shelf life of the product into n sales stages. After each sales stage, count the sales quantity m of the product. The stage retail evaluation module evaluates that the stage sales volume of the product is lower than expected, and triggers an electrical signal to be output to the retail management module.

[0019] According to the above technical solution, the step A further comprises the following steps:

[0020] Step A1: deploy a face recognition submodule at the center of each category of commodity shelf module of the self-service vending machine, and use the face recognition submodule to perform face recognition detection on the area directly in front of the corresponding category of shelf module;

[0021] Step A2: When the face recognition submodule recognizes face information, the face is framed and selected. When multiple face recognition submodules recognize the same portrait information, the face recognition submodule with the highest degree of face frame selection in the corresponding face recognition screen is marked;

[0022] Step A3: the commodity category at the location of the marked face recognition submodule is set as a preset commodity, and then the pupil tracking submodule is further enabled to further track the pupil image in the face information recognized by the marked face recognition submodule;

[0023] Step A4: Obtain the proportion of the pixel points of the whites of the pupils on both sides compared to the pupil pixels. When the deviation of the proportions on both sides is less than 20% and the condition is continuously satisfied for 3 seconds or more, the gaze judgment submodule determines that the preset product is gazed at once; when the deviation of the proportions on both sides is greater than 20% and the condition is continuously satisfied for 3 seconds or more, the gaze judgment submodule determines that the preset product on the side with the smaller white area of ​​the customer's eye is gazed at once;

[0024] Step A5: Obtain the historical retail sales volume of commodities;

[0025] Step A6: The sales heat analysis module analyzes and calculates the sales heat of each category of goods through the formula R=αq+βp, where α and β are control parameters of the number of gazes and historical retail volume, respectively, and are both constants greater than 0; q and p are the historical number of gazes and historical retail volume, respectively.

[0026] According to the above technical solution, the operating method of the retail management module includes the following steps:

[0027] Step u: when a signal indicating that the sales volume is lower than expected is obtained, the retail management module controls the advertising delivery module to autonomously deliver corresponding product advertisements on the display screen of the autonomous vending machine;

[0028] Step v: The system presets a product replacement cycle. After all products have been on sale for a replacement cycle, the product with the lowest sales popularity is marked for sale.

[0029] Compared with the prior art, the beneficial effect achieved by the present invention is that the present invention can further divide the commodity into multiple small stages based on the commodity sales heat analysis results for recent refined evaluation, thereby facilitating intelligent management and adjustment of retail business during the sales period, avoiding large forecast analysis errors resulting in too many commodities not being sold in time, resulting in commodity heating exceeding the safe drinking period and causing commodity waste and economic losses, thereby achieving the effect of sensitively adjusting the management of the retail model. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0031] Figure 1 It is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] See also Figure 1 The present invention provides a technical solution: an intelligent retail business management system based on big data, including a retail tracing and viewing module, a retail business analysis module and a retail management module. The retail tracing and viewing module is suitable for self-service purchase of heated beverages, and self-service viewing and tracking of product label information. The retail business analysis module is used to analyze the product sales status in the automatic retail hot drink sales scenario. The retail management module is used to intelligently manage self-service purchased products. The retail tracing and viewing module is electrically connected to the retail business analysis module, and the retail business analysis module is electrically connected to the retail management module.

[0034] The retail traceability viewing module includes a monitoring and acquisition module, a label printing module and a label information association module. The monitoring and acquisition module is used to monitor and collect the product listing and sales process images. The label printing module is used to print and scroll the product information association label when the product is listed. The label information association module is used to integrate and associate the product information with the label QR code link.

[0035] The retail business analysis module includes a sales heat analysis module and a stage retail evaluation module. The sales heat analysis module is used to analyze the sales heat of self-service retail products, and the stage retail evaluation module is used to evaluate the sales situation of products in stages during the retail process.

[0036] The sales heat analysis module further includes a face recognition submodule, a pupil tracking submodule, a gaze judgment submodule and a sales database. The face recognition submodule is used to identify face information, the pupil tracking submodule is used to further track the pupil image in the identified face information, the gaze judgment submodule is used to determine the product categories that customers pay attention to when browsing products, and the sales database is used to statistically record the historical retail volume of products.

[0037] The retail management module includes an advertising module and a product replacement identification module. The advertising module is used to intelligently place advertisements on self-service vending machines, and the product replacement identification module is used to make product replacement identification for products with low sales popularity, thereby providing suggestions for retail management.

[0038] The operation method of the retail traceability viewing module includes the following steps:

[0039] Step S1: When the goods are automatically put on the shelves from the backup storage of the self-service vending machine and put into the cabinet for sale, the label printing module prints and sticks labels on the goods in the process of rolling into the cabinet;

[0040] Step S2: At the same time, in the above process, the monitoring and collection module is used to collect the whole process of goods entering the cabinet;

[0041] Step S3: After the goods are put into the cabinet, the cabinet monitoring is further used to monitor the screen during the sales period;

[0042] Step S4: Finally, through the tag information association module, the monitoring images of the product during the shelf and cabinet period and the sales period and the production-related information of the product are associated with the tag link of the current product; when the customer purchases the product by himself, he can directly obtain and track the product shelf heating time through the tag, and can also remotely view the production-related information, avoiding the traditional self-service vending machine that requires viewing after purchase, making the information more transparent and improving consumer purchasing confidence.

[0043] The operation method of the retail business analysis module includes the following steps:

[0044] Step A: First, use the sales heat analysis module to analyze and determine the sales heat of each category of goods on the shelves of the self-service vending machine;

[0045] Step B: According to the proportion of sales popularity to the total sales popularity of all product categories in the self-service vending machine, z, and the total number of products in the self-service vending machine, C, the current product shelf base is calculated using the formula B=zC;

[0046] Step C: Divide the maximum shelf life of the product into n sales stages. After each sales stage, count the sales quantity m of the product. The stage retail evaluation module evaluates that the sales volume of the product in that stage is lower than expected, and triggers an electrical signal to be output to the retail management module. Through the above steps, the product can be further divided into multiple small stages based on the analysis results of the product sales heat for recent refined evaluation, thereby facilitating intelligent management and adjustment of the retail business during the sales period, avoiding large forecasting and analysis errors that cause too many products to be unsold in time, resulting in the heating of products exceeding the safe drinking period, causing product waste and economic losses, and achieving the effect of sensitively adjusting the management of the retail model.

[0047] Step A further comprises the following steps:

[0048] Step A1: deploy a face recognition submodule at the center of each category of commodity shelf module of the self-service vending machine, and use the face recognition submodule to perform face recognition detection on the area directly in front of the corresponding category of shelf module;

[0049] Step A2: When the face recognition submodule recognizes face information, the face is framed and selected. When multiple face recognition submodules recognize the same portrait information, the face recognition submodule with the highest degree of face frame selection in the corresponding face recognition screen is marked;

[0050] Step A3: the commodity category at the location of the marked face recognition submodule is set as a preset commodity, and then the pupil tracking submodule is further enabled to further track the pupil image in the face information recognized by the marked face recognition submodule;

[0051] Step A4: Obtain the proportion of the pixel points of the whites of the pupils on both sides compared to the pupil pixels. When the deviation of the proportions on both sides is less than 20% and the condition is continuously satisfied for 3 seconds or more, the gaze judgment submodule determines that the preset product is gazed at once; when the deviation of the proportions on both sides is greater than 20% and the condition is continuously satisfied for 3 seconds or more, the gaze judgment submodule determines that the preset product on the side with the smaller white area of ​​the customer's eye is gazed at once;

[0052] Step A5: Obtain the historical retail sales volume of commodities;

[0053] Step A6: The sales heat analysis module analyzes and calculates the sales heat of each category of goods through the formula R=αq+βp, where α and β are control parameters of the number of gazes and historical retail volume, respectively, and are both constants greater than 0; q and p are the historical number of gazes and historical retail volume, respectively.

[0054] The operation method of the retail management module includes the following steps:

[0055] Step u: when a signal indicating that the sales volume is lower than expected is obtained, the retail management module controls the advertising delivery module to autonomously deliver corresponding product advertisements on the display screen of the autonomous vending machine;

[0056] Step v: The system presets a product replacement cycle. After all products have been on sale for a replacement cycle, the product with the lowest sales popularity is marked for sale.

[0057] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0058] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent retail business management system based on big data, including a retail traceability viewing module, a retail business analysis module and a retail management module, characterized in that: The retail tracing and viewing module is suitable for self-service purchase of heated beverages, and self-service viewing and tracking of product label information. The retail business analysis module is used to analyze the product sales status in the automatic retail hot drink sales scenario. The retail management module is used to intelligently manage self-service purchased products. The retail tracing and viewing module is electrically connected to the retail business analysis module, and the retail business analysis module is electrically connected to the retail management module. The retail traceability viewing module includes a monitoring and acquisition module, a label printing module and a label information association module. The monitoring and acquisition module is used to monitor and collect pictures of product placement and sales process. The label printing module is used to print and scroll the product information association label on the product when the product is put on the shelf. The label information association module is used to integrate and associate the product information with the label QR code link; The retail business analysis module includes a sales heat analysis module and a stage retail evaluation module. The sales heat analysis module is used to analyze the sales heat of self-service retail products, and the stage retail evaluation module is used to evaluate the sales of products in stages during the retail process. The sales heat analysis module further includes a face recognition submodule, a pupil tracking submodule, a gaze judgment submodule and a sales database, wherein the face recognition submodule is used to recognize face information, the pupil tracking submodule is used to further track pupil images in the recognized face information, the gaze judgment submodule is used to judge the commodity categories that customers pay attention to when browsing commodities, and the sales database is used to count and record the historical commodity retail volume; The retail management module includes an advertisement delivery module and a commodity replacement identification module. The advertisement delivery module is used to intelligently deliver advertisements on the self-service vending machine, and the commodity replacement identification module is used to mark commodity replacements for commodities with low sales popularity, thereby providing suggestions for retail management. The operating method of the retail traceability viewing module comprises the following steps: Step S1: When the goods are automatically put on the shelves from the backup storage of the self-service vending machine and put into the cabinet for sale, the label printing module prints and sticks labels on the goods in the process of rolling into the cabinet; Step S2: At the same time, in the above process, the monitoring and collection module is used to collect the whole process of goods entering the cabinet; Step S3: After the goods are put into the cabinet, the cabinet monitoring is further used to monitor the screen during the sales period; Step S4: Finally, through the tag information association module, the monitoring images of the product during the shelf and shelf period and the sales period and the production-related information of the product are associated with the tag link of the current product; The operating method of the retail business analysis module comprises the following steps: Step A: First, use the sales heat analysis module to analyze and determine the sales heat of each category of goods on the shelves of the self-service vending machine; Step B: According to the proportion of sales popularity to the total sales popularity of all product categories in the self-service vending machine, z, and the total number of products in the self-service vending machine, C, the current product shelf base is calculated using the formula B=zC; Step C: Divide the maximum shelf life of the product into n sales stages. After each sales stage, count the sales quantity m of the product. The stage retail evaluation module evaluates that the sales volume of the product stage is lower than expected, and triggers an electrical signal to be output to the retail management module; through the above steps, the product can be further divided into multiple small stages based on the analysis results of the product sales heat to conduct recent refined evaluation, thereby facilitating intelligent management and adjustment of the retail business during the sales period, avoiding the occurrence of large forecast analysis errors resulting in too many products not being sold in time, resulting in product heating exceeding the safe drinking period and causing product waste and economic losses, and achieving the effect of sensitively adjusting the management of the retail model; The operating method of the retail management module comprises the following steps: Step u: when a signal indicating that the sales volume is lower than expected is obtained, the retail management module controls the advertisement placement module to place corresponding product advertisements on the display screen of the autonomous vending machine; Step v: The system presets a product replacement cycle. After all products have been on sale for a replacement cycle, the product with the lowest sales popularity is marked for sale.

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