VR supermarket shopping system with intelligent guide and real-time recommendation functions

Abstract

The invention discloses a VR supermarket shopping system with intelligent guide and real-time recommendation functions, which relates to the technical field of online shopping and comprises an image construction module, a demand analysis module, a guide planning module, a commodity analysis module and a commodity recommendation module. According to the invention, by obtaining the layout of the commodities in the supermarket, constructing the three-dimensional layout map of the positions of the commodities and marking the specific coordinate points of the positions of the commodities, when a shopper carries out shopping, the shopping demands of the commodities are obtained through intelligent identification, and the shopping guide route in the supermarket is generated according to the shopping demands. The shopper is guided to browse the required commodity in a short time through the shopping guide route; when the commodity is selected, a real-time shopping recommendation table is generated according to the required commodity, the recent shopping sales condition of the commodity is displayed, and the customer satisfaction of each commodity is marked for reference of a shopper.

Description

Technical Field

[0001] The present invention relates to the technical field of online shopping, and particularly to a VR supermarket shopping system with intelligent navigation and real-time recommendation functions. Background Art

[0002] VR virtual reality technology is a computer simulation system that can create and experience virtual worlds. It uses a computer to generate a simulated environment, which is a system simulation of multi-source information fusion, interactive three-dimensional dynamic visual scenes, and entity behaviors, enabling users to immerse themselves in this environment. In the early days, VR technology began to enter the shopping field. Brands can use VR technology to replicate offline physical stores 1:1 into virtual spaces, including details such as store layouts, shelf displays, and product placements, which are consistent with reality. After consumers enter the virtual shopping environment, they can browse the preset product displays to select products.

[0003] In existing VR supermarket shopping, after entering the virtual shopping environment, users can only passively browse the preset product displays, unable to obtain detailed product information in real time. When wanting to purchase multiple products distributed in different areas in the virtual supermarket, the system cannot automatically generate the optimal walking route, resulting in consumers getting lost or taking detours in the virtual environment just like in a physical supermarket, thus causing consumers to spend too much time during the shopping process and resulting in a poor experience.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the Invention

[0005] The purpose of the present invention is to: help users quickly find the required products through the intelligent navigation function, reduce the time wasted in searching for products in the supermarket, improve the shopping efficiency, and provide personalized product recommendations for users through the real-time recommendation function, increasing the chance for users to discover their favorite products.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions: A VR supermarket shopping system with intelligent navigation and real-time recommendation functions, including an image construction module, a demand analysis module, a navigation planning module, a product analysis module, and a product recommendation module.

[0007] The image construction module is used to obtain the product information and layout inside the supermarket, integrate them according to the product information and layout, draw a three-dimensional layout map of the products inside the supermarket, classify and label the product positions through different types of product information, and store the obtained product information and layout in the supermarket management terminal.

[0008] The demand analysis module is used to obtain the user's shopping needs and the shopper's location through the supermarket shopping terminal, query the products in the supermarket that meet the needs according to the user's shopping needs, obtain the product location information, generate a product demand list, and send the product demand list to the navigation planning module.

[0009] The navigation planning module is used for the route generation unit and the route analysis unit. The route generation unit obtains the commodity demand list, generates a navigation planning route based on the commodity positions marked in the commodity demand list, the shopper's position, and the real-time commodity positions.

[0010] The route analysis unit is used to obtain the navigation planning route, combine the number of purchased commodities and the commodity positions to obtain the position distances between commodities, generate the consumption time generated between commodities based on the position distances, and generate the time required for the navigation planning route by combining the consumption time and the shopper's position distance.

[0011] The commodity analysis module is used to obtain the historical sales information of commodities in the supermarket, establish a commodity evaluation form based on the historical sales information, record the after-sales evaluation information, sales volume, and price of purchased commodities through the commodity evaluation form, generate the commodity recommendation degree based on the after-sales evaluation information of commodities, and generate the commodity recommendation index by combining the sales volume and price with the commodity recommendation degree.

[0012] The commodity recommendation module is used to obtain the commodity demand list, obtain the recommendation degree and sales volume of commodities according to the commodity demand list, and generate a commodity recommendation form. The commodity recommendation form includes commodity pictures, names, prices, and recommendation degrees.

[0013] Furthermore, the supermarket management terminal is used to manage the commodity information inside the supermarket in real time, including commodity prices, inventory, sales volume, and commodity information updates. It also includes a user terminal, which is used to store the user's shopping history data.

[0014] Furthermore, generating the commodity demand list specifically includes the following:

[0015] S100. The user inputs the name of the commodity to be purchased, determines the category to which the commodity belongs based on the commodity name, and conducts a targeted query on the commodity according to the category.

[0016] S101. Query the commodities that meet the requirements in the category to which the commodity belongs, obtain a list of commodities that meet the requirements, and find the specific position coordinates of the commodities in the supermarket according to the list of commodities that meet the requirements.

[0017] S102. Integrate the specific coordinate positions with the commodity information and highlight the marks in the three-dimensional layout diagram to obtain a commodity demand list with specific position coordinates.

[0018] Furthermore, generating the time planning route according to the order of purchased commodities specifically includes the following:

[0019] S200. Obtain the number of commodities to be shopped according to the commodity demand list, and obtain the position distances between commodities through the number of commodities and the commodity positions.

[0020] S201. Obtain the positional distance between products, calculate the time required based on the positional distance, and combine the required time with the shopper's positional distance to obtain the time required for the navigation planning route.

[0021] S202. When selecting the shopper's positional distance and the positional distance between products, intercept the point closest to the shopper's positional distance as the starting point of the shopping product.

[0022] Further, generating the product recommendation degree based on the product after-sales evaluation information specifically includes the following:

[0023] S300. Obtain the sales quantity of products within a certain period, obtain the product after-sales evaluation information based on the sales quantity, and through the product after-sales evaluation information, obtain the product evaluation quantity, including the number of good reviews and the number of bad reviews.

[0024] S301. Based on the product evaluation quantity a and the number of good reviews s, combined with the sales quantity x, obtain the recommendation degree q, where

[0025]

[0026] In the formula, Take the square root of the evaluation quantity to make the influence of the evaluation quantity not too prominent. is the good review rate, ln(x + 1) is the natural logarithm of the sales quantity, and the sales quantity reflects the market acceptance of the product.

[0027] S302. Generate the recommendation degree of each product within the period through the after-sales evaluation information of each product, and the recommendation degree is updated in real time through the sales quantity of the product.

[0028] Further, generating the product recommendation index specifically includes the following:

[0029] S400. Obtain the sales quantity x and the product price j of the product, and combine the product recommendation degree to calculate the product recommendation index d of each product within the period:

[0030] Preliminarily calculate the relationship t between the product price and the sales quantity:

[0031]

[0032] In the formula, w1 is the weight coefficient used to adjust the relationship between the sales quantity and the product price. is the average value of the sales quantity of the product over a past period of time.

[0033] S401. The product recommendation index generated in different periods is different and is updated in real time with the period as the time segment.

[0034] Furthermore, when the product recommendation module displays the product recommendation list, it is presented in the form of virtual cards, including key information such as product pictures, names, and prices. Users can click on the cards to view the product details and directly add them to the shopping list. Different product information is presented through different colors, charts, and label elements.

[0035] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are as follows:

[0036] The VR supermarket shopping system with intelligent navigation and real-time recommendation functions obtains the layout of products in the supermarket, constructs a three-dimensional layout map of the product positions in the supermarket, marks specific coordinate points for the positions of the products. When shoppers are shopping, it intelligently identifies the shopping needs of the products, generates a shopping navigation route in the supermarket according to the shopping needs of the products, and guides the shoppers to browse the required products in a short time through the shopping navigation route; when selecting products, it generates a real-time shopping recommendation list according to the required products, shows the recent shopping sales situation of the products, and marks the customer satisfaction of each product for the reference of the shoppers. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 Shows the overall structural schematic diagram of the system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0039] Embodiment 1:

[0040] As Figure 1 shown, the VR supermarket shopping system with intelligent navigation and real-time recommendation functions includes an image construction module, a demand analysis module, a navigation planning module, a product analysis module, and a product recommendation module.

[0041] The image construction module is used to obtain the product information and layout inside the supermarket, integrate them according to the product information and layout, draw a three-dimensional layout map of the products inside the supermarket, classify and label the product positions through different types of product information, and store the obtained product information and layout in the supermarket management terminal.

[0042] When constructing a 3D layout diagram, mark the names of each area and major commodity categories, such as "Daily Necessities Area", "Food Area", "Fruits and Vegetables Area". Different colored pens or symbols can be used to distinguish different areas, making the shopping map clearer and easier to understand;

[0043] The demand analysis module is used to obtain the user's shopping needs and the shopper's location through the supermarket shopping terminal. According to the user's shopping needs, query the goods in the supermarket that meet the needs, obtain the location information of the goods, generate a goods demand list, and send the goods demand list to the navigation planning module;

[0044] The navigation planning module is used for a route generation unit and a route analysis unit. The route generation unit obtains the goods demand list, and through the goods positions marked on the goods demand list, generates a navigation planning route according to the shopper's location and the real-time goods positions;

[0045] The route analysis unit is used to obtain the navigation planning route, combine the number of purchased goods and the goods positions to obtain the position distances between the goods, generate the consumption time generated between the goods according to the position distances, and combine the consumption time and the shopper's position distance to generate the time required for the navigation planning route;

[0046] The goods analysis module is used to obtain the historical sales information of the goods in the supermarket, establish a goods evaluation form according to the historical sales information, record the after-sales evaluation information, sales volume, and price of the purchased goods through the goods evaluation form, generate a goods recommendation degree according to the goods after-sales evaluation information, and generate a goods recommendation index by combining the sales volume and price with the goods recommendation degree;

[0047] The goods recommendation module is used to obtain the goods demand list, and according to the goods demand list, obtain the recommendation degree and sales volume of the goods, generate a goods recommendation form, and the goods recommendation form includes goods pictures, names, prices, and recommendation degrees.

[0048] This system includes a server side and a user terminal. The server side includes a VR helmet and a handle device, which are used for the user to experience the VR supermarket shopping environment, receive the image and sound information transmitted by the system, and realize the interaction operation with the virtual environment through the handle, so as to realize the data interaction between the server side and the user terminal through the network.

[0049] The supermarket management terminal is used to manage the goods information inside the supermarket in real time, including goods prices, inventory, sales volume, and goods information update. It also includes a user terminal, which is used to store the user's shopping history data.

[0050] Generating the goods demand list specifically includes the following:

[0051] S100. The user inputs the name of the goods to be purchased, judges the category to which the goods belong according to the goods name, and conducts a targeted query on the goods according to the category;

[0052] S101. Query the products that meet the requirements in the category to which the product belongs, obtain a list of products that meet the requirements, and based on the list of products that meet the requirements, find the specific location coordinates of the products in the supermarket;

[0053] S102. Integrate the specific coordinate positions with the product information and highlight the marks in the 3D layout diagram to obtain a product demand list with specific location coordinates.

[0054] Generate a time - planning route according to the order of purchasing products, which specifically includes the following:

[0055] S200. Obtain the quantity of products to be purchased according to the product demand list, and based on the quantity of products and their positions, obtain the position distances between the products;

[0056] S201. Obtain the position distances between the products, calculate the time required based on the position distances, and combine the time required with the shopper's position distance to obtain the time required for the navigation - planning route;

[0057] S202. When selecting the shopper's position distance and the position distances between the products, intercept the point closest to the shopper's position distance as the starting point for shopping products.

[0058] Generate a product recommendation degree according to the product after - sales evaluation information, which specifically includes the following:

[0059] S300. Obtain the sales quantity of products within a certain period, obtain the product after - sales evaluation information based on the sales quantity, and through the product after - sales evaluation information, obtain the number of product evaluations, including the number of good reviews and the number of bad reviews;

[0060] S301. Based on the number of product evaluations \(a\) and the number of good reviews \(s\), combined with the sales quantity \(x\), obtain the recommendation degree \(q\), where,

[0061]

[0062] In the formula, Take the square root of the number of evaluations to make the influence of the number of evaluations not too prominent, is the good - review rate, \(\ln(x + 1)\) is the natural logarithm of the sales quantity, and the sales quantity reflects the market acceptance of the product;

[0063] S302. Generate the recommendation degree of each product within the period through the after - sales evaluation information of each product, and the recommendation degree is updated in real - time according to the sales quantity of the product.

[0064] Generate a product recommendation index by combining the product recommendation degree, which specifically includes the following:

[0065] S400. Obtain the sales quantity x and the commodity price j of the commodity, and combine the commodity recommendation degree to calculate the commodity recommendation index d of each commodity within the cycle:

[0066] Preliminarily calculate the relationship t between the commodity price and the sales quantity:

[0067]

[0068] In the formula, w1 is the weight coefficient, which is used to adjust the relationship between the sales quantity and the commodity price. is the average value of the sales quantity of the commodity in the past period of time. The explanation of this formula is as follows: q, as the commodity recommendation degree, is a basic recommendation index. If the commodity itself has a high recommendation degree, then its contribution to the recommendation index is large. According to the adjustment of the recommendation index by the current sales quantity, the average sales quantity, and the price influence coefficient, when x > x, that is, the current sales quantity is higher than the average level, it indicates that the commodity currently performs well. When x < x, that is, the current sales quantity is lower than the average level;

[0069] S401. The commodity recommendation index generated in different cycles is different and is updated in real time with the cycle as the time period.

[0070] When the commodity recommendation module displays the commodity recommendation list, it is displayed in the form of virtual cards, including key information such as commodity pictures, names, and prices. Users can click on the card to view the commodity details and directly add them to the shopping list. When displaying commodity information, different colors, charts, and label elements are used to display different commodity information;

[0071] It can also provide voice prompts to introduce commodity information to users. For example, when the user approaches a certain shelf, the voice guide will prompt the popular commodities on the shelf or the commodity information related to the user's shopping list to help the user better understand the surrounding environment.

[0072] The present invention, a VR supermarket shopping system with intelligent navigation and real-time recommendation functions, constructs a three-dimensional layout map of the positions of supermarket commodities through the layout of commodities in the supermarket, marks specific coordinate points for the positions of the commodities. When a shopper is shopping, it obtains the shopping needs of the shopper through intelligent recognition, generates a shopping navigation route in the supermarket according to the shopping needs of the shopper, and guides the shopper to browse the required commodities in a short time through the shopping navigation route; when selecting commodities, it generates a real-time shopping recommendation list according to the required commodities, displays the recent shopping sales situation of the commodities, and marks the customer satisfaction of each commodity for the shopper to refer to.

[0073] The setting of the size of the interval and the threshold is for the convenience of comparison. Regarding the size of the threshold, it depends on the amount of sample data and the base quantity set by those skilled in the art for each group of sample data; as long as the proportional relationship between the parameter and the quantized value is not affected.

[0074] The above formulas are all dimensionless and take their numerical calculations. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0075] In the two embodiments provided in this application, it should be understood that the disclosed devices and systems can be implemented in other ways; for example, the device embodiments described above are only illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there can be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed; another point, the displayed or discussed coupling or direct coupling or communication connection with each other can be through some interfaces. The indirect coupling or communication connection of the devices or modules can be in an electrical, mechanical or other form.

[0076] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution of the present invention and its inventive concept, makes equivalent replacements or changes, and should be covered within the protection scope of the present invention.

Claims

1. A VR supermarket shopping system with intelligent navigation and real-time recommendation functions, characterized in that, It includes an image construction module, a demand analysis module, a navigation planning module, a product analysis module, and a product recommendation module. The image construction module is used to obtain the product information and layout inside the supermarket, integrate them according to the product information and layout, draw a 3D layout diagram of the products inside the supermarket, classify and label the product positions through different types of product information, and store the obtained product information and layout in the supermarket management terminal. The demand analysis module is used to obtain the user's shopping needs and the shopper's location through the supermarket shopping terminal, query the products in the supermarket that meet the needs according to the user's shopping needs, obtain the product location information, generate a product demand list, and send the product demand list to the navigation planning module. The navigation planning module is used for a route generation unit and a route analysis unit. The route generation unit obtains the product demand list, generates a navigation planning route according to the product positions marked in the product demand list, based on the shopper's location and the real-time product positions. The route analysis unit is used to obtain the navigation planning route, combine the number of purchased products and the product positions to get the position distances between the products, generate the time consumed between the products according to the position distances, and combine the consumed time and the shopper's position distance to generate the time required for the navigation planning route. The product analysis module is used to obtain the historical sales information of the products in the supermarket, establish a product evaluation form according to the historical sales information, record the after-sales evaluation information, sales quantity, and price of the purchased products through the product evaluation form, generate a product recommendation degree according to the after-sales evaluation information of the products, and generate a product recommendation index by combining the sales quantity and price with the product recommendation degree. The product recommendation module is used to obtain the product demand list, get the recommendation degree and sales quantity of the products according to the product demand list, and generate a product recommendation form. The product recommendation form includes product pictures, names, prices, and recommendation degrees.

2. The VR supermarket shopping system with intelligent navigation and real-time recommendation functions according to claim 1, characterized in that, The supermarket management terminal is used to manage the product information inside the supermarket in real time, including product prices, inventory, sales volume, and product information updates. It also includes a user terminal, which is used to store the user's shopping history data.

3. The VR supermarket shopping system with intelligent navigation and real-time recommendation functions according to claim 1, characterized in that, Generating the product demand list specifically includes the following: S100. The user inputs the name of the product to be purchased, judges the category to which the product belongs according to the product name, and makes a targeted query for the product according to the category. S101. Query the products that meet the needs in the category to which the product belongs, obtain a list of products that meet the requirements, and find the specific position coordinates of the products in the supermarket according to the list of products that meet the requirements. S102. Integrate the specific coordinate positions with the product information and highlight the marks in the 3D layout diagram to obtain a product demand list with specific position coordinates.

4. The VR supermarket shopping system with intelligent navigation and real-time recommendation functions according to claim 1, characterized in that, Generating a time planning route according to the order of purchased products specifically includes the following: S200. Obtain the quantity of products to be shopped according to the product demand list, and get the position distances between the products through the product quantity and product positions. S201. Obtain the position distances between the products, calculate the time required to consume according to the position distances, and combine the consumed time with the shopper's position distance to get the time required for the navigation planning route. S202. When selecting the position distance between the shopper's location and the product, the point closest to the shopper's location is intercepted as the starting point of the purchased product.

5. The VR supermarket shopping system with intelligent navigation and real-time recommendation functions according to claim 1, wherein Generating a product recommendation degree based on the product after-sales evaluation information specifically includes the following: S300. Obtain the sales volume of a product within a certain period, obtain the product after-sales evaluation information based on the sales volume, and obtain the product evaluation quantity through the product after-sales evaluation information, including the number of good reviews and the number of bad reviews; S301. Based on the product evaluation quantity a and the number of good reviews s, combined with the sales volume x, obtain the recommendation degree q, where In the formula, The evaluation quantity is square-rooted to make the influence of the evaluation quantity not too prominent. is the favorable comment rate, ln(x + 1) is the natural logarithm of the sales quantity, and the sales quantity reflects the market acceptance of the product. S302. Generate the recommendation degree of each product within the period through the after-sales evaluation information of each product, and the recommendation degree is updated in real time according to the sales volume of the product.

6. The VR supermarket shopping system with intelligent navigation and real-time recommendation functions according to claim 1, characterized in that, Generating a product recommendation index in combination with the product recommendation degree specifically includes the following: S400. Obtain the sales volume x and the product price j of the product, and calculate the product recommendation index d of each product within the period in combination with the product recommendation degree: Preliminary calculation of the relationship t between the commodity price and the sales quantity: where w1 is a weight coefficient used to adjust the relationship between the sales quantity and the commodity price, which is the average value of the sales quantity of the commodity over a past period of time; S401. The product recommendation index generated in different periods is different, and it is updated in real time with the period as the time segment.

7. The VR supermarket shopping system with intelligent navigation and real-time recommendation functions according to claim 1, characterized in that, When the product recommendation module displays the product recommendation list, it is displayed in the form of virtual cards, including key information such as product pictures, names, and prices. Users can click on the card to view the product details and directly add them to the shopping list. Different product information is displayed through different colors, charts, and label elements.

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