Automatic replenishment reminding method, system, medium and equipment for vending machine

Abstract

The invention discloses an automatic replenishment reminding method and system for a vending machine, a medium and equipment. The automatic replenishment reminding method comprises the following steps: a data acquisition step: acquiring inventory data, sales data and commodity shelf life data in the vending machine in real time; a data transmission step: transmitting the collected data to a cloud server through a wireless communication network; an output storage step: storing the collected inventory data, sales data, shelf life data and user purchase behavior data in a database; a data analysis step: performing inventory optimization calculation and identification of products difficult to sell through the inventory data, the sales data, the shelf life data and the user purchase behavior data; according to the goods replenishment reminding method and system, real-time monitoring and intelligent management of the goods stock in the vending machine can be achieved, the goods replenishment efficiency and accuracy are greatly improved, and therefore the future sales trend is accurately predicted, the stock structure is optimized, and the phenomenon of goods overstock or stockout is avoided.

Description

Technical Field

[0001] The present invention relates to the field of vending machines, and particularly to an automatic replenishment reminder method, system, medium and device for vending machines. Background Art

[0002] Currently, most vending machines on the market adopt the method of manually checking the inventory of goods regularly and replenishing them. This method has many inconveniences. For example, manual inventory checking requires a lot of time and manpower, and it is often impossible to achieve real-time monitoring, resulting in the vending machine being unable to replenish goods in time after the goods are sold out, affecting the shopping experience of customers. In addition, manual inventory checking is prone to errors, resulting in inaccurate replenishment, further affecting the operation efficiency of the vending machine and customer satisfaction. Therefore, developing a vending machine method that can automatically remind of replenishment is of great significance for improving the operation efficiency of vending machines and enhancing customer satisfaction. Summary of the Invention

[0003] The technical problem solved by the present invention is to provide an automatic replenishment reminder method for vending machines that can realize automatic replenishment reminder and remove unsalable products from the shelves.

[0004] The technical solution adopted by the present invention to solve its technical problems is: an automatic replenishment reminder method for vending machines, including the following steps:

[0005] Data acquisition step: Real-time collect inventory data, sales data and product shelf life data in the vending machine;

[0006] Data transmission step: Transmit the collected data to the cloud server through a wireless communication network;

[0007] Output storage step: The collected inventory data, sales data, shelf life data and user purchase behavior data are stored in the database;

[0008] Data analysis step: Perform inventory optimization calculations and identify products with difficult sales through inventory data, sales data, shelf life data and user purchase behavior data;

[0009] Replenishment reminder step: Generate a replenishment reminder according to the analysis result and notify the replenishment personnel by text message, email or mobile App.

[0010] Furthermore: In the data analysis step, specifically:

[0011] Sales prediction step: Based on historical sales data, weather data, holiday data and nearby event data, use a time series model or a deep learning model to predict future sales trends;

[0012] Inventory optimization step: Use an optimization algorithm to calculate the optimal replenishment quantity;

[0013] Shelf life monitoring steps: Calculate the remaining shelf life of the product in real time and issue reminders or automatically remove products that are nearing their expiration date;

[0014] Steps for identifying products that are difficult to sell: Based on sales data and inventory data, identify products that are difficult to sell and directly remove them from the shelves.

[0015] Furthermore, the step of identifying products that are difficult to sell specifically includes the following steps:

[0016] Calculate product sales rate: Based on historical sales data and inventory data, calculate the sales rate of each product. The formula is:

[0017] Sales rate = (sales quantity / total quantity) × 100%;

[0018] Set sales rate thresholds: Set sales rate thresholds based on product categories and sales cycles. If a product's sales rate is lower than the threshold, it will be considered a difficult-to-sell product.

[0019] Automatic removal from shelves: Products that are difficult to sell are automatically removed from shelves, and the replenishment staff are notified to replace the products through the replenishment reminder module.

[0020] Furthermore, the steps of optimizing product locations are also included, specifically:

[0021] Automatically identify product types and quantities through cameras and computer vision technology;

[0022] Analyze the time users spend in front of vending machines and the types of products they browse to obtain the most popular product categories;

[0023] Place high-profile product categories in prominent locations on vending machines.

[0024] Furthermore, the voice interaction step is also included:

[0025] Voice command reception: Receive user's voice commands through the microphone on the vending machine;

[0026] Speech recognition: Use speech recognition technology to convert voice commands into text;

[0027] Instruction execution: Execute corresponding operations according to the identified instructions;

[0028] Voice feedback: Provides feedback of operation results to users through speech synthesis technology.

[0029] Furthermore, in the replenishment reminder step, specifically:

[0030] Generate replenishment requirements based on sales forecasts and inventory optimization algorithms, and notify replenishment personnel via SMS, email, or mobile app;

[0031] Use an intelligent path planning algorithm to plan the optimal replenishment path for the replenishment personnel, and provide feedback on the replenishment structure after replenishment is completed.

[0032] The present invention also discloses a vending machine automatic reminder replenishment system, including a data collection step module, a data transmission module, an output storage module, a data analysis module, and a replenishment reminder module;

[0033] The data collection module is used to collect inventory data, sales data, and product shelf life data in the vending machine in real time;

[0034] The data transmission module is used to transmit the collected data to the cloud server through a wireless communication network;

[0035] The output storage module is used to store the collected inventory data, sales data, shelf life data, and user purchase behavior data in a database;

[0036] The data analysis module is used to perform inventory optimization calculations and identify difficult-to-sell products through inventory data, sales data, shelf life data, and user purchase behavior data;

[0037] The replenishment reminder module is used to generate a replenishment reminder based on the analysis result, and notify the replenishment personnel through text message, email, or mobile App.

[0038] Furthermore: In the data analysis module, there is also included:

[0039] Sales prediction unit: Based on historical sales data, weather data, holiday data, and nearby event data, use a time series model or a deep learning model to predict future sales trends;

[0040] Inventory optimization unit: Use an optimization algorithm to calculate the optimal replenishment quantity;

[0041] Shelf life monitoring unit: Calculate the remaining shelf life of the product in real time, and remind or automatically remove products approaching the expiration date;

[0042] Difficult-to-sell product identification unit: Based on sales data and inventory data, identify products that are difficult to sell and directly remove them from the shelves.

[0043] The present invention also discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned vending machine automatic reminder replenishment method are realized.

[0044] The present invention also discloses a computer device, including a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the communication bus; among them:

[0045] The memory is used to store computer programs;

[0046] The processor is configured to execute the steps of the vending machine automatic replenishment reminder method described above by running the programs stored on the memory.

[0047] The beneficial effects of the present invention are as follows:

[0048] 1. Through the setting of the data analysis step in the present invention, real-time monitoring and intelligent management of the commodity inventory in the vending machine can be achieved, greatly improving the replenishment efficiency and accuracy, thereby accurately predicting future sales trends, optimizing the inventory structure, and avoiding the occurrence of overstocking or out-of-stock phenomena.

[0049] 2. In the present invention, products with difficult sales can be identified, and such products can be taken off the shelves in a timely manner, and the replenishment personnel can be notified through the replenishment reminder module to replace them with popular products, effectively improving the operation efficiency of the vending machine and customer satisfaction.

[0050] 3. Through the setting of the commodity position optimization step in the present invention, the camera and computer vision technology are used to automatically identify the types and quantities of commodities, analyze the behavior of users in front of the vending machine, and place products with high attention at prominent positions in the vending machine, improving the sales rate of commodities and the purchase experience of customers. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic flowchart of the vending machine automatic replenishment reminder method according to an embodiment of the present application.

[0052] Figure 2 It is a schematic flowchart of the vending machine automatic replenishment reminder method according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following detailed description of the specific embodiments of the present invention will be given with reference to the accompanying drawings.

[0054] As Figure 1 shown, an embodiment of the present application discloses a vending machine automatic replenishment reminder method, including the following steps:

[0055] Data collection step: Real-time collect the inventory data, sales data, and commodity shelf life data in the vending machine;

[0056] Data transmission step: Transmit the collected data to the cloud server through a wireless communication network;

[0057] Output storage step: The collected inventory data, sales data, shelf life data, and user purchase behavior data are stored in the database;

[0058] Data analysis steps: Through inventory data, sales data, shelf life data, and user purchase behavior data, perform inventory optimization calculations and identify products with difficult sales.

[0059] Replenishment reminder steps: Generate replenishment reminders based on the analysis results, and notify replenishment personnel via text messages, emails, or mobile apps.

[0060] Specifically, first, use the sensors and scanning devices built into the vending machine to obtain real-time inventory data, sales data, and product shelf life data inside the vending machine. These data include key information such as the quantity of each product, sales volume, sales time, and the shelf life of the product. Then, use the wireless communication module built into the vending machine, such as 4G, 5G, or Wi-Fi, to transmit the collected data to the cloud server in real time for subsequent data analysis and processing. Then, all the collected data is recorded in the database. At the same time, the database also records user purchase behavior data, including purchase time, types and quantities of purchased products, etc., providing data support for subsequent sales forecasting and inventory optimization. Then, the data analysis step is the core part of the present invention. By comprehensively analyzing inventory data, sales data, shelf life data, and user purchase behavior data, inventory optimization and identification of products with difficult sales are achieved. The replenishment reminder step generates replenishment reminders based on the results of the data analysis step and notifies replenishment personnel via text messages, emails, or mobile apps. The replenishment personnel replenish the vending machine in a timely manner according to the replenishment reminder to ensure that the types and quantities of products in the vending machine meet customer needs.

[0061] In the present invention, through the setting of the data analysis step, real-time monitoring and intelligent management of the product inventory in the vending machine can be realized, greatly improving the replenishment efficiency and accuracy, thereby accurately predicting future sales trends, optimizing the inventory structure, and avoiding the occurrence of overstocking or out-of-stock phenomena.

[0062] In this embodiment, in the data analysis step, specifically:

[0063] Sales forecasting step: Based on historical sales data, weather data, holiday data, and nearby event data, use time series models or deep learning models to predict future sales trends.

[0064] Inventory optimization step: Use optimization algorithms to calculate the optimal replenishment quantity.

[0065] Shelf life monitoring step: Calculate the remaining shelf life of the product in real time, and remind or automatically remove products approaching the expiration date.

[0066] Identification step of products with difficult sales: Based on sales data and inventory data, identify products with difficult sales and directly remove them from the shelves.

[0067] Specifically, by using historical sales data, weather data, holiday data, and nearby event data, and through advanced time series models or deep learning models, the sales trends for a period of time in the future are predicted. This helps the replenishment staff to understand the sales demand in advance and thus make more reasonable replenishment arrangements.

[0068] When optimizing inventory, based on the current inventory situation and the sales forecast results, the optimal replenishment quantity is calculated. This step can avoid the waste of resources caused by inventory backlog and ensure that the products in the vending machine are always sufficient to meet customer needs.

[0069] When conducting shelf life monitoring, the remaining shelf life of the products is monitored in real time, and reminders or automatic removal of products approaching expiration are carried out. This step can effectively avoid losses caused by expired products and improve the shopping experience of customers at the same time.

[0070] Next, by analyzing sales data and inventory data, products with difficult sales are identified and directly removed from the shelves. This can avoid slow-moving products occupying the space in the vending machine, and at the same time, replace them with popular products in a timely manner to improve the operation efficiency of the vending machine and customer satisfaction.

[0071] In this embodiment, the identification of products with difficult sales specifically includes the following steps:

[0072] Calculate the sales rate of products: Based on historical sales data and inventory data, calculate the sales rate of each product. The formula is:

[0073] Sales rate = (number of sales / total quantity) × 100%;

[0074] Set the sales rate threshold: According to the product category and sales cycle, set the sales rate threshold. If the sales rate of a product is lower than the threshold, it is determined as a product with difficult sales;

[0075] Automatic removal processing: Automatically remove products with difficult sales from the shelves and notify the replenishment staff to replace the products through the replenishment reminder module.

[0076] Specifically, the above threshold can be customized. The sales cycle can be set to one week or one month. For example, for food products, the sales rate threshold can be set to 70%. If the sales rate of a certain food product is lower than 70%, it is determined as a product with difficult sales; for daily necessities, the sales rate threshold can be set to 40%. If the sales rate of a certain daily necessity product is lower than 40%, it is determined as a product with difficult sales. Through such settings, products with difficult sales can be identified more accurately and removed from the shelves in a timely manner, avoiding slow-moving products occupying the space in the vending machine and improving the operation efficiency of the vending machine at the same time.

[0077] In this embodiment, it further includes a step of optimizing the positions of commodities, specifically:

[0078] Automatically identify the types and quantities of commodities through cameras and computer vision technology;

[0079] Analyze the staying time of the user in front of the vending machine and the types of commodities browsed to obtain the types of products with high attention;

[0080] Place the types of products with high attention at prominent positions in the vending machine.

[0081] Specifically, through the camera above the vending machine, the situation of the commodities in the vending machine is monitored in real time, that is: the types and quantities of each commodity. At the same time, multiple external cameras can take pictures of the user in real time to obtain the staying time of the user in front of the vending machine and the types of commodities browsed, so as to obtain the types of products with high attention. Based on this information, the system can automatically adjust the positions of the commodities and place the types of products with high attention at prominent positions in the vending machine, such as the middle layer of the vending machine or the position close to the exit, so as to attract the attention of customers and improve the sales rate of commodities.

[0082] In this embodiment, it further includes a voice interaction step:

[0083] Receiving voice commands: Receive the voice commands of the user through the microphone on the vending machine;

[0084] Voice recognition: Use voice recognition technology to convert the voice commands into text;

[0085] Executing commands: Execute corresponding operations according to the recognized commands;

[0086] Voice feedback: Feedback the operation results to the user through voice synthesis technology.

[0087] Specifically, in this system, the buying and selling operations of goods can be realized through voice control. The user only needs to say the name or number of the commodity to be purchased through the microphone on the vending machine, and the system can automatically identify and execute the purchase operation. For example, the user can say: "I want to buy a bottle of cola", and the system can recognize this command and take out the corresponding cola from the vending machine for the user to pick up. After the purchase is completed, the system will also feedback the operation results to the user through voice synthesis technology, such as: "The purchase is successful. Please pick up your commodity". Such a voice interaction method not only simplifies the purchase process but also improves the shopping experience of the user.

[0088] In this embodiment, in the replenishment reminder step, specifically:

[0089] Generate replenishment requirements according to the sales forecast results and inventory optimization algorithms, and notify the replenishment personnel through text messages, emails or mobile apps;

[0090] Use an intelligent path planning algorithm to plan the optimal replenishment path for the replenishment staff, and provide feedback on the replenishment structure after replenishment is completed.

[0091] Specifically, when replenishment is required, the system will automatically generate replenishment requirements based on the sales forecast results and inventory optimization algorithms. These requirements will detail the types, quantities, and replenishment priorities of the products to be replenished. Subsequently, the system will promptly notify the replenishment staff through various means such as text messages, emails, or mobile apps. To further improve the replenishment efficiency, the system will also use an intelligent path planning algorithm to plan the optimal replenishment path for the replenishment staff, ensuring that they can complete the replenishment task at the fastest speed. After replenishment is completed, the replenishment staff can provide feedback on the replenishment results through the system, enabling the management to keep track of the replenishment progress in real time and ensuring that the products in the vending machine are always sufficient.

[0092] The present invention also discloses an automatic replenishment reminder system for a vending machine, including a data collection step module, a data transmission module, an output storage module, a data analysis module, and a replenishment reminder module;

[0093] The data collection module is used to collect inventory data, sales data, and product shelf life data in the vending machine in real time;

[0094] The data transmission module is used to transmit the collected data to the cloud server through a wireless communication network;

[0095] The output storage module is used to store the collected inventory data, sales data, shelf life data, and user purchase behavior data in a database;

[0096] The data analysis module is used to perform inventory optimization calculations and identify products with difficult sales through inventory data, sales data, shelf life data, and user purchase behavior data;

[0097] The replenishment reminder module is used to generate a replenishment reminder based on the analysis results and notify the replenishment staff through text messages, emails, or mobile apps.

[0098] Specifically, in the data analysis module, there is also included:

[0099] A sales forecast unit: Based on historical sales data, weather data, holiday data, and nearby event data, use a time series model or a deep learning model to predict future sales trends;

[0100] An inventory optimization unit: Use an optimization algorithm to calculate the optimal replenishment quantity;

[0101] A shelf life monitoring unit: Calculate the remaining shelf life of products in real time and remind or automatically remove products approaching their expiration dates;

[0102] Difficult-to-sell product identification unit: Based on sales data and inventory data, identify products that are difficult to sell and directly remove them from the shelves.

[0103] In the present invention, through the setting of the data analysis step, real-time monitoring and intelligent management of the commodity inventory in the vending machine can be achieved, greatly improving the replenishment efficiency and accuracy, thereby accurately predicting future sales trends, optimizing the inventory structure, and avoiding the occurrence of overstocking or out-of-stock phenomena. At the same time, in the present invention, products that are difficult to sell can be identified, and such products can be removed from the shelves in a timely manner, and the replenishment personnel can be notified through the replenishment reminder module to replace them with best-selling products, effectively improving the operation efficiency of the vending machine and customer satisfaction.

[0104] The present invention also discloses a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the vending machine automatic reminder replenishment method described above are realized.

[0105] In addition, the computer-readable storage medium of this embodiment can adopt any combination of one or more readable storage media, where the readable storage medium includes systems, devices or components of electricity, light, electromagnetism, infrared rays or semiconductors, or any combination of the above.

[0106] The present invention also discloses a computer device, including a processor, a communication interface, a memory and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the communication bus; where:

[0107] The memory is used to store a computer program;

[0108] The processor is used to execute the steps of the vending machine automatic reminder replenishment method described above by running the program stored on the memory.

[0109] As an implementation manner of the present invention, the communication bus mentioned in the above terminal may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into an address bus, a data bus, a control bus, etc.

[0110] As an implementation manner of the present invention, the communication interface is used for communication between the above terminal and other devices.

[0111] As an implementation manner of the present invention, the memory may include a random access memory (RAM), or may also include a non-volatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located far from the aforementioned processor.

[0112] As an implementation manner of the present invention, the aforementioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0113] The above specific embodiments have further elaborated on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An automatic reminder method for replenishing goods in a vending machine, characterized in that, The steps include: Data collection steps: Real-time collection of inventory data, sales data, and product shelf life data in vending machines; Data transmission step: the collected data is transmitted to the cloud server via a wireless communication network; Output storage step: The collected inventory data, sales data, shelf life data, and user purchase behavior data are stored in the database; Data analysis steps: Use inventory data, sales data, shelf life data, and user purchasing behavior data to perform inventory optimization calculations and identify products that are difficult to sell; Replenishment reminder step: Generate a replenishment reminder based on the analysis results and notify the replenishment personnel via SMS, email or mobile app.

2. The method for automatically reminding replenishment of a vending machine according to claim 1, characterized in that: The data analysis step specifically includes: Sales forecasting step: Based on historical sales data, weather data, holiday data, and nearby event data, use time series models or deep learning models to predict future sales trends; Inventory optimization steps: Use optimization algorithms to calculate the optimal replenishment quantity; Shelf life monitoring steps: Calculate the remaining shelf life of the product in real time and issue reminders or automatically remove products that are nearing their expiration date; Steps for identifying products that are difficult to sell: Based on sales data and inventory data, identify products that are difficult to sell and directly remove them from the shelves.

3. The vending machine automatic reminder replenishment method according to claim 2, characterized in that, The method of identifying products that are difficult to sell specifically includes the following steps: Calculate product sales rate: Based on historical sales data and inventory data, calculate the sales rate of each product. The formula is: Sales rate = (sales quantity / total quantity) × 100%; Set sales rate thresholds: Set sales rate thresholds based on product categories and sales cycles. If a product's sales rate is lower than the threshold, it will be considered a difficult-to-sell product. Automatic delisting: Products that are difficult to sell are automatically delisted, and the replenishment staff are notified to replace the products through the replenishment reminder module.

4. The automatic reminder method for replenishing goods in a vending machine according to claim 1, wherein, It also includes steps for optimizing product locations, specifically: Automatically identify product types and quantities through cameras and computer vision technology; Analyze the time users spend in front of vending machines and the types of products they browse to obtain the most popular product categories; Place high-profile product categories in prominent locations on vending machines.

5. The method for automatically reminding a vending machine to replenish goods according to claim 1, characterized in that, Also includes voice interaction steps: Voice command reception: Receive user's voice commands through the microphone on the vending machine; Speech recognition: Use speech recognition technology to convert voice commands into text; Instruction execution: Execute corresponding operations according to the identified instructions; Voice feedback: Provides feedback of operation results to users through speech synthesis technology.

6. The automatic reminder method for replenishing goods in a vending machine according to claim 1, wherein The replenishment reminder step is specifically as follows: Generate replenishment requirements based on sales forecasts and inventory optimization algorithms, and notify replenishment personnel via SMS, email, or mobile app; Use intelligent path planning algorithms to plan the optimal replenishment path for replenishment personnel, and provide feedback on the replenishment structure after replenishment is completed.

7. Vending machine automatic replenishment reminder system, characterized in that, It includes data collection step module, data transmission module, output storage module, data analysis module and replenishment reminder module; The data collection module is used to collect inventory data, sales data and product shelf life data in the vending machine in real time; The data transmission module is used to transmit the collected data to the cloud server via the wireless communication network; The output storage module is used to store the collected inventory data, sales data, shelf life data, and user purchase behavior data in a database; The data analysis module is used to perform inventory optimization calculations and identify products with difficult sales through inventory data, sales data, shelf life data, and user purchase behavior data; The replenishment reminder module is used to generate a replenishment reminder based on the analysis results and notify the replenishment personnel via text message, email, or mobile App.

8. The automatic replenishment reminder system for vending machines according to claim 7, characterized in that: In the data analysis module, it further includes: A sales prediction unit: Based on historical sales data, weather data, holiday data, and nearby event data, use a time series model or a deep learning model to predict future sales trends; An inventory optimization unit: Use an optimization algorithm to calculate the optimal replenishment quantity; A shelf life monitoring unit: Calculate the remaining shelf life of products in real time and remind or automatically remove products approaching their expiration dates; A difficult-to-sell product identification unit: Based on sales data and inventory data, identify products with difficult sales and directly remove them from the shelves.

9. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the vending machine automatic replenishment reminder method described in any one of claims 1 to 6 are implemented.

10. A computer device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete communication with each other through the communication bus; where: The memory is used to store a computer program; The processor is used to execute the steps of the vending machine automatic replenishment reminder method described in any one of claims 1 to 6 by running the program stored on the memory.

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