Self-service vending machine with automatic replenishment function

Abstract

The invention belongs to the field of self-service replenishment, and discloses an automatic replenishment self-service vending machine which monitors inventory and sales data of all goods in the vending machine in real time through a goods data real-time monitoring module. The automatic replenishment triggering module sends a replenishment signal to the background management system according to the real-time stock quantity monitored by the real-time cargo data monitoring module, the replenishment process is triggered, real-time monitoring and automatic replenishment of the commodity stock in the vending machine are achieved, and therefore the operation efficiency is improved, the labor cost is reduced, and the product quality is improved. Meanwhile, the replenishment time selection module analyzes the real-time sales quantity monitored by the goods data real-time monitoring module to obtain the optimal replenishment time period every day, it is guaranteed that the automatic replenishment process is not affected, and the automatic replenishment efficiency is improved. Meanwhile, the intelligent demand prediction module analyzes the data monitored by the cargo data real-time monitoring module, adjusts the replenishment threshold value of each cargo, and ensures sufficient supply of the commodities in any time period.

Description

Technical Field

[0001] The invention belongs to the field of self-service replenishment, and in particular relates to a self-service vending machine with automatic replenishment. Background Art

[0002] Traditional self-service vending machines mostly use manual replenishment mode, which not only increases operating costs, but also may lead to problems such as untimely replenishment and product shortages. At the same time, during the replenishment process, due to improper operation or mechanical failure, it is easy to damage the goods or affect the normal operation of the vending machine. Therefore, the development of a self-service vending machine that can automatically replenish is of great significance to improve sales efficiency and reduce operating costs.

[0003] Most of the existing self-service vending machines with automatic replenishment count the inventory of goods. When the inventory of goods falls below the set threshold, they will replenish the goods. However, the sales volume of different goods fluctuates, and factors such as data, weather, and holidays can have an impact. If they are always compared with a unified threshold, out-of-stock situations may occur. Summary of the invention

[0004] The purpose of the present invention is to provide a self-service vending machine with automatic replenishment to solve the problems faced in the above-mentioned background technology.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A self-service vending machine with automatic replenishment, the system comprising a goods data real-time monitoring module, an automatic trigger replenishment module, a replenishment time selection module and an intelligent demand prediction module; The goods data real-time monitoring module is used to monitor the real-time sales quantity and real-time inventory quantity of each type of goods in the vending machine; The automatic replenishment triggering module is used to send a replenishment signal to the backend management system according to the real-time inventory quantity monitored by the goods data real-time monitoring module to trigger the replenishment process; The replenishment time selection module is used to analyze the real-time sales quantity monitored by the goods data real-time monitoring module to obtain the best replenishment time period every day; The intelligent demand prediction module is used to analyze the data monitored by the cargo data real-time monitoring module and predict the demand for each cargo.

[0006] As a further description of the solution of the present invention, the working process of the cargo data real-time monitoring module includes: Number the different goods in the vending machine, 1, 2, ..., n in sequence; Get the inventory quantity change curve of different goods over time in the historical period; Divide each day into 12 time periods, which are: , , …, ; Get the real-time sales quantity of different goods in different time periods of each day during the historical time period.

[0007] As a further description of the solution of the present invention, the working process of the automatic triggering replenishment module includes: The inventory of different goods is obtained in real time. When the inventory of the i-th goods is lower than the preset inventory threshold, a replenishment signal is immediately sent to the background management system to trigger the replenishment process.

[0008] As a further description of the solution of the present invention, the working process of the replenishment time selection module includes: Get the number of goods of type i in the historical time period. The daily sales volume in a time period is constructed to construct the sales volume of the i-th type of goods in the The function of daily sales volume changing with time in a time period , and fit the i-th cargo in the coordinate system The daily sales volume changes over time in a time period, and obtain the sales volume of the i-th product in the The area enclosed by the daily sales volume variation curve and the x-axis in a time period ; Obtain the area enclosed by the daily sales volume change curve of the i-th commodity in 12 time periods and the x-axis in the historical time period, and record them as , , …, , …, ; Will - Arrange them in ascending order, obtain the time periods corresponding to the top x items as the optimal replenishment time period for the i-th item, and recommend replenishing the i-th item in these x time periods; Among them, i belongs to [1,n], belong[ , ].

[0009] As a further description of the solution of the present invention, the working process of the replenishment time selection module also includes: The optimal replenishment time periods for n types of goods are obtained in turn, and then the optimal replenishment time periods for the n goods are counted, and the y time periods with the highest frequency are selected as the unified replenishment time periods for each day.

[0010] As a further description of the solution of the present invention, the working process of the intelligent demand prediction module includes: Get the daily sales volume change data Q of the i-th commodity in the historical period, and fit the sales pointer function of the i-th commodity ; The pointer function for selling the i-th type of goods And the preset i-th type of goods sales pointer function Compare and calculate the demand forecast index of the i-th commodity : ; In the formula, t is the time, according to the demand forecast index of the i-th commodity Adjust the preset inventory threshold of the i-th type of goods.

[0011] As a further description of the solution of the present invention, the process of adjusting the preset inventory threshold of the i-th type of goods includes: Will and preset indicator threshold range Compare, when belong When , the preset inventory threshold of the current i-th commodity remains unchanged; when Less than When , the preset inventory threshold of the current i-th commodity is reduced; when Greater than , increase the preset inventory threshold of the current i-th type of goods.

[0012] As a further description of the solution of the present invention, the working process of the intelligent demand prediction module also includes: Demand forecast indicators for n types of goods are obtained in sequence, and according to the comparison between the demand forecast indicators for the n types of goods and the corresponding preset indicator threshold ranges, it is determined whether the preset inventory thresholds for all goods need to be adjusted.

[0013] Beneficial effects of the present invention: The goods data real-time monitoring module of the present invention monitors the inventory and sales data of all goods in the vending machine in real time, and automatically triggers the replenishment module to send a replenishment signal to the background management system through the real-time inventory quantity monitored by the goods data real-time monitoring module, triggering the replenishment process, thereby realizing real-time monitoring and automatic replenishment of the commodity inventory in the vending machine, thereby improving operational efficiency, reducing labor costs, and ensuring that customers can purchase the required commodities at any time. At the same time, the replenishment time selection module analyzes the real-time sales quantity monitored by the goods data real-time monitoring module to obtain the best daily replenishment time period, ensuring that the automatic replenishment process is not affected. At the same time, the intelligent demand prediction module analyzes the data monitored by the goods data real-time monitoring module, adjusts the replenishment threshold of each commodity, and ensures that the commodity supply is sufficient in any time period.

[0014] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0016] Figure 1 This is a schematic diagram of the structure of a self-service vending machine with automatic replenishment according to the present invention. DETAILED DESCRIPTION

[0017] 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.

[0018] See also Figure 1 As shown, a self-service vending machine with automatic replenishment is disclosed, wherein the system includes a goods data real-time monitoring module, an automatic trigger replenishment module, a replenishment time selection module and an intelligent demand prediction module; The goods data real-time monitoring module is used to monitor the real-time sales quantity and real-time inventory quantity of each type of goods in the vending machine; The automatic replenishment triggering module is used to send a replenishment signal to the backend management system according to the real-time inventory quantity monitored by the goods data real-time monitoring module to trigger the replenishment process; The replenishment time selection module is used to analyze the real-time sales quantity monitored by the goods data real-time monitoring module to obtain the best replenishment time period every day; The intelligent demand prediction module is used to analyze the data monitored by the cargo data real-time monitoring module and predict the demand for each cargo.

[0019] Through the above technical scheme, the real-time monitoring module of goods data of the present invention monitors the inventory and sales data of all goods in the vending machine in real time, and automatically triggers the replenishment module to send a replenishment signal to the background management system through the real-time inventory quantity monitored by the real-time monitoring module of goods data, triggering the replenishment process, thereby realizing real-time monitoring and automatic replenishment of the inventory of goods in the vending machine, thereby improving operational efficiency, reducing labor costs, and ensuring that customers can purchase the required goods at any time. At the same time, the replenishment time selection module analyzes the real-time sales quantity monitored by the real-time monitoring module of goods data to obtain the best daily replenishment time period to ensure that the automatic replenishment process is not affected. At the same time, the intelligent demand prediction module analyzes the data monitored by the real-time monitoring module of goods data and adjusts the replenishment threshold of each commodity to ensure sufficient supply of goods in any time period.

[0020] As a further description of the solution of the present invention, the working process of the cargo data real-time monitoring module includes: Number the different goods in the vending machine, 1, 2, ..., n in sequence; Get the inventory quantity change curve of different goods over time in the historical period; Divide each day into 12 time periods, which are: , , …, ; Get the real-time sales quantity of different goods in different time periods of each day during the historical time period.

[0021] As a further description of the solution of the present invention, the working process of the automatic triggering replenishment module includes: The inventory of different goods is obtained in real time. When the inventory of the i-th goods is lower than the preset inventory threshold, a replenishment signal is immediately sent to the background management system to trigger the replenishment process.

[0022] Through the above technical solution, this embodiment compares the inventory quantity of different goods with the corresponding inventory threshold. When the inventory quantity of a certain product is lower than the preset inventory threshold, a replenishment signal is immediately sent to the background management system. The vending machine grabs the goods from the replenishment area and transports them to the corresponding aisle via a conveyor belt to complete the replenishment operation.

[0023] As a further description of the solution of the present invention, the working process of the replenishment time selection module includes: Get the number of goods of type i in the historical time period. The daily sales volume in a time period is constructed to construct the sales volume of the i-th type of goods in the The function of daily sales volume changing with time in a time period , and fit the i-th cargo in the coordinate system The daily sales volume changes over time in a time period, and obtain the sales volume of the i-th product in the The area enclosed by the daily sales volume variation curve and the x-axis in a time period ; Obtain the area enclosed by the daily sales volume change curve of the i-th commodity in 12 time periods and the x-axis in the historical time period, and record them as , , …, , …, ; Will - Arrange them in ascending order, obtain the time periods corresponding to the top x items as the optimal replenishment time period for the i-th item, and recommend replenishing the i-th item in these x time periods; Among them, i belongs to [1,n], belong[ , ].

[0024] As a further description of the solution of the present invention, the working process of the replenishment time selection module also includes: The optimal replenishment time periods for n kinds of goods are obtained in turn, and then the optimal replenishment time periods for the n goods are counted, and the y time periods with the highest frequency are selected as the unified replenishment time periods for each day.

[0025] Through the above technical solution, this embodiment divides the time of each day into 12 time periods, and obtains the area enclosed by the daily sales volume change curve of the i-th type of goods in the 12 time periods and the x-axis in the historical time period. - ,Will - Arrange them in ascending order, obtain the time periods corresponding to the top x as the optimal replenishment time periods for the i-th product, and recommend replenishing the i-th product in these x time periods. Then obtain the optimal replenishment time periods for n types of products in turn, and then count the optimal replenishment time periods for the n products. Select the y time periods with the highest frequency as the unified replenishment time periods for each day to ensure that the number of products in the replenishment area meets the replenishment demand.

[0026] As a further description of the solution of the present invention, the working process of the intelligent demand prediction module includes: Get the daily sales volume change data Q of the i-th commodity in the historical period, and fit the sales pointer function of the i-th commodity ; The pointer function for selling the i-th type of goods And the preset i-th type of goods sales pointer function Compare and calculate the demand forecast index of the i-th commodity : ; In the formula, t is the time, according to the demand forecast index of the i-th commodity Adjust the preset inventory threshold of the i-th type of goods.

[0027] As a further description of the solution of the present invention, the process of adjusting the preset inventory threshold of the i-th type of goods includes: Will and preset indicator threshold range Compare, when belong When , the preset inventory threshold of the current i-th commodity remains unchanged; when Less than When , the preset inventory threshold of the current i-th commodity is reduced; when Greater than , increase the preset inventory threshold of the current i-th type of goods.

[0028] As a further description of the solution of the present invention, the working process of the intelligent demand prediction module also includes: Demand forecast indicators for n types of goods are obtained in sequence, and according to the comparison between the demand forecast indicators for the n types of goods and the corresponding preset indicator threshold ranges, it is determined whether the preset inventory thresholds for all goods need to be adjusted.

[0029] Through the above technical solution, this embodiment obtains the daily sales volume change data Q of the i-th commodity in the historical time period, and fits the sales pointer function of the i-th commodity , and then by the formula Calculate the demand forecast index for the i-th type of goods ,Will and preset indicator threshold range Compare, when belong When , the preset inventory threshold of the current i-th commodity remains unchanged; when Less than When , the preset inventory threshold of the current i-th commodity is reduced; when Greater than When the preset inventory threshold of the current i-th type of goods is increased, the replenishment threshold of each commodity can be adjusted in real time according to the actual situation to ensure sufficient supply of goods in any time period.

[0030] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A self-service vending machine with automatic replenishment, characterized in that: The system includes a cargo data real-time monitoring module, an automatic trigger replenishment module, a replenishment time selection module and an intelligent demand prediction module; The goods data real-time monitoring module is used to monitor the real-time sales quantity and real-time inventory quantity of each type of goods in the vending machine; The automatic replenishment triggering module is used to send a replenishment signal to the backend management system according to the real-time inventory quantity monitored by the goods data real-time monitoring module to trigger the replenishment process; The replenishment time selection module is used to analyze the real-time sales quantity monitored by the goods data real-time monitoring module to obtain the best replenishment time period every day; The intelligent demand prediction module is used to analyze the data monitored by the cargo data real-time monitoring module and predict the demand for each cargo.

2. The automatic replenishment vending machine according to claim 1, characterized in that: The working process of the cargo data real-time monitoring module includes: Number the different goods in the vending machine, 1, 2, ..., n in sequence; Get the inventory quantity change curve of different goods over time in the historical period; Divide each day into 12 time periods, which are: , , …, ; Get the real-time sales quantity of different goods in different time periods of each day during the historical time period.

3. The automatic replenishment vending machine according to claim 2, characterized in that: The working process of the automatic trigger replenishment module includes: The inventory of different goods is obtained in real time. When the inventory of the i-th goods is lower than the preset inventory threshold, a replenishment signal is immediately sent to the background management system to trigger the replenishment process.

4. The automatic replenishment vending machine according to claim 2, characterized in that: The working process of the replenishment time selection module includes: Get the number of goods of type i in the historical time period. The daily sales volume in a time period is constructed to construct the sales volume of the i-th type of goods in the The function of daily sales volume changing with time in a time period , and fit the i-th cargo in the coordinate system The daily sales volume changes over time in a time period, and obtain the sales volume of the i-th product in the The area enclosed by the daily sales volume variation curve and the x-axis in a time period ; Obtain the area enclosed by the daily sales volume change curve of the i-th commodity in 12 time periods and the x-axis in the historical time period, and record them as , , …, , …, ; Will - Arrange them in ascending order, obtain the time periods corresponding to the top x items as the optimal replenishment time period for the i-th item, and recommend replenishing the i-th item in these x time periods; Among them, i belongs to [1,n], belong[ , ].

5. The automatic replenishment vending machine according to claim 4, characterized in that: The working process of the replenishment time selection module also includes: The optimal replenishment time periods for n kinds of goods are obtained in turn, and then the optimal replenishment time periods for the n goods are counted, and the y time periods with the highest frequency are selected as the unified replenishment time periods for each day.

6. The automatic replenishment self-service vending machine according to claim 5, characterized in that: The working process of the intelligent demand prediction module includes: Get the daily sales volume change data Q of the i-th commodity in the historical period, and fit the sales pointer function of the i-th commodity ; The pointer function for selling the i-th type of goods And the preset i-th type of goods sales pointer function Compare and calculate the demand forecast index of the i-th commodity : ； In the formula, t is the time, according to the demand forecast index of the i-th commodity Adjust the preset inventory threshold of the i-th type of goods.

7. The automatic replenishment self-service vending machine according to claim 6, characterized in that: The process of adjusting the preset inventory threshold of the i-th type of goods includes: Will and preset indicator threshold range Compare, when belong When , the preset inventory threshold of the current i-th commodity remains unchanged; when Less than When , the preset inventory threshold of the current i-th commodity is reduced; when Greater than , increase the preset inventory threshold of the current i-th type of goods.

8. The automatic replenishment vending machine according to claim 7, characterized in that: The working process of the intelligent demand prediction module also includes: Demand forecast indicators for n types of goods are obtained in sequence, and according to the comparison between the demand forecast indicators for the n types of goods and the corresponding preset indicator threshold ranges, it is determined whether the preset inventory thresholds for all goods need to be adjusted.

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