Vending machine goods channel layout optimization calculation method

Abstract

The invention discloses a vending machine goods channel layout optimization calculation method, which successfully solves the problems of low precision, mismatching of buckle hole sites, low utilization rate of goods channel space and the like in the background technology by accurately calculating the number of buckle hole sites occupied by goods and combining an optimization algorithm and a big data technology. According to the vending machine, by accurately calculating the buckle hole positions occupied by the commodities, it is ensured that each commodity can be in butt joint with the buckle hole positions of the partition plates, so that the problem that the partition plates cannot be normally installed due to calculation errors is avoided, and reasonable placement of the commodities and reasonability of the structure of the vending machine are ensured. In addition, a big data technology is introduced, and by collecting and analyzing historical layout data and commodity sales data, optimizing commodity channel layout and adjusting commodity positions according to real-time data, the utilization rate of commodity channel space is effectively improved, the space of each inch can be utilized to the maximum extent, and the problem of space waste in a traditional method is avoided.

Description

Technical Field

[0001] This application relates to the technical field of optimizing the calculation of the vending machine lane layout, and particularly to a method for optimizing the calculation of the vending machine lane layout. Background Art

[0002] With the wide application of vending machines, how to effectively design the lane layout of vending machines has become a key factor in improving operation efficiency and usage convenience. The lane layout not only affects the display effect of goods, but also directly impacts the inventory capacity and maintenance convenience of vending machines. A reasonable lane layout can ensure that goods are neatly arranged, avoid space waste, and improve the usage efficiency of vending machines. Therefore, the design and optimization of the lane layout have become an important research direction in the field of vending machines.

[0003] Currently, the traditional method for designing the vending machine lane layout mainly determines the placement of goods in the lane by calculating the comparison between the width of the goods and the width of the lane. However, this traditional calculation method does not fully consider the actual distribution of the buckle holes in the lane, resulting in a mismatch between the width of the goods and the position of the partition buckle holes in some cases, thus affecting the normal use of the lane. Especially when the lane partition is installed by buckling, the calculation error of the goods width may cause the partition to be unable to be installed smoothly, further affecting the placement effect of the goods and the normal operation of the vending machine.

[0004] In addition, the width calculation methods in the existing technologies usually ignore the precise relationship between the goods and the lane layout. Especially when the distribution of the buckle holes in the lane partition is uneven, the traditional width calculation method will produce large errors. These errors may lead to difficulties in placing goods or low utilization rate of the lane space, affecting the inventory capacity of the vending machine, and thus reducing the operation efficiency of the vending machine.

[0005] Therefore, in the design of the vending machine lane layout, the traditional width calculation method has problems such as accuracy issues, inaccurate matching of buckle hole positions, and low utilization rate of the lane space, becoming a technical problem that urgently needs to be solved. Summary of the Invention

[0006] This application provides a method, device, computer device, and storage medium for optimizing the calculation of the vending machine lane layout, aiming to solve the problems such as accuracy issues, inaccurate matching of buckle hole positions, and low utilization rate of the lane space in the design of the vending machine lane layout.

[0007] In a first aspect, a method for optimizing the calculation of the vending machine lane layout, the method includes:

[0008] Step 1: Calculate the number of snap holes occupied by the merchandise, and based on the physical dimensions of the merchandise and the distribution characteristics of the snap holes, accurately determine the number of snap holes required for each piece of merchandise;

[0009] Step 2: Based on the number of snap holes occupied by the merchandise, use an optimization algorithm to adjust the layout of the lanes to maximize the utilization rate of the lane space and ensure that the merchandise can be accurately docked with the snap hole positions of the lane partitions;

[0010] Step 3: Dynamically adjust the placement position of each piece of merchandise according to the default width of the lane and the preset holes, ensuring that the lane layout achieves the best merchandise utilization rate, while considering the adaptability of the snap holes to the merchandise placement.

[0011] In the above solution, optionally, the step of calculating the number of snap holes occupied by the merchandise includes:

[0012] Step 1.1: Measure the width of each piece of merchandise, and based on the actual width of the merchandise and the distribution of the snap holes, estimate the number of snap holes occupied by the merchandise through a mathematical model to ensure that each piece of merchandise accurately occupies a reasonable space in the lane;

[0013] Step 1.2: Dynamically evaluate the dimensions of each piece of merchandise, and according to the placement situation of the merchandise in the lane, adjust the calculation method of the snap holes in real time to solve the problem of irregular dimensions caused by the shape and packaging factors of the merchandise.

[0014] In the above solution, optionally, the optimization of the lane layout is achieved by integrating big data technology and machine learning algorithms, and the method further includes:

[0015] Step 3.1: Collect and analyze the historical layout data of a large number of vending machines, and use machine learning technology to learn about the placement of the merchandise, so as to optimize the layout decision-making and ensure that the configuration of the merchandise in the lane reaches the optimal state;

[0016] Step 3.2: Through big data analysis, predict the demand for each type of merchandise, and adjust the lane layout accordingly to make the merchandise placement more in line with market demand, and further improve the storage capacity of the lane and the merchandise turnover efficiency.

[0017] In the above solution, optionally, the maximization of the utilization rate of the lane width is achieved by accurately calculating the matching of the snap hole positions between the merchandise and the lane partitions, and the method further includes:

[0018] Step 4.1: According to the physical dimensions of each piece of merchandise and the default holes of the lane, use an optimization algorithm to automatically adjust the docking position of the snap holes between the merchandise and the partition, so as to avoid gaps or mismatches between the merchandise and the partition and ensure the maximization of the utilization of the lane space;

[0019] Step 4.2: In the case of insufficient snap hole positions, ensure the stable installation of the partition by adjusting the way of placing the goods or using adjustable partitions, and do not affect the storage space of the goods.

[0020] In the above solution, optionally, the matching of the goods and the snap hole positions ensures that each good can be smoothly installed with the partition in the goods lane, avoiding actual installation problems caused by calculation errors. The method further includes:

[0021] Step 5.1: Conduct a simulation analysis on each goods lane, simulate the interaction between the goods and the goods lane partition through a simulation tool, ensure that the goods can be perfectly docked with the snap hole positions of the partition when actually placed, and avoid installation difficulties caused by errors;

[0022] Step 5.2: Provide snap hole positions of different specifications for partitions according to the sizes of different goods, and automatically select the most suitable partition installation position to ensure the best placement method for each good.

[0023] In the above solution, optionally, the steps for optimizing the goods lane layout include:

[0024] Step 6.1: Analyze the total width of all goods through an algorithm and compare it with the default width of the goods lane. By converting it into snap hole positions, ensure that the total width of the goods does not exceed the available space of the goods lane;

[0025] Step 6.2: Based on the calculation of the goods width, dynamically adjust the goods lane layout, and automatically insert adjustable partitions using the remaining space, thereby further improving the storage efficiency of the goods lane.

[0026] In the above solution, optionally, the optimization calculation method further includes:

[0027] Step 7.1: Based on the default width and default holes of the goods lane, combined with the actual size of the goods, use a physics engine for simulation to ensure the stability of each good in the goods lane and the firmness of the partition;

[0028] Step 7.2: Optimize the partition position of the goods lane according to the storage mode of the goods to achieve efficient docking of the goods and the goods lane.

[0029] In the above solution, optionally, the calculation method is implemented through a computer software tool, and the tool can perform automatic adjustment according to the real-time changes of the goods and has the following functions:

[0030] Step 8.1: Automatically identify changes in the goods size and update the snap hole position distribution in real time to optimize the goods lane layout;

[0031] Step 8.2: According to the real-time operation status of the vending machine, perform dynamic adjustment, and automatically perform inventory replenishment and optimization of the goods arrangement.

[0032] In the above solution, optionally, the optimization calculation method ensures that the docking accuracy between the commodity and the buckle hole position of the lane partition reaches within ±0.5 cm, and further ensures the docking accuracy through the following steps:

[0033] Step 9.1: Precision process the lane partition to ensure the consistency of the distribution of the buckle hole positions and high manufacturing accuracy;

[0034] Step 9.2: During the optimization process of the lane layout, combine the actual structure of the lane to adjust the position of the partition to ensure that the docking accuracy of the buckle hole positions meets the design standard.

[0035] In the above solution, optionally, the method can perform personalized layout according to different types of commodities, so that the position of each commodity in the lane matches the partition hole position best, thereby effectively improving the overall operation efficiency and inventory of the vending machine.

[0036] Compared with the prior art, the present application has at least the following beneficial effects:

[0037] This application is based on further analysis and research of existing technical problems. It is recognized that in the design of the channel layout of vending machines, there are problems such as accuracy issues in traditional width calculation methods, inaccurate matching of snap holes, and low utilization rate of channel space. By accurately calculating the number of snap holes occupied by products and combining optimization algorithms and big data technology, the accuracy issues, snap hole mismatches, and low utilization rate of channel space existing in the background technology have been successfully solved. In traditional technologies, due to the neglect of the precise matching between products and snap holes on the channel partitions, it often leads to ineffective installation of partitions or utilization of channel space due to calculation errors in product width, resulting in inefficient channel layout, which in turn affects the storage capacity and operation efficiency of vending machines. However, through precise calculation of the snap holes occupied by products, this invention ensures that each product can be docked with the snap holes on the partition, thus avoiding the problem of abnormal installation of partitions caused by calculation errors and ensuring the reasonable placement of products and the rationality of the vending machine structure. In addition, this invention also introduces big data technology. By collecting and analyzing historical layout data and product sales data, it optimizes the channel layout and adjusts the product positions according to real-time data, thereby effectively improving the utilization rate of channel space, maximizing the use of every inch of space, and avoiding the problem of space waste in traditional methods. By dynamically adjusting the placement positions and layouts of products, not only can products achieve the best arrangement in the channels, but also they can be adaptively optimized according to market demands and product sales situations, improving the operation efficiency of vending machines and the product turnover speed. Generally speaking, the optimization calculation method provided by this invention greatly improves the utilization rate of channels, avoids difficulties in installing partitions, increases the inventory capacity and overall operation efficiency of vending machines, effectively solves the problems of insufficient accuracy and low space utilization rate mentioned in the background technology, and provides a brand-new optimization solution for the design and operation of vending machines. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic flow chart of an optimized calculation method for the channel layout of a vending machine provided by an embodiment of this application;

[0039] Figure 2 It is a schematic diagram of the detailed placement of products in the channels provided by an embodiment of this application;

[0040] Figure 3 It is one of the relationship diagrams between products and channel holes provided by an embodiment of this application;

[0041] Figure 4 It is another relationship diagram between products and channel holes provided by an embodiment of this application;

[0042] Figure 5 It is the third relationship diagram between products and channel holes provided by an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0044] In one embodiment, as Figure 1 shown, an optimization calculation method for the channel layout of a vending machine is provided, including the following steps:

[0045] Step 1: Calculate the number of snap holes occupied by the product, and accurately determine the number of snap holes required for each product according to the physical size of the product and the distribution characteristics of the snap holes.

[0046] Step 2: Based on the number of snap holes occupied by the product, use an optimization algorithm to adjust the channel layout to maximize the utilization rate of the channel space and ensure that the product can be accurately docked with the snap hole positions of the channel partition.

[0047] Step 3: Dynamically adjust the placement position of each product according to the default width of the channel and the preset hole positions, ensure that the channel layout achieves the best product utilization rate, and consider the adaptability of the snap hole positions to the product placement.

[0048] This embodiment relates to an optimization calculation method for the channel layout of a vending machine. The core idea is to maximize the utilization rate of the channel space by accurately calculating the number of snap holes occupied by the product, combined with an optimization algorithm and big data technology, and at the same time ensure that the product can be successfully matched with the snap hole positions of the channel partition, thereby improving the inventory capacity and operation efficiency of the vending machine.

[0049] Step 1: Calculate the number of snap holes occupied by the product: First, it is necessary to measure the width of each product. The width of the product is one of the key parameters affecting the channel layout. By measuring the width of each product and combining the distribution characteristics of the snap holes on the channel partition, the number of snap holes required for the product is estimated. The measurement of the product width not only includes the size of the product itself, but also the actual size of the product packaging, because for some products, the width may be different due to different packaging materials or shapes. Therefore, through multiple measurements of the product, the system can accurately calculate the number of snap holes required to be occupied by each product and avoid problems with partition installation caused by width calculation errors.

[0050] Step 2: Optimize the lane layout based on the number of snap holes occupied by the products: After calculating the number of snap holes required for each product, the lane layout can be optimized according to this information. In traditional methods, the calculation of lane width is simply based on a simple comparison between the width of the product and the available space in the lane, without considering the actual position and distribution of the snap holes on the internal partition of the lane. As a result, many products may not be able to accurately align with these holes, or valuable space may be wasted. To solve this problem, the present invention uses an advanced optimization algorithm to automatically optimize the placement of products in the lane based on the number of snap holes occupied by the products, the hole layout of the lane, and the available space.

[0051] This optimization method utilizes the snap hole calculation based on the product width to ensure that the products in the lane can perfectly align with the snap holes on the lane partition, thus avoiding the difficulty of partition installation caused by width calculation errors in traditional calculation methods and greatly improving the utilization rate of the lane space. In addition, the optimization algorithm will also dynamically adjust the placement of products in the lane according to the shapes and sizes of different products, making full use of every inch of space in the lane.

[0052] Step 3: Further optimize the layout based on big data technology: To further improve the efficiency and accuracy of the lane layout, the present invention introduces big data analysis and machine learning technologies. By collecting the layout data of historical vending machines, as well as the data of product sales and inventory, the system can analyze the sales trends, demand quantities, and actual effects of space utilization of the products. Based on these data, the optimization algorithm can automatically make adjustments to make the product layout more reasonable and maximize the storage capacity of the lane and the product turnover efficiency.

[0053] For example, the system can dynamically adjust the lane layout according to the sales situations of different products. For hot-selling products, the system will give priority to ensuring that they occupy more space in the lane and adjust the layout according to real-time data to adapt to changes in market demand. At the same time, the system can also predict changes in product demand and optimize the layout in advance to ensure that the vending machine always operates in the best state.

[0054] The automatic vending machine lane layout optimization calculation method of the present invention can effectively solve the problems existing in the prior art, such as insufficient accuracy, snap hole mismatch, and low utilization rate of lane space, by accurately calculating the number of snap holes occupied by the products and combining big data technology for intelligent optimization.

[0055] Traditional methods for calculating the layout of product channels mainly determine the placement method of products by simply comparing the width of the product with the width of the channel. However, this method does not consider the actual position of the buckle holes inside the channel, resulting in the product may not be accurately docked with the buckle holes of the partition, thereby affecting the effective utilization of space. In contrast, the present invention calculates the number of buckle holes occupied by the product and optimizes the layout of the product in combination with big data technology, enabling the maximization of the utilization of the channel space. This not only ensures that the placement position of each product is reasonable but also avoids layout problems caused by mismatched buckle holes or space waste.

[0056] Since the traditional method ignores the distribution of the buckle holes on the channel partition when calculating the width of the product, it often leads to a mismatch between the product and the buckle holes of the partition, thus affecting the installation of the partition and further affecting the placement of the product and the normal operation of the vending machine. The present invention accurately calculates the buckle holes occupied by the product, ensuring that each product can be accurately docked with the buckle holes of the channel partition, thereby avoiding installation problems caused by errors. This technical effect effectively improves the operating efficiency of the vending machine and reduces the difficulties of maintenance and installation.

[0057] The present invention combines big data technology and machine learning algorithms, which can not only optimize the initial layout but also automatically adjust the channel layout according to real-time sales data and changes in product demand. This makes the vending machine more adaptable and flexible during operation, able to automatically adjust the placement position and layout of products according to changes in market demand, thereby improving the inventory volume and product turnover efficiency of the vending machine.

[0058] Through precise optimization of the channel layout and product placement, the present invention can significantly increase the inventory volume of the vending machine and ensure that the products are neatly and stably placed. More importantly, by optimizing the product layout and improving the utilization rate of the channel space, the vending machine can accommodate more products, reduce waste of inventory space, and thus improve the operating efficiency of the vending machine. Especially during peak hours, the optimized layout can ensure that customers can quickly select and purchase products, increasing sales volume and thus improving the overall economic benefits of the vending machine.

[0059] In this embodiment, the steps of calculating the number of buckle holes occupied by the product include:

[0060] Step 1.1: Measure the width of each product and, based on the actual width of the product and the distribution of the buckle holes, estimate the number of buckle holes occupied by the product through a mathematical model to ensure that each product accurately occupies a reasonable space in the channel;

[0061] Step 1.2: Dynamically evaluate the size of each product and, according to the placement of the product in the channel, adjust the calculation method of the buckle holes in real time to solve the problem of irregular sizes caused by the shape and packaging of the product.

[0062] In this embodiment, the optimization of the lane layout is achieved by integrating big data technology and machine learning algorithms. The method further includes:

[0063] Step 3.1: Collect and analyze a large amount of historical layout data of vending machines, and use machine learning technology to learn the placement of goods, so as to optimize the layout decision-making and ensure that the configuration of goods in the lanes reaches the optimal state;

[0064] Step 3.2: Through big data analysis, predict the demand for each commodity, and adjust the lane layout accordingly to make the placement of goods more in line with market demand, and further improve the storage capacity of the lanes and the commodity circulation efficiency.

[0065] In this embodiment, the utilization rate of the lane width is maximized by accurately calculating the matching position of the snap holes between the goods and the lane partition. And the method further includes:

[0066] Step 4.1: According to the physical size of each commodity and the default hole positions of the lanes, use an optimization algorithm to automatically adjust the docking position of the snap holes between the goods and the partition, so as to avoid gaps or mismatches between the goods and the partition, and ensure the maximum utilization of the lane space;

[0067] Step 4.2: In the case of insufficient snap hole positions, ensure the stable installation of the partition by adjusting the placement method of the goods or using adjustable partitions, and do not affect the storage space of the goods.

[0068] In this embodiment, the matching of the goods and the snap hole positions ensures that each commodity can be smoothly installed with the partition in the lane, avoiding actual installation problems caused by calculation errors. The method further includes:

[0069] Step 5.1: Conduct a simulation analysis on each lane, and simulate the interaction between the goods and the lane partition through a simulation tool to ensure that the goods can be perfectly docked with the snap hole positions of the partition during actual placement, avoiding installation difficulties caused by errors;

[0070] Step 5.2: Provide different specifications of snap hole positions for the partition according to the sizes of different goods, and automatically select the most suitable partition installation position to ensure the best placement method for each commodity.

[0071] In this embodiment, the steps for optimizing the lane layout include:

[0072] Step 6.1: Analyze the total width of all goods through an algorithm and compare it with the default width of the lane. By converting it into snap hole positions, ensure that the total width of the goods does not exceed the available space of the lane;

[0073] Step 6.2: Based on the calculation of the commodity width, dynamically adjust the lane layout, and automatically insert adjustable partitions using the remaining space, thereby further improving the storage efficiency of the lanes.

[0074] In this embodiment, the optimization calculation method further includes:

[0075] Step 7.1: According to the default width and default hole positions of the lanes, combined with the actual size of the commodity, use a physics engine for simulation to ensure the stability of each commodity in the lane and the firmness of the partition.

[0076] Step 7.2: Optimize the partition position of the lane according to the storage mode of the commodity to achieve efficient docking of the commodity and the lane.

[0077] In this embodiment, the calculation method is implemented through a computer software tool, which can automatically adjust according to the real-time changes of the commodity and has the following functions:

[0078] Step 8.1: Automatically identify the change in the commodity size and update the distribution of the snap holes in real time to optimize the lane layout.

[0079] Step 8.2: According to the real-time operation status of the vending machine, make dynamic adjustments, and automatically perform inventory replenishment and optimization of commodity arrangement.

[0080] In this embodiment, the optimization calculation method ensures that the docking accuracy of the snap holes between the commodity and the lane partition reaches within ±0.5 cm, and further guarantees the docking accuracy through the following steps:

[0081] Step 9.1: Precision process the lane partition to ensure the consistency of the distribution of the snap holes and high manufacturing accuracy.

[0082] Step 9.2: During the optimization process of the lane layout, adjust the position of the partition in combination with the actual structure of the lane to ensure that the docking accuracy of the snap holes reaches the design standard.

[0083] In this embodiment, the method can perform personalized layout according to different types of commodities, so that the position of each commodity in the lane matches the partition hole position best, thereby effectively improving the overall operation efficiency and inventory of the vending machine.

[0084] In one embodiment, the lane layout design of the vending machine is crucial for the display and placement of commodities. The utilization rate of the traditional lane layout is usually evaluated by calculating the ratio of the commodity width to the lane width. However, there are certain accuracy problems in the actual application of this method. Especially when the lane partition is installed by snaps, the calculation of the commodity width may lead to the situation that the snap hole positions cannot be matched during the actual placement, thus affecting the installation of the partition and the normal use of the lane.

[0085] Specifically, the current conventional calculation method cannot take into account the actual distribution of the snap holes in the cargo channel, and the accurate position of the snap holes plays a crucial role in the construction of the cargo channel and the placement of goods. In some cases, the cumulative calculation of the width of the goods may result in insufficient snap hole positions in the actual cargo channel to support the installation of the partition, thereby affecting the actual placement of the goods.

[0086] Therefore, how to improve the utilization rate of the cargo channel and at the same time ensure that the goods can be placed normally with the partition installed becomes the key to solving this problem.

[0087] Through the technical solution of this embodiment, the accuracy problem existing in the existing cargo channel layout calculation method can be effectively solved. The specific effects are as follows:

[0088] Improve the utilization rate of the cargo channel: By calculating the snap hole positions occupied by the goods, the width of the cargo channel is more accurately utilized, thus maximizing the actual usable area of the cargo channel.

[0089] Avoid installation problems: By ensuring the matching of the goods and the snap hole positions, the partition installation problems caused by calculation errors are avoided, ensuring that the goods can be placed smoothly according to the calculation results.

[0090] Improve the operation efficiency of the vending machine: By optimizing the calculation method, the cargo channel space of the vending machine is more efficiently utilized, thereby increasing the inventory capacity and operation efficiency of the vending machine.

[0091] The purpose of the present invention is to provide a new calculation method for optimizing the layout of the cargo channel of a vending machine. By calculating the snap hole positions occupied by the goods and combining with the big data calculation method, the maximum width of the cargo channel is utilized through the conversion of the goods and the hole positions, while ensuring that the goods can be placed normally with the partition installed, and solving the accuracy problem existing in the prior art.

[0092] To achieve the above objectives, the present invention proposes the following technical solutions: Snap hole position calculation method: When calculating the maximum utilization rate of the cargo channel, the snap hole positions occupied by the goods are used for calculation. By pre-estimating the number of snap holes occupied by each good and combining with the big data algorithm to optimize the layout of each good, ensure that each good can be accurately aligned with the snap hole positions of the cargo channel partition.

[0093] Optimization algorithm combined with big data: By analyzing a large amount of vending machine layout data and using big data technology to optimize the layout of the goods. This method can not only improve the width utilization rate of the cargo channel, but also ensure that each good can be accurately docked with the partition snap holes during actual installation, thus avoiding the actual installation problems caused by width calculation errors.

[0094] Consistency guarantee of the goods channel layout: Through the calculation method of this embodiment, it is ensured that the layout of the goods is consistent with the calculation results, maximizing the utilization rate of the goods channels while also ensuring that the partition boards can be smoothly installed when the goods are placed, avoiding the actual installation difficulties caused by the mismatch of the installation hole positions.

[0095] In one embodiment, the device hardware information is as follows:

[0096] Default width of the goods channel: 72.42;

[0097] Default hole positions of the goods channel: 57;

[0098] The details of the goods placement in the goods channels are as Figure 2 shown;

[0099] From the above data, it can be concluded that the cumulative width of the goods requires 72.96 cm, which is already greater than 72.42 cm. These goods cannot be placed through width calculation, but when the width is converted into hole positions, it can be seen that all use 57 hole positions and these goods can be placed.

[0100] The relationship diagram between the goods and the hole positions of the goods channel is as Figure 3 、 4 、5 shown:

[0101] The distance between two partition boards of the goods channel is greater than or equal to 4.5 cm and less than or equal to 24 cm (there is a risk of goods jamming when it is more than 22 cm);

[0102] The farthest scanning distance is 71.5 cm, and the stopping distance for goods placement is 82.5 cm, which is only applicable to the first, second, and fifth floors;

[0103] The stopping distance of the four goods channels is 71.5 cm;

[0104] The width of the spring goods channel is 15 cm. The spring goods channel is an extended goods channel and can be split into 2 single goods channels or combined into;

[0105] 1 double goods channel, which is only available on the first, second, and fifth floors;

[0106] The net depth of the goods channel is 39 cm, that is, the capacity depth of the goods that can be placed on each floor is 39 cm (6 bottles of 500 ml beverages);

[0107] The height and width of each floor of the goods channel are shown in the following figure, and the placement of goods shall not exceed this limit;

[0108] On the first, second, and fifth floors, at most 9 direct push goods channels + 2 spring goods channels can be placed, and on the third and fourth floors, at most 8 goods channels can be placed;

[0109] The width of the partition strip is 2 cm, and the hole position spacing is 1.3 cm.;

[0110] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

Claims

1. A method for calculating the optimization of the layout of aisles in a vending machine, characterized in that: The method comprises: Step 1: Calculate the number of snap holes occupied by the product, and accurately determine the number of snap holes required for each product based on the physical size of the product and the distribution characteristics of the snap holes; Step 2: Based on the number of buckle holes occupied by the product, an optimization algorithm is used to adjust the aisle layout to maximize the utilization of the aisle space and ensure that the product can be accurately docked with the buckle hole positions of the aisle partition; Step 3: Dynamically adjust the placement of each product based on the default width and preset hole positions of the aisle to ensure that the aisle layout achieves optimal product utilization, while considering the adaptability of the buckle hole positions to product placement.

2. The optimization calculation method according to claim 1, characterized in that: The step of calculating the number of buckle holes occupied by the product includes: Step 1.1: Measure the width of each product, and estimate the number of buckle holes occupied by the product through a mathematical model based on the actual width of the product and the distribution of buckle holes, to ensure that each product accurately occupies a reasonable space in the aisle; Step 1.2: Dynamically evaluate the size of each product and adjust the calculation method of the buckle hole position in real time according to the placement of the product in the aisle to solve the problem of irregular size caused by product shape and packaging factors.

3. The optimization calculation method according to claim 1, characterized in that: The optimization of cargo lane layout is achieved by integrating big data technology and machine learning algorithms. The method also includes: Step 3.1: Collect and analyze a large amount of historical layout data of vending machines, use machine learning technology to learn the placement of goods, thereby optimizing layout decisions and ensuring that the configuration of goods in the aisle is optimal; Step 3.2: Through big data analysis, predict the demand for each product and adjust the aisle layout accordingly to make the placement of goods more in line with market demand, further improving the storage capacity of the aisle and the efficiency of commodity circulation.

4. The optimization calculation method according to claim 1, characterized in that: The utilization rate of the aisle width is maximized by accurately calculating the matching position of the buckle holes of the goods and the aisle partitions, and the method further includes: Step 4.1: Based on the physical size of each product and the default hole position of the aisle, an optimization algorithm is used to automatically adjust the docking position of the snap holes of the product and the partition, thereby avoiding gaps or mismatches between the product and the partition, ensuring maximum utilization of the aisle space; Step 4.2: If there are insufficient clip holes, adjust the way the products are placed or use adjustable partitions to ensure that the partitions are stably installed and do not affect the storage space of the products.

5. The optimization calculation method according to claim 1, characterized in that: The matching of the commodity and the buckle hole position ensures that each commodity can be smoothly installed with the partition in the aisle, avoiding actual installation problems caused by calculation errors. The method also includes: Step 5.1: Perform simulation analysis on each aisle, and use simulation tools to simulate the interaction between the goods and the aisle partitions to ensure that the goods can be perfectly connected with the partition buckle holes when actually placed, avoiding installation difficulties caused by errors; Step 5.2: According to the sizes of different products, provide different specifications of partition buckle holes, and automatically select the most suitable partition installation position to ensure the best placement of each product.

6. The optimization calculation method according to claim 1, characterized in that: The steps to optimize the cargo lane layout include: Step 6.1: Analyze the total width of all products through an algorithm and compare it with the default width of the aisle. Convert it into buckle hole positions to ensure that the total width of the products does not exceed the available space in the aisle. Step 6.2: Based on the calculation of the product width, dynamically adjust the aisle layout and automatically insert adjustable partitions using the remaining space to further improve the storage efficiency of the aisle.

7. The optimization calculation method according to claim 1, characterized in that: The optimization calculation method also includes: Step 7.1: Based on the default width and hole position of the aisle and the actual size of the product, use the physical engine to simulate to ensure the stability of each product in the aisle and the stability of the partition; Step 7.2: According to the storage mode of the goods, optimize the position of the partitions in the aisle to achieve efficient connection between goods and aisles.

8. The optimization calculation method according to claim 1, characterized in that: The calculation method is implemented by a computer software tool, which can automatically adjust according to the real-time changes of the goods and has the following functions: Step 8.1: Automatically identify changes in product size and update the buckle hole distribution in real time to optimize the aisle layout; Step 8.2: Make dynamic adjustments based on the real-time operating status of the vending machine to automatically replenish inventory and optimize product arrangement.

9. The optimization calculation method according to claim 1, characterized in that: The optimized calculation method ensures that the docking accuracy between the buckle holes of the goods and the cargo aisle partitions is within ±0.5cm, and the docking accuracy is further guaranteed by the following steps: Step 9.1: Precision machining the cargo aisle partition to ensure that the distribution of the buckle holes is consistent and has a high degree of manufacturing accuracy; Step 9.2: During the optimization of the aisle layout, adjust the position of the partitions based on the actual aisle structure to ensure that the docking accuracy of the snap holes meets the design standards.

10. The optimization calculation method according to claim 1, characterized in that: The method can perform personalized layout according to different types of commodities, so that the position of each commodity in the aisle and the partition hole position are optimally matched, thereby effectively improving the overall operating efficiency and inventory of the vending machine.

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