Warehouse distribution and cabinet separation method, system and equipment and storage medium

Abstract

The invention relates to a warehouse cargo allocation and cabinet separation method, system and device and a storage medium. The method comprises the following steps of collecting cargo data stored in a warehouse, preprocessing the cargo data and then constructing a cargo data set; constructing an inventory consumption prediction model, inputting the cargo data set into the inventory consumption prediction model for training to obtain a trained inventory consumption prediction model, and predicting the inventory consumption of the warehouse cargo through the trained inventory consumption prediction model; constructing a warehouse cargo allocation priority model, calculating the cargo allocation priority of each warehouse based on the stock consumption of the cargos predicted by each warehouse, and allocating the cargos to each warehouse according to a cargo allocation priority sequence; and a warehouse cabinet loading model is constructed, when the goods are allocated to the corresponding warehouse, the cabinet loading priority of the goods is calculated through the warehouse cabinet loading model, and the goods are stored according to the cabinet loading priority.

Description

Technical Field

[0001] The invention relates to a method, system, equipment and storage medium for warehouse cargo distribution and cabinet distribution, and belongs to the technical field of warehouse management. Background Art

[0002] With the rapid development of globalization and e-commerce, the scale of the cross-border e-commerce logistics industry continues to grow, and the construction of cross-border e-commerce overseas warehouses has made significant progress. These overseas warehouses have not only improved the speed and efficiency of cross-border logistics, but also greatly reduced transportation costs and improved consumers' shopping experience. Among them, supply chain management has become increasingly important. In the supply chain, warehouse allocation is also a crucial link.

[0003] Reasonable inventory allocation enables the supply chain to process orders more efficiently, improve warehouse inventory turnover and corporate competitiveness. In supply chain management, different companies may adopt different inventory allocation methods, which depends on the company's strategic goals, product characteristics, market conditions and other factors. Therefore, inventory allocation should take into account various factors and be adjusted and optimized according to the actual situation of the company. Summary of the invention

[0004] In order to solve the above problems existing in the prior art, the present invention proposes a warehouse distribution cabinet method, system, equipment and storage medium.

[0005] The technical solution of the present invention is as follows: In one aspect, the present invention provides a method for warehouse cargo distribution and cabinet distribution, comprising the following steps: Collect the cargo data stored in the warehouse and construct the cargo data set after preprocessing the cargo data; Construct an inventory consumption prediction model, input the cargo data set into the inventory consumption prediction model for training, obtain a trained inventory consumption prediction model, and use the trained inventory consumption prediction model to predict the inventory consumption of warehouse cargo; Build a warehouse distribution priority model, calculate the distribution priority of each warehouse based on the predicted inventory consumption of the goods in each warehouse, and distribute goods to each warehouse according to the distribution priority order; Build a warehouse loading model. When the goods are delivered to the corresponding warehouse, the loading priority of the goods is calculated through the warehouse loading model, and the goods are stored according to the loading priority.

[0006] As a preferred embodiment of the present invention, the inventory consumption prediction model is constructed based on a long short-term memory model and an extreme gradient boosting model; The cargo data set is input into the long short-term memory model and the extreme gradient boosting model for training respectively. The output results of the two models are weighted summed to obtain the predicted value. After each training is completed, the following loss function is used Calculate the loss: ; in: Indicates the cost of insufficient inventory; represents the cost of excess inventory; represents the predicted value; Indicates actual value; represents the indicator function; Then, the posterior distribution of the predicted value is modeled through the Bayesian network to obtain the uncertainty of the prediction result; When the loss and the uncertainty of the prediction results reach a preset threshold, a trained inventory consumption prediction model is obtained, and the inventory consumption of warehouse goods is predicted by the trained inventory consumption prediction model.

[0007] As a preferred embodiment of the present invention, the warehouse distribution priority model includes a market priority model, a warehouse priority model and a transportation priority model; Goods are allocated to each warehouse in the order of transportation priority, market priority, and warehouse priority.

[0008] As a preferred implementation of the present invention, the market priority model is specifically shown in the following formula: ; in: Indicates the market priority of the warehouse; Indicates the market demand for goods in the warehouse; represents the seasonal adjustment factor; Indicates the intensity of competition for goods in the warehouse; Indicates the current inventory level in the warehouse; Indicates the future inventory growth rate of the warehouse; It represents the market risk indicator; , , , , Represents the market priority model weight coefficient.

[0009] As a preferred embodiment of the present invention, the warehouse priority model is specifically shown as follows: ; in: Indicates warehouse priority; Indicates the cargo safety factor of the warehouse; Indicates the utilization rate of the warehouse; represents the transportation cost of the warehouse; Indicates the warehouse turnover rate; Indicates the risk factor of the warehouse; , , , , Represents the weight coefficient of the warehouse priority model.

[0010] As a preferred embodiment of the present invention, the transport priority model constructs an objective function by minimizing the transport cost and the storage cost. , as shown in the following formula: ; in: Indicates from location To warehouse transportation costs; Indicates from location To warehouse The amount of cargo transported; Represents warehouse Quantity of goods; Represents warehouse The storage cost per unit of goods; At the same time, set the following constraints: ; ; ; in: Represents warehouse The demand for goods; Indicate location Supply volume; Represents warehouse Maximum storage quantity; Indicates the usage status of the warehouse; After solving the objective function through a linear programming or integer programming solver, the transportation priority of each warehouse is obtained according to the solution results.

[0011] As a preferred embodiment of the present invention, the warehouse loading model is specifically shown as follows: ; ; in: Indicates the loading priority of the current goods; Indicates the weight and volume ratio deviation of the current cargo and container; Indicates the current cargo weight; Indicates the current cargo volume; Indicates the maximum load weight of the container; Indicates the maximum volume of the container; Indicates the maximum weight of all cargo; Indicates the largest volume among all cargoes; All cargoes are loaded into the container according to the calculated cargo loading priority.

[0012] On the other hand, the present invention also provides a warehouse distribution and cabinet system, including a data collection module, an inventory consumption prediction module, a warehouse distribution module and a cargo cabinet loading module; The data collection module is used to collect the cargo data stored in the warehouse and construct the cargo data set after preprocessing the cargo data; The inventory consumption prediction module is used to build an inventory consumption prediction model, input the cargo data set into the inventory consumption prediction model for training, obtain the trained inventory consumption prediction model, and predict the inventory consumption of warehouse cargo through the trained inventory consumption prediction model; The warehouse distribution module is used to build a warehouse distribution priority model, calculate the distribution priority of each warehouse based on the inventory consumption of goods predicted by each warehouse, and distribute goods to each warehouse according to the distribution priority order; The cargo loading module is used to construct a warehouse loading model. When the cargo is delivered to the corresponding warehouse, the loading priority of the cargo is calculated through the warehouse loading model, and the cargo is stored according to the loading priority.

[0013] On the other hand, the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any embodiment of the present invention when executing the program.

[0014] In yet another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any embodiment of the present invention.

[0015] The present invention has the following beneficial effects: 1. The present invention uses the long short-term memory model and the extreme gradient boosting model to accurately predict inventory consumption, and uses the Bayesian network to model the posterior distribution of the predicted value to reduce uncertainty.

[0016] 2. The present invention constructs a market priority model, a warehouse priority model and a transportation priority model to ensure balanced distribution in multiple dimensions such as market demand, transportation cost, and warehouse utilization, thereby improving the overall response speed and decision-making efficiency of the supply chain.

[0017] 3. The present invention realizes the dual optimal configuration of the weight and volume of the container through the cargo loading priority model, reduces the space waste and overloading risk, and improves the automation and intelligence level of the loading process. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0019] 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.

[0020] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0021] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0022] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0023] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0024] Embodiment 1: See also Figure 1 A warehouse distribution method comprises the following steps: Collect the cargo data stored in the warehouse and construct the cargo data set after preprocessing the cargo data; Construct an inventory consumption prediction model, input the cargo data set into the inventory consumption prediction model for training, obtain a trained inventory consumption prediction model, and use the trained inventory consumption prediction model to predict the inventory consumption of warehouse cargo; Build a warehouse distribution priority model, calculate the distribution priority of each warehouse based on the predicted inventory consumption of the goods in each warehouse, and distribute goods to each warehouse according to the distribution priority order; Build a warehouse loading model. When the goods are delivered to the corresponding warehouse, the loading priority of the goods is calculated through the warehouse loading model, and the goods are stored according to the loading priority.

[0025] As a preferred implementation of this implementation, the inventory consumption prediction model is constructed based on the long short-term memory model and the extreme gradient boosting model; The cargo data set is input into the long short-term memory model and the extreme gradient boosting model for training respectively. The output results of the two models are weighted summed to obtain the predicted value. After each training is completed, the following loss function is used Calculate the loss: ; in: Indicates the cost of insufficient inventory; represents the cost of excess inventory; represents the predicted value; Indicates actual value; represents the indicator function; The cost of insufficient inventory is composed of direct losses (such as loss of sales opportunities due to out-of-stock, loss of profits or opportunity costs caused by unfulfilled sales orders) and indirect losses (such as decreased customer satisfaction, damaged brand reputation, subsequent customer loss, etc.); The cost of excess inventory is composed of holding costs (storage costs, insurance costs, labor and equipment costs caused by extra inventory, etc.), capital occupation (excess inventory occupies the company's operating funds, causing opportunity costs and affecting cash flow) and possible depreciation or expiration (for example, for fresh food, medicine or time-sensitive goods, the expiration or unsalable excess inventory will cause greater losses); If it is estimated that: for every piece of goods less prepared, a profit loss of 20 yuan (including the subsequent customer loss effect) will be lost, and for every piece of goods more prepared, an average expenditure of 5 yuan will be required (including storage, capital occupation, etc.), then the initial setting can be , ; Then, the posterior distribution of the predicted value is modeled through the Bayesian network to obtain the uncertainty of the prediction result; When the loss and the uncertainty of the prediction results reach a preset threshold, a trained inventory consumption prediction model is obtained, and the inventory consumption of warehouse goods is predicted by the trained inventory consumption prediction model.

[0026] As a preferred implementation of this embodiment, the warehouse distribution priority model includes a market priority model, a warehouse priority model and a transportation priority model; Allocate goods to each warehouse in the order of transportation priority, market priority, and warehouse priority, i.e. transportation priority > market priority > warehouse priority; In this embodiment, in order to ensure uniform distribution, a minimum distribution principle is set to ensure balanced distribution of materials among regions to avoid local supply shortages or surpluses.

[0027] As a preferred implementation of this embodiment, the market priority model is specifically shown as follows: ; in: Indicates the market priority of the warehouse; Indicates the market demand for goods in the warehouse; represents the seasonal adjustment factor; Indicates the intensity of competition for goods in the warehouse; Indicates the current inventory level of the warehouse (available inventory / ideal inventory); Indicates the future inventory growth rate of the warehouse; Indicates market risk indicators (such as economic fluctuations, policy impact, etc.); , , , , Represents the market priority model weight coefficient.

[0028] Market demand for goods in the warehouse The calculation formula is: ; in: Indicates the warehouse Historical sales data of the goods (sales volume per unit time); Indicates The weight coefficient of each product (based on the importance, profit margin or market share of the product, for example, high-profit or high-turnover products may have a higher weight); Indicates The amount of demand growth for a particular product (based on market trends, seasonality or new product introductions); Indicates the types of goods in the warehouse; ; in: Indicates warehouse The market demand for goods in each season (for example, air conditioner sales are high in summer and heating sales are high in winter); Indicates Benchmark market demand for the quarter (using average sales for the entire year or multiple years as benchmark demand); ; in: Indicates the market price of goods in the warehouse; It indicates the average price of the same goods in the market; represents the warehouse’s market share (as a proportion of total market demand); Indicates the total demand in the target market; Indicates the inventory level of the warehouse; Indicates the maximum storage capacity of the warehouse; Indicates the degree of product differentiation at the warehouse (e.g., by examining product differentiation and the availability of substitutes); Indicates the degree of barriers to the goods in the warehouse entering the market (such as laws and regulations, technical barriers, capital requirements, etc.); Indicates brand loyalty (the degree of consumer preference for the warehouse brand or supplier); ; in: Indicates at time Inventory consumption; Indicates time Inventory consumption; A number representing a time period (for example, the last 12 months).

[0029] As a preferred implementation of this embodiment, the warehouse priority model is specifically shown in the following formula: ; in: Indicates warehouse priority; Indicates the cargo safety factor of the warehouse; Indicates the utilization rate of the warehouse (the ratio of available space to total space, the lower the value, the better); represents the shipping cost of the warehouse (which can be calculated by the shipping priority model); Indicates the warehouse turnover rate (reflects the flow efficiency of goods in the warehouse, the higher the value, the better); Indicates the risk factor of the warehouse (such as natural disasters, theft risks, etc., the lower the value, the better); , , , , Represents the weight coefficient of the warehouse priority model; ; in: It indicates the value of goods, the economic value of a unit of goods; Indicates the level of transport risk, the degree of risk associated with the transport of goods, which can usually be assessed through historical data; Indicates the storage environment level, which is used to measure the environmental risks of storing goods, including factors such as humidity, temperature, and pollution; Indicates the packaging quality level, specifically the packaging strength and protective capacity of the goods; Indicates cargo insurance coverage; Indicates the risk factor of special goods, which is an additional risk factor for special goods such as fragile goods and flammable goods. The range can be set from 0 (no impact) to 10 (high impact); ; Among them: Cost of sales represents the total cost of goods sold within a certain period of time (usually one year or quarter); Average inventory = (beginning inventory + ending inventory) / 2; As a preferred implementation of this embodiment, the transportation priority model constructs an objective function by minimizing transportation cost and storage cost. , as shown in the following formula: ; in: Indicates from location To warehouse transportation costs; Indicates from location To warehouse The amount of cargo transported; Represents warehouse Quantity of goods; Represents warehouse The storage cost per unit of goods; At the same time, set the following constraints: ; ; ; in: Represents warehouse The demand for goods; Indicate location Supply volume; Represents warehouse Maximum storage quantity; Indicates the usage status of the warehouse; After solving the objective function through a linear programming or integer programming solver, the transportation priority of each warehouse is obtained according to the solution results.

[0030] As a preferred implementation of this embodiment, the warehouse loading model is specifically shown as follows: ; ; in: Indicates the loading priority of the current goods; Indicates the weight and volume ratio deviation of the current cargo and container; Indicates the current cargo weight; Indicates the current cargo volume; Indicates the maximum load weight of the container; Indicates the maximum volume of the container; Indicates the maximum weight of all cargo; Indicates the largest volume among all cargoes; All goods are loaded into the container according to the calculated loading priority. When all goods are loaded into the container, it may happen that the weight of the container reaches the threshold but the volume is left. In this embodiment, the simulated annealing algorithm is used to optimize the load-bearing capacity of the container and the volume of the cabinet. Specifically, The preset weight range, volume range and cargo quantity range of the container are calculated by cyclically calculating whether the container reaches the preset weight range, volume range and cargo quantity range through the simulated annealing algorithm; For containers that have not been reached, when the weight of the container exceeds the weight range, the heaviest cargo is taken out and the cargo placement is recalculated using the simulated annealing algorithm. Similarly, when the volume of the container exceeds the volume range, the cargo with the largest volume is taken out and the cargo placement is recalculated using the simulated annealing algorithm.

[0031] Embodiment 2: A warehouse distribution and cabinet system, comprising a data collection module, an inventory consumption prediction module, a warehouse distribution module and a cargo cabinet loading module; The data collection module is used to collect the cargo data stored in the warehouse and construct the cargo data set after preprocessing the cargo data; The inventory consumption prediction module is used to build an inventory consumption prediction model, input the cargo data set into the inventory consumption prediction model for training, obtain the trained inventory consumption prediction model, and predict the inventory consumption of warehouse cargo through the trained inventory consumption prediction model; The warehouse distribution module is used to build a warehouse distribution priority model, calculate the distribution priority of each warehouse based on the inventory consumption of goods predicted by each warehouse, and distribute goods to each warehouse according to the distribution priority order; The cargo loading module is used to construct a warehouse loading model. When the cargo is delivered to the corresponding warehouse, the loading priority of the cargo is calculated through the warehouse loading model, and the cargo is stored according to the loading priority.

[0032] The system is used to implement the method in Example 1, which will not be described in detail here.

[0033] Embodiment three: This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any embodiment of the present invention when executing the program.

[0034] Embodiment 4: This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in any embodiment of the present invention is implemented.

[0035] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0036] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0037] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0038] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0039] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for warehouse distribution and cabinet distribution, characterized in that: The following steps are involved: Collect the cargo data stored in the warehouse and construct the cargo data set after preprocessing the cargo data; Construct an inventory consumption prediction model, input the cargo data set into the inventory consumption prediction model for training, obtain a trained inventory consumption prediction model, and use the trained inventory consumption prediction model to predict the inventory consumption of warehouse cargo; Build a warehouse distribution priority model, calculate the distribution priority of each warehouse based on the predicted inventory consumption of the goods in each warehouse, and distribute goods to each warehouse according to the distribution priority order; Build a warehouse loading model. When the goods are delivered to the corresponding warehouse, the loading priority of the goods is calculated through the warehouse loading model, and the goods are stored according to the loading priority.

2. A warehouse distribution and cabinet distribution method according to claim 1, characterized in that: The inventory consumption prediction model is constructed based on the long short-term memory model and the extreme gradient boosting model; The cargo data set is input into the long short-term memory model and the extreme gradient boosting model for training respectively. The output results of the two models are weighted summed to obtain the predicted value. After each training is completed, the following loss function is used Calculate the loss: ； in: Indicates the cost of insufficient inventory; represents the cost of excess inventory; represents the predicted value; Indicates actual value; represents the indicator function; Then, the posterior distribution of the predicted value is modeled through the Bayesian network to obtain the uncertainty of the prediction result; When the loss and the uncertainty of the prediction results reach a preset threshold, a trained inventory consumption prediction model is obtained, and the inventory consumption of warehouse goods is predicted by the trained inventory consumption prediction model.

3. A warehouse distribution and cabinet distribution method according to claim 1, characterized in that: The warehouse distribution priority model includes a market priority model, a warehouse priority model and a transportation priority model; Goods are allocated to each warehouse in the order of transportation priority, market priority, and warehouse priority.

4. A warehouse distribution and cabinet distribution method according to claim 3, characterized in that: The market priority model is specifically shown as follows: ； in: Indicates the market priority of the warehouse; Represents the market demand for goods in the warehouse; represents the seasonal adjustment factor; Indicates the intensity of competition for goods in the warehouse; Indicates the current inventory level in the warehouse; Indicates the future inventory growth rate of the warehouse; It represents the market risk indicator; , , , , Represents the market priority model weight coefficient.

5. A warehouse distribution and cabinet distribution method according to claim 3, characterized in that: The warehouse priority model is specifically shown in the following formula: ； in: Indicates warehouse priority; Indicates the cargo safety factor of the warehouse; Indicates the utilization rate of the warehouse; represents the transportation cost of the warehouse; Indicates the warehouse turnover rate; Indicates the risk factor of the warehouse; , , , , Represents the weight coefficient of the warehouse priority model.

6. A warehouse distribution and cabinet distribution method according to claim 3, characterized in that: The transportation priority model constructs the objective function by minimizing transportation cost and storage cost. , as shown in the following formula: ； in: Indicates from location To warehouse transportation costs; Indicates from location To warehouse The amount of cargo transported; Represents warehouse Quantity of goods; Represents warehouse The storage cost per unit of goods; At the same time, set the following constraints: ； ； ； in: Represents warehouse The demand for goods; Indicate location Supply volume; Represents warehouse The maximum storage quantity; Indicates the usage status of the warehouse; After solving the objective function through a linear programming or integer programming solver, the transportation priority of each warehouse is obtained according to the solution results.

7. A warehouse distribution and cabinet distribution method according to claim 1, characterized in that: The warehouse loading model is specifically shown as follows: ； ； in: Indicates the loading priority of the current goods; Indicates the weight and volume ratio deviation of the current cargo and container; Indicates the current cargo weight; Indicates the current cargo volume; Indicates the maximum load weight of the container; Indicates the maximum volume of the container; Indicates the maximum weight of all cargo; Indicates the largest volume among all cargoes; All cargoes are loaded into the container according to the calculated cargo loading priority.

8. A warehouse distribution cabinet system, characterized in that: It includes data collection module, inventory consumption forecasting module, warehouse distribution module and cargo loading module; The data collection module is used to collect the cargo data stored in the warehouse and construct the cargo data set after preprocessing the cargo data; The inventory consumption prediction module is used to build an inventory consumption prediction model, input the cargo data set into the inventory consumption prediction model for training, obtain the trained inventory consumption prediction model, and predict the inventory consumption of warehouse cargo through the trained inventory consumption prediction model; The warehouse distribution module is used to build a warehouse distribution priority model, calculate the distribution priority of each warehouse based on the inventory consumption of goods predicted by each warehouse, and distribute goods to each warehouse according to the distribution priority order; The cargo loading module is used to construct a warehouse loading model. When the cargo is delivered to the corresponding warehouse, the loading priority of the cargo is calculated through the warehouse loading model, and the cargo is stored according to the loading priority.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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