Cargo warehouse dividing method and device

Abstract

The invention discloses a cargo warehouse dividing method and device, and relates to the technical field of computers. A specific embodiment of the method comprises the steps of obtaining a warehouse dividing demand, warehouse information, information of each to-be-divided cargo, and relevancy between the to-be-divided cargos; establishing a planning model according to at least one warehouse dividing strategy of maximizing the relevancy between the goods and minimizing the warehouse dividing number, and inputting the warehouse dividing demand, the warehouse information, the information of each to-be-divided goods and the relevancy between the to-be-divided goods into the planning model so as to obtain the warehouse dividing demand, the warehouse information, the information of each to-be-divided goods and the relevancy between the to-be-divided goods. And by solving the planning model, distributing corresponding warehouses for the to-be-separated cargos. According to the embodiment, scientific and reasonable cargo warehouse division is achieved, the order splitting rate is reduced, the human efficiency and the warehouse utilization rate are improved, the management cost is reduced, the warehouse division requirements of clients are considered, and the satisfaction degree of the clients is improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method and device for dividing goods into warehouses. Background Art

[0002] In modern warehousing management, how to allocate suitable warehouses for goods and establish a scientific warehouse allocation method has always been a problem that continues to be explored in this field. The essence of goods allocation is to determine the corresponding relationship between goods and warehouses. By allocating scientific and reasonable storage warehouses to goods, the inventory turnover rate of goods in the warehouse can be effectively improved, inventory costs can be effectively reduced, inventory configuration can be improved, and warehouse utilization can be improved, thereby improving logistics efficiency, improving logistics processes, and bringing more economic benefits to enterprises. The existing goods allocation method mainly determines the warehouse corresponding to the goods by determining the matching relationship between the storage quantity of the goods to be allocated and the storage capacity of the warehouse.

[0003] In the process of implementing the present invention, the inventors found that the prior art has the following problems:

[0004] The existing data used for the warehouse division of goods is relatively simple, and only considers the adaptation relationship between demand and storage capacity. Although it can meet the storage needs of goods, it cannot guarantee the scientificity and rationality of the warehouse division. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a method and device for dividing warehouses for goods, which realizes the scientific and reasonable allocation of storage warehouses for goods. Based on multi-dimensional basic data, the goods are divided into warehouses with the goal of maximum correlation between goods stored in the warehouse and / or least occupied warehouses. This not only reduces the order splitting rate, improves labor efficiency and warehouse utilization, and reduces management costs, but also meets the individual warehouse division needs of customers and improves customer satisfaction.

[0006] To achieve the above object, according to one aspect of an embodiment of the present invention, a method for dividing goods into warehouses is provided, comprising:

[0007] Obtaining warehouse division requirements, warehouse information, information of each item to be divided into warehouses, and the correlation between each item to be divided into warehouses;

[0008] According to at least one of the warehouse division strategies of maximizing the correlation between goods and minimizing the number of warehouse divisions, a planning model is established, the warehouse division demand, the warehouse information, the information of each of the goods to be divided into warehouses, and the correlation between each of the goods to be divided into warehouses are input into the planning model, and the corresponding warehouse is allocated to each of the goods to be divided into warehouses by solving the planning model.

[0009] Optionally, the correlation between each of the goods to be warehoused is obtained, including: taking category and brand as statistical dimensions, counting the number of times that goods of different brands, different categories, and different categories and brands appear in the same order in historical transaction orders; obtaining a correlation reference table based on the statistical number of times; and determining the correlation between each of the goods to be warehoused according to the correlation reference table.

[0010] Optionally, before inputting the warehouse division requirement, the warehouse information, the information of each of the goods to be divided into warehouses, and the correlation between each of the goods to be divided into warehouses into the planning model, the method also includes: obtaining the same warehouse brand of the same warehouse specified in the warehouse division requirement; performing same warehouse brand decoupling processing and corresponding correlation decoupling calculation on the information of each of the goods to be divided into warehouses according to the correlation between the same warehouse brand and each of the goods to be divided into warehouses, so as to obtain the information of the goods to be divided into warehouses after the decoupling processing and the correlation between the goods to be divided into warehouses after the decoupling calculation; inputting the warehouse division requirement, the warehouse information, the information of each of the goods to be divided into warehouses, and the correlation between each of the goods to be divided into warehouses into the planning model, including: inputting the warehouse division requirement, the warehouse information, the information of the goods to be divided into warehouses after the decoupling processing and the correlation between the goods to be divided into warehouses after the decoupling calculation into the planning model.

[0011] Optionally, according to the correlation between the same warehouse brand and each of the goods to be separated, the information of each of the goods to be separated is decoupled according to the same warehouse brand and the corresponding correlation decoupling calculation is performed to obtain the information of the goods to be separated after the decoupling processing and the correlation between the goods to be separated after the decoupling calculation, including: removing the goods of the same warehouse brand from each of the goods to be separated, aggregating the remaining goods according to the category dimension, and obtaining the category goods and the information of the category goods; aggregating the removed goods of the same warehouse brand according to the brand dimension to obtain the brand goods and the information of the brand goods; the information of the category goods and the information of the brand goods constitute the information of the goods to be separated after the decoupling processing; according to the correlation between each of the goods to be separated, the category correlation between the category goods, the brand correlation between the brand goods, and the correlation between the category goods and the brand goods are calculated, and the category correlation, the brand correlation, and the correlation between the category goods and the brand goods constitute the correlation between the goods to be separated after the decoupling calculation.

[0012] Optionally, the planning model includes a correlation function between goods and a function of the number of warehouses to be divided; before inputting the warehouse division demand, the warehouse information, the information of each of the goods to be divided into warehouses, and the correlation between each of the goods to be divided into the pre-built planning model, the method also includes: determining the constraints of the planning model according to the warehouse division demand, the warehouse information, and the information of each of the goods to be divided into warehouses, and the constraints include warehouse energy storage constraints, warehouse production capacity constraints, warehouse volume constraints, and demand constraints.

[0013] Optionally, before inputting the warehouse division demand, the warehouse information, the information of each of the goods to be divided into warehouses, and the correlation between each of the goods to be divided into warehouses into the planning model, the method further includes: performing data cleaning on the warehouse division demand, the warehouse information, the information of each of the goods to be divided into warehouses, and the correlation between each of the goods to be divided into warehouses, so as to subsequently input the warehouse division demand after data cleaning, the warehouse information after data cleaning, the information of each of the goods to be divided into warehouses after data cleaning, and the correlation between each of the goods to be divided into warehouses after data cleaning.

[0014] Optionally, after allocating a corresponding warehouse to each of the goods to be warehoused, the method further includes: in response to receiving a warehouse utilization statistics request, counting the goods allocated to each warehouse according to the warehouses to which each of the goods to be warehoused is allocated, thereby obtaining the warehouse utilization of each of the warehouses, and visually displaying the warehouse utilization.

[0015] According to a second aspect of an embodiment of the present invention, there is provided a cargo storage device, comprising:

[0016] An information acquisition module is used to acquire warehouse division requirements, warehouse information, information of each cargo to be divided into warehouses, and the correlation between each cargo to be divided into warehouses;

[0017] The goods warehouse division module is used to establish a planning model according to at least one warehouse division strategy of maximizing the correlation between goods and minimizing the number of warehouse divisions, input the warehouse division demand, the warehouse information, the information of each of the goods to be divided into warehouses, and the correlation between each of the goods to be divided into warehouses into the planning model, and allocate a corresponding warehouse to each of the goods to be divided into warehouses by solving the planning model.

[0018] According to a third aspect of an embodiment of the present invention, there is provided an electronic warehouse distribution device for goods, comprising:

[0019] one or more processors;

[0020] a storage device for storing one or more programs,

[0021] When the one or more programs are executed by the one or more processors, the one or more processors implement the method provided in the first aspect of the embodiment of the present invention.

[0022] According to a fourth aspect of an embodiment of the present invention, a computer-readable medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method provided by the first aspect of the embodiment of the present invention is implemented.

[0023] An embodiment of the invention has the following advantages or beneficial effects: by obtaining the warehouse division demand, warehouse information, information of each cargo to be divided into warehouses, and the correlation between each cargo to be divided into warehouses; according to at least one warehouse division strategy of maximizing the correlation between the cargoes and minimizing the number of warehouse divisions, a planning model is established, and the warehouse division demand, warehouse information, information of each cargo to be divided into warehouses, and the correlation between each cargo to be divided into warehouses are input into the planning model. By solving the planning model, a technical solution of allocating corresponding warehouses to each cargo to be divided into warehouses is achieved, thereby realizing scientific and reasonable warehouse division of the cargoes. Based on multi-dimensional basic data, the cargoes are divided into warehouses with the goal of maximizing the correlation between the cargoes stored in the warehouses and / or occupying the least warehouses. This not only reduces the order splitting rate, improves labor efficiency and warehouse utilization, and reduces management costs, but also takes into account the warehouse division needs of customers and improves customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings are used to better understand the present invention and do not constitute an improper limitation of the present invention.

[0025] Figure 1 is a schematic diagram of the main process of the cargo storage method according to an embodiment of the present invention;

[0026] Figure 2 This is a schematic diagram of the decoupling process of the same warehouse brand according to an embodiment of the present invention;

[0027] Figure 3 It is a schematic diagram of a specific implementation flow of the method for dividing warehouses of goods according to an embodiment of the present invention;

[0028] Figure 4 1 is a schematic diagram of a system flow of a method for dividing warehouses of goods according to an embodiment of the present invention;

[0029] Figure 5 Schematic diagram of the overall principle of the cargo storage method according to an embodiment of the present invention;

[0030] Figure 6 is a schematic diagram of warehouse utilization statistics according to an embodiment of the present invention;

[0031] Figure 7 2 is a schematic diagram of main modules of a cargo compartment separation device according to an embodiment of the present invention;

[0032] Figure 8 is an exemplary system architecture diagram to which embodiments of the present invention may be applied;

[0033] Fig. 9 It is a schematic diagram of the structure of a computer system of a terminal device or a server suitable for implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0034] It should be noted that the acquisition, storage and application of user personal information involved in the technical solution of the present disclosure are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0035] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0036] The existing data for the warehouse division of goods is relatively simple, and only considers the adaptation relationship between demand and storage capacity. Although it can meet the storage needs of goods, it cannot guarantee the scientificity and rationality of the warehouse division, and cannot meet the practical application well.

[0037] In order to solve the above problems existing in the prior art, the present invention proposes a method for dividing warehouses of goods. According to at least one of the warehouse division strategies of maximizing the correlation between goods and minimizing the number of warehouse divisions, a planning model is constructed. According to the acquired warehouse division requirements, warehouse information, information of each to-be-divided warehouse, and the correlation between each of the said to-be-divided warehouses and other multi-dimensional basic data, the planning model is solved to determine the warehouse corresponding to each to-be-divided warehouse. The scientific and reasonable allocation of warehouses for goods is achieved. Based on the multi-dimensional basic data, the goods are divided into warehouses with the goal of the warehouse with the maximum correlation between the goods stored in the warehouse and / or the least occupied warehouse, which not only reduces the order splitting rate, improves labor efficiency and warehouse utilization, and reduces management costs, but also meets the customer's warehouse division needs and improves customer satisfaction.

[0038] In the introduction to the embodiments of the present invention, the terms and their meanings are as follows:

[0039] Energy storage: The energy storage by piece refers to the number of pieces of goods. For a warehouse, the energy storage refers to the maximum number of pieces of goods that can be stored.

[0040] Capacity: refers to the warehouse capacity. For a warehouse, capacity refers to the maximum number of goods that can be shipped out of the warehouse in one day, which is limited by human resources, machinery, production line resources, etc.

[0041] Category: refers to the physical classification of goods, which is a known label. For example, the category of a wireless headset is divided into: digital accessories-headphones-wireless headsets. All goods are marked as a known category, which is the result of the natural aggregation of the physical properties of the goods.

[0042] Figure 1 Schematic diagram of the main process of the cargo storage method according to an embodiment of the present invention. Figure 1 As shown, the cargo storage method according to the embodiment of the present invention includes the following steps S101 to S102.

[0043] Step S101, obtaining warehouse division requirements, warehouse information, information of each cargo to be divided into warehouses, and the correlation between each cargo to be divided into warehouses.

[0044] Specifically, when the system receives a request for goods to be divided into warehouses, it considers that the goods to be divided into warehouses belong to the customer, and some customers may have business requirements for division of warehouses, such as goods A cannot be placed together with goods that are easy to spill, etc. Therefore, when receiving a request for goods to be divided into warehouses, it first queries whether the customer of the goods to be divided into warehouses has a demand for division of warehouses. If a demand for division of warehouses is found, the demand for division of warehouses is obtained. At the same time, the information of each current goods to be divided into warehouses is obtained, such as goods identification, quantity, volume, weight and other information, and the correlation between each goods to be divided into warehouses is determined according to the pre-defined related goods combination, such as a mobile phone and a mobile phone case, a mobile phone film is a related goods combination, and a mouse and a mouse pad is a related goods combination. In addition, it is also necessary to obtain the warehouse information corresponding to the warehouses currently available for use in the warehouse cluster. These basic data are a collection of multiple dimensional data related to division of warehouses, which provide rich and comprehensive data support for subsequent goods division of warehouses.

[0045] According to one embodiment of the present invention, the correlation between each of the goods to be warehoused is obtained, including: taking category and brand as statistical dimensions, counting the number of times that goods of different brands, different categories, and different categories and brands appear in the same order in historical transaction orders; obtaining a correlation reference table based on the statistical number of times; and determining the correlation between each of the goods to be warehoused according to the correlation reference table.

[0046] Specifically, in the above method of determining the relevance between each to-be-divided warehouse goods by manually defining a combination of related goods, the coverage rate of the manually defined combination of related goods is usually relatively low, and the categories and brands of each to-be-divided warehouse goods are numerous, and some of the related categories or brands may not be found through manually defined combinations of related goods. In the embodiment of the present invention, with the help of historical transaction orders, in order to facilitate subsequent use, the embodiment of the present invention uses the minimum statistical dimension (category and brand) to jointly count the number of times that goods of different brands, different categories, and different categories and brands appear in the same order, and then obtains a relevance reference table based on the number of statistics. For example, in the historical transaction orders, there are 1000 orders for goods A and goods B that are placed together, then the relevance between the two is 1000. Specifically, there is the following relevance reference table example. According to the following Table 1, it can also be obtained that the relevance between brand 1 and brand 2 is 1000, the relevance between stationery category and hardware category is 1000, and the relevance between stationery brand 1 and hardware brand 2 is 1000. Then, the correlation between the categories and brands of the goods to be divided into warehouses is searched in the correlation reference table obtained from the statistics of historical transaction orders, and the correlation between the goods to be divided into warehouses is obtained.

[0047] Step S102: Establish a planning model according to at least one of the warehouse division strategies of maximizing the correlation between goods and minimizing the number of warehouse divisions, input the warehouse division demand, the warehouse information, the information of each of the goods to be divided into warehouses, and the correlation between each of the goods to be divided into warehouses into the planning model, and allocate a warehouse to each of the goods to be divided into warehouses by solving the planning model.

[0048] Specifically, according to the multi-dimensional basic data obtained above, the embodiments of the present invention mainly divide the goods to be divided into warehouses from two perspectives. One is the correlation between the goods, and the goods with the greatest correlation are combined in one warehouse. The other is the number of warehouses used, and the least warehouses are used to store the goods to be divided into warehouses. The two perspectives are selected according to needs. You can only focus on the correlation between the goods to maximize the correlation between the goods to allocate warehouses, or you can only focus on the number of warehouses used to minimize the number of warehouses to allocate warehouses. Of course, it is preferred to choose to use both the warehouse division strategy that maximizes the correlation between the goods and the warehouse division strategy that minimizes the number of warehouses. In this way, while ensuring that related goods are stored in the same warehouse, the least warehouses can be used.

[0049] Furthermore, in order to improve the efficiency and accuracy of warehouse distribution, the embodiment of the present invention, based on the selected warehouse distribution strategy, uses the modeling concept of operations research to establish a planning model corresponding to the warehouse distribution strategy, and inputs the warehouse distribution demand, warehouse information, information of each cargo to be distributed, and the correlation between each cargo to be distributed into the planning model. By using the solver tool of the planning model, the corresponding warehouse can be efficiently and accurately allocated to each cargo to be distributed, thereby realizing the scientific and reasonable allocation of corresponding warehouses for goods based on multi-dimensional data.

[0050] According to one embodiment of the present invention, before inputting the warehouse division requirement, the warehouse information, the information of each of the goods to be divided into warehouses, and the correlation between each of the goods to be divided into warehouses into the planning model, the method also includes: obtaining the same warehouse brand of the same warehouse specified in the warehouse division requirement; performing same warehouse brand decoupling processing and corresponding correlation decoupling calculation on the information of each of the goods to be divided into warehouses according to the correlation between the same warehouse brand and each of the goods to be divided into warehouses, and obtaining the information of the goods to be divided into warehouses after the decoupling processing and the correlation between the goods to be divided into warehouses after the decoupling calculation; inputting the warehouse division requirement, the warehouse information, the information of each of the goods to be divided into warehouses, and the correlation between each of the goods to be divided into warehouses into the planning model, including: inputting the warehouse division requirement, the warehouse information, the information of the goods to be divided into warehouses after the decoupling processing and the correlation between the goods to be divided into warehouses after the decoupling calculation into the planning model.

[0051] Specifically, in some business scenarios, customers will propose the requirement that the goods of a specified brand be allocated in the same warehouse based on the management dimension of the business brand, and that they are not allowed to be stored in multiple warehouses. For this requirement of specifying the same warehouse for a specified brand, before the warehouse division requirement, the warehouse information, the information of each of the goods to be divided into warehouses, and the correlation between each of the goods to be divided into warehouses are input into the planning model, it is necessary to perform decoupling processing on the brand dimension for the information of each of the goods to be divided into warehouses in the input data. Specifically, the goods of the same warehouse brand and other goods can be identified and distinguished from the information of each of the goods to be divided into warehouses based on the same warehouse brand. Accordingly, according to the correlation between each of the goods to be divided into warehouses, the correlation between the goods corresponding to the result of the decoupling processing of the above-mentioned same warehouse brand is calculated. For example, according to the correlation between each of the goods to be divided into warehouses, the correlation between the goods of the same warehouse brand and the correlation between other goods are calculated respectively. Finally, the information corresponding to the goods of the same warehouse brand and other goods is used as the information of the goods to be divided into warehouses after decoupling processing, and the correlation between the goods of the same warehouse brand and the correlation between other goods are used as the correlation between the goods to be divided into warehouses after decoupling calculation. Furthermore, the information of the goods to be divided into warehouses after decoupling processing and the correlation between the goods to be divided into warehouses after decoupling calculation replace the information of the goods to be divided into warehouses and the correlation between the goods to be divided into warehouses, and are input into the planning model of the embodiment of the present invention, so as to process the relevant data in advance according to the customer's same warehouse needs, which is more convenient for the subsequent solution of the scale model.

[0052] According to another embodiment of the present invention, according to the correlation between the same warehouse brand and each of the goods to be separated, the information of each of the goods to be separated is decoupled according to the same warehouse brand and the corresponding correlation decoupling calculation is performed to obtain the information of the goods to be separated after the decoupling processing and the correlation between the goods to be separated after the decoupling calculation, including: removing the goods of the same warehouse brand from each of the goods to be separated, aggregating the remaining goods according to the category dimension, and obtaining the category goods and the information of the category goods; aggregating the removed goods of the same warehouse brand according to the brand dimension to obtain the brand goods and the information of the brand goods; the information of the category goods and the information of the brand goods constitute the information of the goods to be separated after the decoupling processing; according to the correlation between each of the goods to be separated, the category correlation between the category goods, the brand correlation between the brand goods, and the correlation between the category goods and the brand goods are calculated, and the category correlation, the brand correlation, and the correlation between the category goods and the brand goods constitute the correlation between the goods to be separated after the decoupling calculation.

[0053] Specifically, in actual warehousing scenarios, the storage capacity of a warehouse is large relative to the goods to be stored. Usually, goods of the same warehouse brand cannot completely occupy a warehouse. In order to make rational use of resources, other goods of another category (goods of the same warehouse brand that are not specified) need to be added to the warehouse. Then the question is, how to select suitable target goods from these other goods to be stored in the same warehouse with the goods of the same warehouse brand? In order to solve this problem, the embodiment of the present invention further optimizes the correlation decoupling calculation.

[0054] First, remove the goods of the same warehouse brand from the goods to be divided into warehouses, aggregate the remaining goods according to the category dimension, and obtain the category goods and the information of the category goods; aggregate the goods of the same warehouse brand that have been removed according to the brand dimension, and obtain the brand goods and the information of the brand goods, and combine the information of the category goods and the information of the brand goods to form the information of the goods to be divided into warehouses after decoupling. Then, according to the correlation between the goods to be divided into warehouses, calculate the category correlation between the category goods and the brand correlation between the brand goods. In addition, the correlation between the brand goods of the same warehouse brand and other goods (category goods) is additionally calculated, and the category correlation, brand correlation, and the correlation between the category goods and the brand goods form the correlation between the goods to be divided into warehouses after decoupling calculation.

[0055] In this way, when the quantity or capacity of branded goods of the same warehouse brand is not enough to occupy a warehouse, the system can also select category goods with the greatest brand relevance to the goods of the same warehouse brand from other goods (category goods) based on the correlation between the category goods and the brand goods, and add them to the warehouse.

[0056] The embodiments of the present invention fully consider that in actual warehousing scenarios, large-scale warehouses often add category goods to the warehouse where the goods of the same warehouse brand are located. In order to meet the requirements of the embodiments of the present invention to maximize the correlation between goods and minimize the number of sub-warehouses and achieve reasonable sub-warehouses, in addition to decoupling and identifying the brand goods of the same warehouse and other goods, and the corresponding correlation within the two types of data, the embodiments of the present invention also additionally calculate the correlation between category goods and brand goods, so as to scientifically and reasonably store the category goods with a high correlation with the brand goods of the same warehouse in the warehouse where the brand goods of the same warehouse are located, so as to save storage costs, ensure the utilization rate of the warehouse, and reduce the split order fulfillment rate of the final order fulfillment.

[0057] Through the above-mentioned decoupling of the designated same-warehouse brands and the decoupling of the correlation of the goods to be divided into warehouses, the obtained category goods and brand goods, as well as the corresponding category correlation, brand correlation, and the correlation between category goods and brand goods, ensure the distribution demand of designated same-warehouse brands to the same warehouse. The decoupled data is divided into warehouses according to at least one of the division strategies of maximizing the correlation between goods and minimizing the number of divisions. It can be achieved that all goods to be divided into warehouses are divided under the premise of satisfying the division of brands in the same warehouse.

[0058] On the other hand, there may be no demand for a specified brand in the warehouse distribution demand. In this case, the specified brand is not obtained. Accordingly, there is no need to decouple the specified brand or the correlation. There is no need to remove the goods of the specified brand from the goods to be distributed. Instead, the goods to be distributed need to be aggregated according to the category dimension to obtain the category goods.

[0059] In the case where no brand is specified in the warehouse division requirements, the categories of the goods to be divided are first aggregated, which can effectively solve the computing pressure caused by using the goods identification of the goods to be divided as the warehouse division granularity. Because the number of goods identifications is large, it increases the complexity of subsequent warehouse division calculations. Using the category dimension for warehouse division can greatly improve the computing efficiency and save computing resources. In the subsequent warehouse division process, the embodiment of the present invention preferably divides the warehouse by category as the goods granularity.

[0060] Figure 2 It is a schematic diagram of the decoupling process of the brands in the same warehouse of an embodiment of the present invention. According to the obtained list of designated brands in the same warehouse, the correlation between each cargo to be divided into warehouses, and the quantity of each cargo to be divided into warehouses, the energy storage occupancy and other information, wherein the energy storage occupancy is the amount of storage energy occupied by each cargo to be divided into warehouses, the goods of the same warehouse brand are removed from each cargo to be divided into warehouses, and the remaining goods are aggregated according to the category dimension to obtain the category goods and the information of the category goods; the goods of the same warehouse brand that are removed are aggregated according to the brand dimension to obtain the brand goods and the information of the brand goods; the category correlation between the category goods, the brand correlation between the brand goods, and the correlation between the category goods and the brand goods are calculated, and finally the information of the category goods, the brand goods information of the specified brand, and the correlation after decoupling are obtained.

[0061] According to another embodiment of the present invention, the planning model includes a correlation function between goods and a warehouse number function; before inputting the warehouse demand, the warehouse information, the information of each of the goods to be warehoused, and the correlation between each of the goods to be warehoused into a pre-built planning model, the method also includes: determining the constraints of the planning model according to the warehouse demand, the warehouse information, and the information of each of the goods to be warehoused, the constraints including warehouse energy storage constraints, warehouse production capacity constraints, warehouse volume constraints, and demand constraints.

[0062] Specifically, the planning model of the embodiment of the present invention selects an operations research model, and both maximizing the correlation between goods and minimizing the number of warehouses are taken into consideration in the planning model. In this way, a planning model can be applicable to three situations: selecting a warehouse strategy that maximizes the correlation between goods, selecting a warehouse strategy that minimizes the number of warehouses, and selecting a warehouse strategy that both maximizes the correlation between goods and minimizes the number of warehouses. The entire planning model consists of two parts: a correlation function and a warehouse number function. In addition, in order to ensure the scientificity and rationality of warehouse division from multiple dimensions as much as possible, before solving the planning model, the constraints of the planning model are established based on the acquired individual warehouse requirements, warehouse information, and information on each warehouse to be divided into warehouses. For example, the total number of goods identifications must not exceed the threshold number of warehouse identifications. Peak shaving constraints for goods stored in warehouses can also be established based on the time corresponding to the historical outbound peak in the information of each warehouse to be divided into warehouses. For example, if the outbound volume of category C reaches its peak at the end of the year, and the outbound volume of category D also reaches its peak at the end of the year, then if category C and category D are placed in the same warehouse, the warehouse utilization rate will be low in summer and autumn, and the warehouse load will increase at the end of the year, which is prone to overflow, so these two categories need to be stored in different warehouses. By establishing multi-dimensional constraints, the solution is guaranteed to be more scientific and reasonable.

[0063] Exemplarily, the planning model of the embodiment of the present invention is as follows: Among them, the parameters Indicates category and Brand correlation; correlation variable express Category and Whether the brand is in the same warehouse, , I is the category set, B is the brand set; Indicates category and The correlation, correlation variable express Category and Whether the categories are in the same warehouse, ; Indicates brand and The correlation, correlation variable express Brand and Whether the brand is in the same warehouse, ; The first three items of the planning model are the correlation functions between goods, including the correlation sub-function between categories and brands, the correlation sub-function between categories, and the correlation sub-function between brands; the last item Indicates the number of sub-warehouses. By minimizing the number of sub-warehouses, the categories are placed as aggregated as possible. , express Is the category in Bin, 0-1 variable, ; Minimizing the number of warehouses is given higher priority, and the model will optimize the category association while placing the categories as concentrated as possible.

[0064] Further, the embodiment of the present invention considers the factors affecting the warehouse division as comprehensively as possible, and the warehouse information obtained mainly includes the warehouse energy storage, warehouse capacity, number of picking positions of the warehouse, priority of warehouse use, and upper limit of the warehouse volume of different warehouses in the warehouse group. For the information of each to-be-divided warehouse goods, according to the aggregation results of the above-mentioned category dimensions, combined with the corresponding historical in-and-out warehouse records and the corresponding brand information, the quantity of goods (category order quantity) of each category dimension, the volume of goods (category volume) of the category dimension, and the energy storage prediction of the category dimension, the capacity prediction of the category dimension, the energy storage prediction of the brand dimension, the capacity prediction of the brand dimension, etc. are obtained. In addition, considering the specific warehouse division requirements, such as the mutually exclusive warehouse division requirements of two categories that cannot be in the same warehouse, the requirements for specifying specific warehouses, and the requirements for specifying disabled warehouses, etc. Combining these specific factors, the warehouse energy storage constraints, warehouse capacity constraints, warehouse volume constraints, and demand constraints corresponding to various warehouse division requirements of the planning model are obtained. At the same time, considering the correlation variables in the above-mentioned planning model, the constraints also include the constraints of the correlation variables and the warehouse division placement variables.

[0065] Exemplarily, for the above-mentioned planning model, the constraints used in the embodiment of the present invention are as follows: The sets, parameters, and decision variables involved are detailed in the following table: Among the above constraints, constraint (1) ensures that the designated brand is allocated to the same warehouse. Constraint (2) describes the category warehouse situation, and the sum of the category warehouse ratio variables should be equal to 1. Constraint (3) is the relationship between the 0-1 variable of whether category i is in warehouse j and the category warehouse ratio variable. When , indicating a warehouse Existing categories Constraints (4) and (5) are the constraints between whether the warehouse is in use and whether there are goods in the warehouse. M is a sufficiently large positive number, such as 99999. When there are goods in the warehouse, the left side of the equation is greater than 0. It can only be 1, which means that the warehouse where the goods are stored must be used. Constraints (6)-(8) are the constraints between the relevance variables and the category / brand warehouse placement variables. For example, for a specific warehouse , if category , are in the warehouse, then , , in which case, , since the item related to this variable in the objective function takes the maximum value, , on the contrary, when , When one of them is 0, . Constraints (9)-(12) correspond to warehouse energy storage constraints, production capacity constraints, cargo identification quantity constraints, and volume constraints, respectively. The left side of the inequality is the sum of the category brand dimension occupancy of the cargo in the warehouse, and the right side is the actual upper limit of the warehouse. The category brand occupancy and warehouse upper limit are the results of data preprocessing, and are used as the left coefficient and right parameter in this formula, respectively. (9)-(12) all restrict the cargo in the warehouse after optimization to not exceed the upper limit of the warehouse. Constraint (13) describes the mutually exclusive relationship between categories. Category and category They cannot be stored in the same warehouse. Constraints (14) and (15) describe the relationship between warehouses and categories. Some categories cannot be stored in certain warehouses, while other categories may only be stored in certain warehouses. Constraint (16) describes the priority relationship of warehouse use. Low-priority warehouses can only be used after all high-priority warehouses have been used.

[0066] According to another embodiment of the present invention, before inputting the warehouse division demand, the warehouse information, the information of each of the goods to be divided into the warehouse, and the correlation between each of the goods to be divided into the planning model, the method further includes: performing data cleaning on the warehouse division demand, the warehouse information, the information of each of the goods to be divided into the warehouse, and the correlation between each of the goods to be divided into the warehouse, so as to subsequently input the warehouse division demand after data cleaning, the warehouse information after data cleaning, the information of each of the goods to be divided into the warehouse, and the correlation between each of the goods to be divided into the warehouse after data cleaning.

[0067] Specifically, in view of the fact that the present invention utilizes multi-dimensional basic data information (warehouse division requirements, the warehouse information, information on each of the goods to be divided into warehouses, and the correlation between each of the goods to be divided into warehouses) to solve the planning model, different data information may come from different systems, and the data formats between the systems will inevitably be different. In addition, in the process of acquisition, due to external interference or other factors, dirty data such as noise may be added. Therefore, in order to avoid unnecessary resource consumption when solving the planning model, before the warehouse division requirements, warehouse information, information on each of the goods to be divided into warehouses, and the correlation between each of the goods to be divided into warehouses are input into the planning model, it is necessary to unify and standardize the data formats of all acquired data, and filter out noise data according to preset verification rules, such as data type verification rules, to obtain the cleaned warehouse division requirements, warehouse information, information on each of the goods to be divided into warehouses, and the correlation between each of the goods to be divided into warehouses, so that the cleaned data can be used as the input of the planning model later.

[0068] Figure 3It is a schematic diagram of the specific implementation flow of the method for dividing warehouses of goods in an embodiment of the present invention. According to the acquired warehouse information, the designated brand demand for the same warehouse, other special warehouse demand, the correlation between each cargo to be divided into warehouses, and the quantity of each cargo to be divided into warehouses, the energy storage occupancy and other information, the same warehouse brand of different warehouse demands in the warehouse demand is obtained, and the same warehouse brand of the cargo to be divided into warehouses is decoupled according to the same warehouse brand to obtain the result after decoupling processing. Then the warehouse information, other special warehouse demand, and the category cargo information, brand cargo information and decoupling correlation after decoupling processing are input into the pre-built planning model. The planning model obtains and divides the warehouses for each cargo to be divided according to at least one warehouse distribution strategy of maximizing the correlation and minimizing the number of warehouses, and obtains the warehouse corresponding to the allocation of each cargo to be divided into warehouses.

[0069] Figure 4 It is a system flow diagram of the method for dividing warehouses of goods in an embodiment of the present invention. Customers or operators upload the requirements of the designated brands in the same warehouse and other special warehouse requirements through the front-end page; the data information received by the front-end is saved to the database through the front-end and back-end interactive interfaces; in response to receiving the request for goods division, by calling the requirements of the brands in the same warehouse, other special warehouse requirements, currently available warehouse information, the quantity of each cargo to be divided, the energy storage occupancy and other information, and the correlation between each cargo to be divided, the constructed planning model is used to output the warehouse corresponding to each cargo to be divided, and the feedback is given to the front-end customer so that the front-end can query and display the final warehouse division result.

[0070] Figure 5 It is a schematic diagram of the overall principle of the method for dividing warehouses of goods in an embodiment of the present invention. According to the inventory information in the warehouse information, the quantity and volume of each to-be-divided warehouse, the total number of goods identifications of each to-be-divided warehouse and other input information, combined with the single warehouse storage capacity, production capacity, number of picking positions, agreed goods categories and warehouse correspondence and other constraints in the warehouse information, a planning model is formulated with the goal of maximizing at least one of the correlation between goods and minimizing the number of divided warehouses.

[0071] According to another embodiment of the present invention, after allocating a corresponding warehouse to each of the goods to be warehoused, the method further includes: in response to receiving a warehouse utilization statistics request, counting the goods allocated to each warehouse according to the warehouses to which each of the goods to be warehoused is allocated, thereby obtaining the warehouse utilization of each of the warehouses, and visually displaying the warehouse utilization.

[0072] Specifically, the embodiment of the present invention can not only allocate corresponding warehouses for each cargo to be divided into warehouses, but also visually obtain the utilization rate of the warehouses according to the warehouses corresponding to the acquired cargoes, so as to facilitate the overall macro management of the warehouses. When a warehouse utilization statistics request is received, the cargoes allocated to each warehouse are counted according to the warehouses allocated to each cargo to be divided into warehouses, the utilization rate of each warehouse is calculated according to the volume capacity of each warehouse in the warehouse group, and the utilization rate of each warehouse is visually displayed using a rectangular bar chart.

[0073] The embodiment of the present invention is compared with the existing warehouse division method in terms of warehouse utilization. Figure 6 This is a schematic diagram of warehouse utilization statistics in an embodiment of the present invention. The figure shows the warehouse utilization after the existing single-dimensional cargo warehouse division in comparison. Among them, the vertical axis is the utilization rate, and each rectangular column on the horizontal axis corresponds to a warehouse. The dark color is the warehouse utilization rate of the embodiment of the present invention, and the light color is the warehouse utilization rate of the prior art. Compared with the current situation where goods are placed in a scattered manner and the utilization rate of some warehouses is extremely low, the optimized warehouse division method increases the average warehouse utilization rate from 0.48 to 0.73, and 7 warehouses can be terminated, reducing the annual operating cost.

[0074] In addition to improving the utilization rate of the warehouse itself, the present invention also optimizes the average number of warehouses for categories and the overall order splitting rate for order fulfillment. According to statistics, the average number of warehouses for categories after optimization has dropped from 4.7 to about 2.9, and the categories are more concentrated. Correspondingly, the overall order splitting rate has been reduced by about 3 percentage points, and the annual number of packages is expected to be reduced by about 1.7 million.

[0075] The embodiment of the present invention adopts a reasonable and scientific warehouse distribution strategy to centralize the storage of goods of different categories, reduce the difficulty of operation and delivery of goods by suppliers, and improve labor efficiency; by centrally storing related goods, the order splitting rate of actual order fulfillment is reduced; by using peak shaving constraints on the energy storage capacity utilization rate in different periods, the warehouse explosion situation is reduced; and the customer's warehouse distribution needs are given priority consideration, and the warehouse distribution results that meet the individual needs of customers are obtained, thereby improving customer satisfaction.

[0076] Figure 7 Schematic diagram of the main modules of the cargo compartment device according to an embodiment of the present invention. Figure 7 As shown, the goods storage device 700 mainly includes an information acquisition module 701 and a goods storage module 702 .

[0077] The information acquisition module 701 is used to acquire the warehouse division requirements, warehouse information, information of each cargo to be divided into warehouses, and the correlation between each cargo to be divided into warehouses;

[0078] The goods warehouse division module 702 is used to establish a planning model according to at least one warehouse division strategy of maximizing the correlation between goods and minimizing the number of warehouse divisions, input the warehouse division demand, the warehouse information, the information of each of the goods to be divided into warehouses, and the correlation between each of the goods to be divided into warehouses into the planning model, and allocate a corresponding warehouse to each of the goods to be divided into warehouses by solving the planning model.

[0079] According to one embodiment of the present invention, the information acquisition module 701 is also used to: use category and brand as statistical dimensions to count the number of times that goods of different brands, different categories, and different categories and brands appear in the same order in historical transaction orders; obtain a correlation reference table based on the statistical number of times; and determine the correlation between each of the goods to be warehoused according to the correlation reference table.

[0080] According to another embodiment of the present invention, the goods warehouse distribution device 700 further includes a decoupling module (not shown in the figure), which is used to: before inputting the warehouse distribution demand, the warehouse information, the information of each of the goods to be distributed, and the correlation between each of the goods to be distributed into the planning model, obtain the same warehouse brand of the same warehouse specified in the warehouse distribution demand; according to the correlation between the same warehouse brand and each of the goods to be distributed, perform decoupling processing of the same warehouse brand and corresponding correlation decoupling calculation on the information of each of the goods to be distributed, and obtain the information of the goods to be distributed after the decoupling processing and the correlation between the goods to be distributed after the decoupling calculation; the goods warehouse distribution module 702 is also used to: input the warehouse distribution demand, the warehouse information, the information of the goods to be distributed after the decoupling processing and the correlation between the goods to be distributed after the decoupling calculation into the planning model.

[0081] According to another embodiment of the present invention, the decoupling module (not shown in the figure) is further used to: eliminate the goods of the same warehouse brand from each of the goods to be divided into warehouses, aggregate the remaining goods according to the category dimension, and obtain category goods and information about the category goods; aggregate the eliminated goods of the same warehouse brand according to the brand dimension to obtain brand goods and information about the brand goods; the information of the category goods and the information of the brand goods constitute the information of the goods to be divided into warehouses after the decoupling processing; calculate the category correlation between the category goods, the brand correlation between the brand goods, and the correlation between the category goods and the brand goods according to the correlation between each of the goods to be divided into warehouses, and the category correlation, the brand correlation, and the correlation between the category goods and the brand goods constitute the correlation between the goods to be divided into warehouses after the decoupling calculation.

[0082] According to another embodiment of the present invention, the planning model includes a correlation function between goods and a function of the number of warehouses to be divided; the goods warehouse division device 700 also includes a constraint determination module (not shown in the figure), which is used to: before inputting the warehouse division demand, the warehouse information, the information of each of the goods to be divided into warehouses, and the correlation between each of the goods to be divided into the pre-built planning model, determine the constraints of the planning model according to the warehouse division demand, the warehouse information, and the information of each of the goods to be divided into warehouses, and the constraints include warehouse energy storage constraints, warehouse production capacity constraints, warehouse volume constraints, and demand constraints.

[0083] According to another embodiment of the present invention, the goods storage device 700 further includes a data cleaning module (not shown in the figure), which is used to: before inputting the storage demand, the warehouse information, the information of each of the goods to be stored in the warehouse, and the correlation between each of the goods to be stored in the warehouse into the planning model, perform data cleaning on the storage demand, the warehouse information, the information of each of the goods to be stored in the warehouse, and the correlation between each of the goods to be stored in the warehouse, so that the storage demand after data cleaning, the warehouse information after data cleaning, the information of each of the goods to be stored in the warehouse after data cleaning, and the correlation between each of the goods to be stored in the warehouse after data cleaning can be subsequently input into the planning model.

[0084] According to another embodiment of the present invention, the goods storage device 700 further includes a statistical module (not shown in the figure), which is used for: after allocating a corresponding warehouse to each of the goods to be stored, in response to receiving a warehouse utilization statistics request, counting the goods allocated to each warehouse according to the warehouses allocated to each of the goods to be stored, and then obtaining the warehouse utilization of each of the warehouses, and visually displaying the warehouse utilization.

[0085] Figure 8 is an exemplary system architecture diagram to which embodiments of the present invention can be applied.

[0086] like Figure 8 As shown, system architecture 800 may include terminal devices 801, 802, 803, a network 804, and a server 805. Network 804 is used to provide a medium for communication links between terminal devices 801, 802, 803 and server 805. Network 804 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0087] Users can use terminal devices 801, 802, 803 to interact with server 805 through network 804 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 801, 802, 803, such as warehouse applications, etc. (only as an example).

[0088] The terminal devices 801 , 802 , and 803 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0089] Server 805 may be a server that provides various services, such as a background management server that supports the goods warehouse division performed by users using terminal devices 801, 802, and 803 (only as an example). The background management server may obtain warehouse division requirements, warehouse information, information of each of the goods to be warehoused, and the correlation between each of the goods to be warehoused; establish a planning model according to at least one warehouse division strategy of maximizing the correlation between goods and minimizing the number of warehouse divisions, input the warehouse division requirements, the warehouse information, information of each of the goods to be warehoused, and the correlation between each of the goods to be warehoused into the planning model, and allocate corresponding warehouses to each of the goods to be warehoused by solving the planning model.

[0090] It should be noted that the goods storage method provided in the embodiment of the present invention is generally executed by the server 805 , and accordingly, the goods storage device is generally set in the server 805 .

[0091] It should be understood that Figure 8 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0092] Reference below Fig. 9 , which shows a schematic diagram of the structure of a computer system of a terminal device or a server suitable for implementing an embodiment of the present invention. Fig. 9 The terminal device or server shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0093] like Figure 8 As shown, the computer system 900 includes a central processing unit (CPU) 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage part 908 into a random access memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the system 900 are also stored. The CPU 901, the ROM 902, and the RAM 903 are connected to each other through a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0094] The following components are connected to the I / O interface 905: an input section 906 including a keyboard, a mouse, etc.; an output section 907 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN card, a modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the I / O interface 905 as needed. A removable medium 911, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 910 as needed, so that a computer program read therefrom is installed into the storage section 908 as needed.

[0095] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 909, and / or installed from the removable medium 911. When the computer program is executed by the central processing unit (CPU) 901, the above-mentioned functions defined in the system of the present invention are executed.

[0096] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination thereof.

[0097] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0098] The units involved in the embodiments of the present invention may be implemented by software or hardware. The units described may also be set in a processor, for example, it may be described as: a processor includes: an information acquisition module and a cargo storage module.

[0099] Among them, the names of these modules do not constitute a limitation of the modules themselves in some cases. For example, the information acquisition module can also be described as "a module for obtaining warehouse distribution requirements, warehouse information, information of each cargo to be distributed, and the correlation between each cargo to be distributed."

[0100] On the other hand, the present invention also provides a computer-readable medium, which may be included in the device described in the embodiment; or it may exist independently without being assembled into the device. The computer-readable medium carries one or more programs, and when the one or more programs are executed by a device, the device includes: obtaining warehouse division requirements, warehouse information, information of each cargo to be divided into warehouses, and the correlation between each cargo to be divided into warehouses; according to at least one warehouse division strategy of maximizing the correlation between cargoes and minimizing the number of warehouse divisions, a planning model is established, the warehouse division requirements, the warehouse information, information of each cargo to be divided into warehouses, and the correlation between each cargo to be divided into warehouses are input into the planning model, and the corresponding warehouse is allocated to each cargo to be divided into warehouses by solving the planning model.

[0101] The technical solution according to the embodiment of the present invention has the following advantages or beneficial effects: by obtaining the warehouse division demand, warehouse information, information of each cargo to be divided into warehouses, and the correlation between each cargo to be divided into warehouses; according to at least one warehouse division strategy of maximizing the correlation between the cargoes and minimizing the number of warehouse divisions, a planning model is established, and the warehouse division demand, warehouse information, information of each cargo to be divided into warehouses, and the correlation between each cargo to be divided into warehouses are input into the planning model. By solving the planning model, a technical solution of allocating a corresponding warehouse to each cargo to be divided into warehouses is realized, and scientific and reasonable warehouse division of the cargo is achieved. Based on multi-dimensional basic data, the cargo is divided into warehouses with the goal of maximizing the correlation between the cargoes stored in the warehouses and / or occupying the least warehouses. This not only reduces the order splitting rate, improves labor efficiency and warehouse utilization, and reduces management costs, but also takes into account the warehouse division needs of customers and improves customer satisfaction.

[0102] The specific implementation methods described herein do not constitute limitations on the scope of protection of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention.

Claims

1. A method for dividing goods into warehouses, characterized in that: include: Obtaining warehouse division requirements, warehouse information, information of each item to be divided into warehouses, and the correlation between each item to be divided into warehouses; According to at least one of the warehouse division strategies of maximizing the correlation between goods and minimizing the number of warehouse divisions, a planning model is established, the warehouse division demand, the warehouse information, the information of each of the goods to be divided into warehouses, and the correlation between each of the goods to be divided into warehouses are input into the planning model, and the corresponding warehouse is allocated to each of the goods to be divided into warehouses by solving the planning model.

2. The method according to claim 1, characterized in that Obtaining the correlation between the goods to be warehoused, including: Taking category and brand as statistical dimensions, count the number of times goods of different brands, different categories, and different categories and brands appear in the same order in historical transaction orders; According to the statistical times, a correlation reference table is obtained; The correlation between the goods to be warehoused is determined according to the correlation reference table.

3. The method according to claim 1, characterized in that Before inputting the warehouse division requirement, the warehouse information, the information of each of the goods to be divided into warehouses, and the correlation between each of the goods to be divided into warehouses into the planning model, the method further includes: Get the same warehouse brand of the same warehouse specified in the warehouse sub-requirement; According to the correlation between the same warehouse brand and each of the goods to be separated, decoupling processing of the same warehouse brand and decoupling calculation of the corresponding correlation are performed on the information of each of the goods to be separated, so as to obtain the information of the goods to be separated after the decoupling processing and the correlation between the goods to be separated after the decoupling calculation; Inputting the warehouse division requirement, the warehouse information, the information of each of the goods to be divided into warehouses, and the correlation between each of the goods to be divided into warehouses into the planning model includes: The warehouse division requirements, the warehouse information, the information of the goods to be divided into warehouses after the decoupling process, and the correlation between the goods to be divided into warehouses after the decoupling calculation are input into the planning model.

4. The method according to claim 3, characterized in that According to the correlation between the same warehouse brand and each of the goods to be separated, the information of each of the goods to be separated is subjected to the same warehouse brand decoupling processing and the corresponding correlation decoupling calculation, so as to obtain the information of the goods to be separated after the decoupling processing and the correlation between the goods to be separated after the decoupling calculation, including: Eliminate the goods of the same warehouse brand from the goods to be warehoused, aggregate the remaining goods according to the category dimension, and obtain category goods and information about the category goods; Aggregate the removed goods of the same warehouse brand according to the brand dimension to obtain brand goods and information of the brand goods; The information of the category goods and the information of the brand goods constitute the information of the goods to be warehoused after decoupling processing; According to the correlation between each of the goods to be warehoused, the category correlation between the goods of the category, the brand correlation between the brand goods, and the correlation between the goods of the category and the brand goods are calculated. The category correlation, the brand correlation, and the correlation between the goods of the category and the brand goods constitute the correlation between the goods to be warehoused after the decoupling calculation.

5. The method according to claim 1, characterized in that The planning model includes a correlation function between goods and a function of the number of warehouses; Before inputting the warehouse division requirement, the warehouse information, the information of each of the goods to be divided into warehouses, and the correlation between each of the goods to be divided into warehouses into a pre-built planning model, the method further includes: According to the warehouse division requirements, the warehouse information, and the information of each of the goods to be divided into warehouses, the constraints of the planning model are determined, and the constraints include warehouse energy storage constraints, warehouse production capacity constraints, warehouse volume constraints, and demand constraints.

6. The method according to claim 1, characterized in that Before inputting the warehouse division requirement, the warehouse information, the information of each of the goods to be divided into warehouses, and the correlation between each of the goods to be divided into warehouses into the planning model, the method further includes: Data cleaning is performed on the warehouse division demand, the warehouse information, the information of each of the goods to be divided into warehouses, and the correlation between each of the goods to be divided into warehouses, so that the warehouse division demand after data cleaning, the warehouse information after data cleaning, the information of each of the goods to be divided into warehouses after data cleaning, and the correlation between each of the goods to be divided into warehouses after data cleaning can be subsequently input into the planning model.

7. The method according to claim 1, characterized in that After allocating corresponding warehouses to the goods to be warehoused, the method further includes: In response to receiving a warehouse utilization statistics request, the goods allocated to each warehouse are counted according to the warehouses to which the goods to be warehoused are allocated, so as to obtain the warehouse utilization of each warehouse, and the warehouse utilization is displayed visually.

8. A device for dividing goods into warehouses, characterized in that: include: An information acquisition module is used to acquire warehouse division requirements, warehouse information, information of each cargo to be divided into warehouses, and the correlation between each cargo to be divided into warehouses; The goods warehouse division module is used to establish a planning model according to at least one warehouse division strategy of maximizing the correlation between goods and minimizing the number of warehouse divisions, input the warehouse division demand, the warehouse information, the information of each of the goods to be divided into warehouses, and the correlation between each of the goods to be divided into warehouses into the planning model, and allocate a corresponding warehouse to each of the goods to be divided into warehouses by solving the planning model.

9. A mobile electronic device terminal, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computer readable 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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