Warehouse management method and device, equipment and storage medium

Abstract

The present disclosure relates to a warehouse management method and device, equipment and storage medium, the method comprises: based on the historical order data of the commodity, the heat of the commodity in the target warehouse is classified, and the heat category of the commodity is determined; the storage space corresponding to each heat category of the commodity is collected respectively, and the category of the storage space is obtained correspondingly; based on the category of the storage space, the heat frequency value of the storage space is calculated; according to the heat frequency value and the inventory of the storage space, the target storage space is selected from the target warehouse, so as to transfer the to-be-transferred commodity to the target storage space, the category of the commodity and the storage space can be associated with the actual goods handling and warehouse operation in the warehouse management, the secondary storage or picking task is simplified, so as to improve the production efficiency in the warehouse and reduce the management cost in the warehouse.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to a warehouse management method, apparatus, equipment and storage medium. Background Technology

[0002] With the rapid development of the retail industry in recent years, the demand for warehouses has increased accordingly, and higher requirements have been placed on the management of warehouse merchandise categories. Currently, e-commerce logistics warehouses have a large national coverage and scale. In the logistics operation with tens of thousands of storage locations and SKUs (Stock Keeping Units), how to scientifically link the four flows of information across the entire supply chain, including sales orders, merchandise storage, merchandise layout, and merchandise operation, directly determines the efficiency and cost of warehouse operations. However, considering the industry business characteristics and complexity of the actual problems, it is unrealistic to optimize the entire link of warehouse shelving, planning, sorting, replenishment, and picking through a single model. Optimizing individual links to improve warehouse operational efficiency is more practical. Among these, how to improve picking efficiency through the transfer operation in the sorting process is a crucial part of optimizing the existing warehouse links.

[0003] In existing technologies, the transfer of goods in the sorting process is generally carried out through preset rules, which lacks scientific basis and can lead to unreasonable sorting and transfer operations. Summary of the Invention

[0004] In order to solve the above-mentioned technical problems, or at least partially solve the above-mentioned technical problems, embodiments of this disclosure provide a warehouse management method, apparatus, equipment and storage medium.

[0005] In a first aspect, embodiments of this disclosure provide a warehouse management method, comprising the following steps:

[0006] Based on historical order data, products in the target warehouse are categorized by popularity to determine the product's popularity level.

[0007] Collect the storage locations corresponding to each type of popular product, and obtain the corresponding storage location type;

[0008] Calculate the thermal frequency value of the storage location based on its type;

[0009] A target storage location is selected from the target warehouse based on the storage location's heat frequency value and inventory level, so that the goods to be transferred can be moved to the target storage location.

[0010] In one possible implementation, the step of classifying goods in the target warehouse by popularity based on historical order data to determine the popularity category of the goods includes:

[0011] Based on the historical sales data of the products in the historical order data, a preset prediction algorithm is used to predict the sales volume of the products in the target warehouse within a preset time period.

[0012] The popularity category of a product is determined by its sales volume within a preset time period.

[0013] In one possible implementation, calculating the thermal frequency value of a storage location based on its type includes:

[0014] Calculate the initial value of the thermal frequency of the storage location;

[0015] The thermal frequency value of the storage location is calculated based on the type of storage location and the initial thermal frequency value.

[0016] In one possible implementation, the initial value of the thermal frequency of the storage location is calculated using the following expression:

[0017]

[0018] Among them, y i Let O be the initial value of the thermal frequency for the i-th storage location. i Let max{O} be the frequency of the quantity of goods stored in the i-th storage location within a preset period. i} represents the maximum value among the frequency of the quantity of goods stored in all storage locations within a preset period. When max{O j When} = 0, y j This is a preset fixed value.

[0019] In one possible implementation, the storage location types include best-selling product storage locations, and the heat frequency value of the storage location is calculated based on the storage location type and the initial heat frequency value using the following expression:

[0020]

[0021] Among them, h i Let x be the heat frequency value of the i-th best-selling product storage location. i Let x be the initial value of the thermal frequency for the i-th best-selling product storage location, min{x} i} represents the minimum initial value of the thermal frequency among all best-selling product storage locations, max{x} i} represents the maximum value among the initial thermal frequencies of all best-selling product storage locations.

[0022] In one possible implementation, the storage location includes storage locations for slow-moving goods, and the heat frequency value of the storage location is calculated based on the type of storage location and the initial heat frequency value using the following expression:

[0023]

[0024] Among them, h i Let ′ be the heat frequency value of the i-th slow-moving product storage location, and x be the heat frequency value of the i-th slow-moving product storage location.i Let ′ be the initial value of the heat frequency for the i-th slow-moving product storage location, min{x i '} represents the minimum initial value of the heat frequency among all slow-moving goods storage locations, max{x} i ′} represents the maximum value among the initial heat frequency values ​​of all slow-moving goods storage locations.

[0025] In one possible implementation, the step of selecting a target storage location from the target warehouse based on the storage location's heat frequency value and inventory level, in order to transfer the goods to be transferred to the target storage location, includes:

[0026] Select multiple storage locations corresponding to the heat level of the goods to be transferred;

[0027] Select a target storage location from the plurality of storage locations based on the heat frequency value and inventory level of the storage location, such that the sum of the heat frequency value and / or inventory level of all selected target storage locations is maximized.

[0028] In one possible implementation, the target product and target storage location are selected from the product to be transferred and the plurality of storage locations based on the heat frequency value and inventory level of the storage location, such that the sum of the heat frequency value and / or inventory level of all selected target storage locations is maximized, using the following expression:

[0029]

[0030]

[0031] Where h is the thermal frequency of the nth storage location, and m n Let y be the inventory quantity of the nth storage location. j x can be either 0 or 1. n Take 0 or 1, m j Let α be the inventory quantity to be transferred in the j-th storage location, where α + β = 1, 0 ≤ α, β ≤ 1.

[0032] Secondly, embodiments of this disclosure provide a warehouse management device, comprising:

[0033] The classification module is used to classify products in the target warehouse based on their popularity data and determine the popularity categories of the products.

[0034] The calculation module is used to collect the storage locations corresponding to each type of product in terms of heat level, obtain the type of storage location, and calculate the heat frequency value of the storage location based on the type of storage location.

[0035] The selection module is used to select a target storage location from the target warehouse based on the storage location's heat frequency value and inventory level, so as to transfer the goods to be transferred to the target storage location.

[0036] Thirdly, embodiments of this disclosure provide an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0037] Memory, used to store computer programs;

[0038] The processor, when executing programs stored in memory, implements the aforementioned warehouse management method.

[0039] Fourthly, embodiments of this disclosure provide a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the above-described warehouse management method.

[0040] Compared with the prior art, the technical solutions provided in this disclosure have at least some or all of the following advantages:

[0041] The warehouse management method and apparatus described in the embodiments of this disclosure,

[0042] Based on historical order data, products in the target warehouse are categorized by popularity to determine their popularity levels. Storage locations corresponding to each popularity level are then compiled to identify storage location types. Based on these storage location types, a popularity frequency value is calculated. Target storage locations are selected from the target warehouse according to the popularity frequency value and inventory levels to transfer the products to be moved. This approach links product and storage location categorization with actual warehouse handling and transfer operations, simplifying secondary transfers or picking tasks, thereby improving warehouse production efficiency and reducing warehouse management costs. Attached Figure Description

[0043] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0044] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0045] Figure 1 A schematic diagram illustrating a warehouse management method according to an embodiment of the present disclosure is shown.

[0046] Figure 2 A schematic diagram illustrating a warehouse management method according to another embodiment of the present disclosure is shown.

[0047] Figure 3A schematic diagram illustrating a warehouse management method according to yet another embodiment of the present disclosure is shown.

[0048] Figure 4 A schematic diagram illustrating a warehouse management method according to another embodiment of the present disclosure is shown.

[0049] Figure 5 A schematic diagram illustrating the structure of a warehouse management apparatus according to an embodiment of the present disclosure is shown; and

[0050] Figure 6 A schematic block diagram of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0052] See Figure 1 The embodiments of this disclosure provide a warehouse management method, including the following steps:

[0053] S1, based on historical order data, classify the products in the target warehouse by popularity to determine the product popularity category.

[0054] In practical applications, historical order data can refer to the historical order data of goods in the target warehouse. The classification of goods in the target warehouse based on popularity can be achieved by sorting goods according to their popularity and categorizing them in that order. This popularity classification includes, but is not limited to, best-selling and slow-moving goods. Best-selling goods, in order, include best-selling item A, best-selling item B, etc., and slow-moving goods, in order, include slow-moving item C, slow-moving item D, slow-moving item E, etc. Different types of goods can be distinguished using SKU codes.

[0055] S2, collect the storage locations corresponding to each type of product in terms of popularity, obtain the type of storage location, and calculate the popularity frequency value of the storage location based on the type of storage location.

[0056] In practical applications, the storage locations corresponding to products under the same heat index category are collected, and the storage locations in the target warehouse are divided into multiple categories to establish a correspondence between the heat index category of the product and the storage location number. The heat frequency value of each storage location in each category is calculated separately.

[0057] S3, select a target storage location from the target warehouse based on the heat frequency value and inventory of the storage location, so as to transfer the goods to be transferred to the target storage location.

[0058] In practical applications, target storage locations with high heat frequency values ​​and / or large inventory levels are selected from the target warehouse. That is, goods of the same heat frequency category are transferred from storage locations with low heat frequency values ​​and / or small inventory levels to storage locations of the same type with high heat frequency values ​​and / or large inventory levels, and it is ensured that the total amount of goods transferred to the target storage location is less than or equal to the inventory level of the target storage location.

[0059] Based on the above steps S1 to S3, the classification of goods and storage locations can be linked to the actual sorting and transfer operations in the warehouse, simplifying secondary transfer or picking tasks, thereby improving warehouse production efficiency and reducing warehouse management costs.

[0060] See Figure 2 In step S1, the step of classifying the products in the target warehouse by popularity based on the historical order data of the products and determining the popularity category of the products includes: S21, predicting the sales volume of the products in the target warehouse within a preset time period using a preset prediction algorithm based on the historical sales volume of the products in the historical order data of the products; S22, determining the popularity category of the products according to the sales volume of the products within the preset time period.

[0061] Based on the above steps S21 to S22, the popularity category of the product is determined according to the predicted sales volume of the product, and the popularity category of the product is associated with the calculation of the storage location heat frequency value and the classification of storage location categories to ensure that a suitable and optimized storage location can be found for the product to be transferred.

[0062] See Figure 3 In step S32, calculating the thermal frequency value of a storage location based on its type includes:

[0063] S31, calculate the initial value of the thermal frequency of the storage location.

[0064] In practical applications, the initial value of the thermal frequency of the storage space is calculated using the following expression:

[0065]

[0066] Among them, y i Let O be the initial value of the thermal frequency for the i-th storage location. i Let max{O} be the frequency of the quantity of goods stored in the i-th storage location within a preset period. i} represents the maximum value among the frequency of the quantity of goods stored in all storage locations within a preset period. When max{O i When} = 0, y i This is a preset fixed value.

[0067] S32, calculate the thermal frequency value of the storage location based on the type of storage location and the initial thermal frequency value.

[0068] In practical applications, the storage locations include best-selling product storage locations. The thermal frequency value of a storage location is calculated based on its type and initial thermal frequency value using the following expression:

[0069]

[0070] Among them, h i Let x be the heat frequency value of the i-th best-selling product storage location. i Let x be the initial value of the thermal frequency for the i-th best-selling product storage location, min{x} i} represents the minimum initial value of the thermal frequency among all best-selling product storage locations, max{x} i} represents the maximum value among the initial thermal frequencies of all best-selling product storage locations.

[0071] In practical applications, the storage locations also include those for slow-moving goods. The heat frequency value of the storage location is calculated based on the type of storage location and the initial heat frequency value using the following expression:

[0072]

[0073] Among them, h i Let ′ be the heat frequency value of the i-th slow-moving product storage location, and x be the heat frequency value of the i-th slow-moving product storage location. i Let ′ be the initial value of the heat frequency for the i-th slow-moving product storage location, min{x i '} represents the minimum initial value of the heat frequency among all slow-moving goods storage locations, max{x} i ′} represents the maximum value among the initial heat frequency values ​​of all slow-moving goods storage locations.

[0074] See Figure 4 In step S3, selecting a target storage location from the target warehouse based on the heat frequency value and inventory of the storage location to transfer the goods to be transferred to the target storage location includes: S41, selecting multiple storage locations corresponding to the heat frequency type of the goods to be transferred; S42, selecting a target storage location from the multiple storage locations based on the heat frequency value and inventory of the storage location, such that the sum of the heat frequency value and / or inventory of all selected target storage locations is maximized.

[0075] In practical applications, the following expression is used to select target goods and target storage locations from the goods to be transferred and the multiple storage locations based on the heat frequency value and inventory level of the storage location, so that the sum of the heat frequency value and / or inventory level of all selected target storage locations is maximized:

[0076]

[0077]

[0078] Where h is the thermal frequency of the nth storage location, and m n Let y be the inventory quantity of the nth storage location. j The value is either 0 or 1, meaning that when the item in the j-th storage location is selected for transfer, y... j Set to 1. When the item in the j-th storage location is not selected for transfer, y j Take 0; x n The value is either 0 or 1, meaning that when the i-th storage location is selected as the target storage location, x... n Set to 1, when the i-th storage location is not selected as the target storage location, x n Take 0; m j Let α be the inventory quantity to be transferred in the j-th storage location, where α + β = 1, 0 ≤ α, β ≤ 1.

[0079] The warehouse management method described in this disclosure is suitable for warehouses that store a wide variety of goods from suppliers in large quantities and that can quickly, accurately, cost-effectively, and efficiently pick, verify, and pack goods according to user order requirements. For example, a book warehouse that can store about 20,000 kinds of goods can achieve economical and fast goods picking and outbound, and improve picking efficiency through the transfer operation in the sorting process to optimize the inventory warehouse.

[0080] This disclosed embodiment categorizes goods in a target warehouse based on historical order data, unlike the commonly used ABC classification method (also known as ABC analysis, a classification method based on Vifredo Pareto's 20 / 80 rule; ABC classification aims to maximize inventory service levels, reduce costs, and improve inventory management efficiency and effectiveness; therefore, it is widely used in warehouse or distribution center inventory control; its core idea is to apply the "critical few and trivial many" principle revealed by the Pareto curve to management; its basic principle is to classify controlled objects according to their value or importance). This method combines the actual situation of goods popularity to classify goods, which is beneficial to the actual operational efficiency within the warehouse.

[0081] The warehouse management method described in this disclosure differs from the existing technology in which the sorting action in existing warehouses involves quantity verification and simple, extensive sorting and storage. It can avoid problems such as secondary storage or detours in picking tasks, which make it difficult to improve warehouse production efficiency or reduce costs.

[0082] Using a WMS (Warehouse Management System) system as an application scenario, this disclosure addresses the problem of inventory transfer recommendation algorithms for e-commerce warehouses with different SKU popularity levels, aiming to reduce operational picking difficulties and improve picking efficiency through scientific recommendation transfer algorithms. The algorithm comprises the following three main stages:

[0083] Phase 1: SKU Category Popularity Prediction

[0084] Based on historical sales and order data within a time period T, the system predicts warehouse sales data using forecasting methods such as exponential smoothing and XGBoost. It outputs sales forecasts for a given time period (e.g., week), provides a sales popularity list for each SKU, and tags all SKUs using the ABCDE classification method. The system also assigns popularity labels to different labels (e.g., labeling AB as best-selling items and other categories as slow-moving items).

[0085] Phase 2: Storage thermal frequency calculation:

[0086] The main methods for calculating thermal frequency of storage space include

[0087] Step 1: Frequency analysis of the number of storage locations within period T for each storage location

[0088] Step 2: The initial value of the thermal frequency is defined as follows, specifically, max{O i When} = 0, y i Set it to a fixed value γ, for example, 0.1.

[0089]

[0090] Among them, y i Let O be the initial value of the thermal frequency for the i-th storage location. i Let max{O} be the frequency of the quantity of goods stored in the i-th storage location within a preset period. i} represents the maximum frequency of the number of goods stored in all storage locations within a preset period.

[0091] Step 3: Storage location hot-frequency mapping conversion strategy:

[0092] (1) Based on the output of step 2, a standardized normalization method (such as Min-Max normalization) is applied to the storage hot frequency of best-selling products for the sequence x1, x, ... x. n Perform the transformation:

[0093]

[0094] Among them, h i Let x be the heat frequency value of the i-th best-selling product storage location. i Let x be the initial value of the thermal frequency for the i-th best-selling product storage location, min{x}i} represents the minimum initial value of the thermal frequency among all best-selling product storage locations, max{x} i} represents the maximum value among the initial heat frequencies of all best-selling product storage locations. New sequence h1, h2, ... h n h i Data ∈ [0,1] and dimensionless can generally be normalized before use. Min-max normalization refers to performing a linear transformation on the original data, mapping the values ​​to the range [0,1]. When max{x i}=min{x i When}>0, h i =1, when max{x i}=min{x i When} = 0, h i =0.

[0095] (2) Based on the output of step 2, the storage location heat frequency for slow-moving products is defined in a standardized way as follows: a is the storage location heat frequency for best-selling products, and b is the minimum value among the initial values ​​of the heat frequency for all best-selling product storage locations.

[0096]

[0097] Among them, h i Let ′ be the heat frequency value of the i-th slow-moving product storage location, and x be the heat frequency value of the i-th slow-moving product storage location. i Let ′ be the initial value of the heat frequency for the i-th slow-moving product storage location, min{x i '} represents the minimum initial value of the heat frequency among all slow-moving goods storage locations, max{x} i '} represents the maximum initial value of the heat frequency among all slow-moving goods storage locations. When max{x i ′}=min{x i When h > 0, i ′=1, when max{x i ′}=min{x i When ′}=0, h i =0.1.

[0098] Phase 3: Cargo Handling and Storage Operation Decisions

[0099] Model parameters:

[0100] h represents the storage thermal frequency

[0101] m represents the storage space inventory, indicating the quantity of goods stored there.

[0102] C represents storage capacity limitation.

[0103] N represents the maximum number of storage locations.

[0104] n represents the storage location number.

[0105] Weighting coefficients α, β

[0106] Decision variables:

[0107] x n This indicates whether the nth storage location has been selected as the target location for the transfer; 1 indicates that it has been selected.

[0108] y j This indicates whether the product in the j-th storage location has been selected; 1 indicates that it has been selected and moved.

[0109]

[0110]

[0111] st

[0112] For any n

[0113] Obviously, we can use various methods to handle multi-objective optimization problems. Here, we use a weighted sum approach to transform the original problem into:

[0114]

[0115] Here α+β=1, 0≤α,β≤1

[0116] Based on the mapping transformation strategy in step 2, the problem can be transformed from NLIP into two LIP problems, which can be solved using commercial solvers such as the SCIP solver.

[0117] The following is an application scenario: Select the goods to be transferred from the goods shown in Table 1 below, and select the target storage location from the storage locations shown in Table 2 below to obtain the sorting and transfer instance in Table 3 below (SKU fragment A, goods with a popularity of A). Using the method disclosed in this paper, the transfer scheme in Table 4 below (SKU fragment A) can be output.

[0118] Table 1

[0119]

[0120]

[0121] Table 2

[0122] 1 2JK434 1 2 7AB312 0.875 3 7AC161 0.875 4 4GN846 0.75 5 7AP132 0.75 6 4KW373 0.625 7 1E2071 0.5 8 7AR051 0.5 9 3RF043 0.375 … … … 10 4MF123 0.125

[0123] Table 3

[0124] 2JK434 1 1 9 7AC161 3 0.875 6 7AB312 2 0.875 7 7AP132 4 0.75 6

[0125] Table 4

[0126]

[0127] This invention provides a two-level heat calculation scheme for inventory sorting and storage transfer problems, namely SKU and storage location, as a prerequisite for the transfer scheme. It also innovatively provides a mathematical model and accurate solution scheme for inventory sorting and storage transfer problems, which can be applied to the transfer scenarios of category warehouses such as book warehouses and 3C product warehouses.

[0128] Based on the same inventive concept, referring to Figure 5 As shown, a second exemplary embodiment of this disclosure provides a warehouse management device, including: a classification module 11, a calculation module 12, and a selection module 13.

[0129] In this embodiment, the classification module 11 is used to classify the products in the target warehouse by popularity based on historical order data and determine the popularity category of the products.

[0130] In this embodiment, the calculation module 12 is used to collect the storage locations corresponding to each heat type of goods, obtain the type of storage location, and calculate the heat frequency value of the storage location based on the type of storage location.

[0131] In this embodiment, the selection module 13 is used to select a target storage location from the target warehouse based on the heat frequency value and inventory of the storage location, so as to transfer the goods to be transferred to the target storage location.

[0132] In one possible implementation, the classification module is further configured to:

[0133] Based on the historical sales data of the products in the historical order data, a preset prediction algorithm is used to predict the sales volume of the products in the target warehouse within a preset time period.

[0134] The popularity category of a product is determined by its sales volume within a preset time period.

[0135] In one possible implementation, the computing module is further configured to:

[0136] Collect the storage locations corresponding to each type of popular product, and obtain the corresponding storage location type;

[0137] The thermal frequency value of the storage location is calculated based on the type of storage location.

[0138] In one possible implementation, the computing module is further configured to:

[0139] Calculate the initial value of the thermal frequency of the storage location;

[0140] The thermal frequency value of the storage location is calculated based on the type of storage location and the initial thermal frequency value.

[0141] In one possible implementation, the initial value of the thermal frequency of the storage space is calculated in the calculation module using the following expression:

[0142]

[0143] Among them, y i Let O be the initial value of the thermal frequency for the i-th storage location. i Let max{O} be the frequency of the quantity of goods stored in the i-th storage location within a preset period. i} represents the maximum value among the frequency of the quantity of goods stored in all storage locations within a preset period. When max{O j When} = 0, y j This is a preset fixed value.

[0144] In one possible implementation, in the calculation module, the storage location types include best-selling product storage locations, and the heat frequency value of the storage location is calculated based on the storage location type and the initial heat frequency value using the following expression:

[0145]

[0146] Among them, h i Let x be the heat frequency value of the i-th best-selling product storage location. i Let x be the initial value of the thermal frequency for the i-th best-selling product storage location, min{x} i} represents the minimum initial value of the thermal frequency among all best-selling product storage locations, max{x} i} represents the maximum value among the initial thermal frequencies of all best-selling product storage locations.

[0147] In one possible implementation, in the calculation module, the types of storage locations include slow-moving goods storage locations, and the heat frequency value of the storage location is calculated based on the type of storage location and the initial heat frequency value using the following expression:

[0148]

[0149] Among them, h i Let ′ be the heat frequency value of the i-th slow-moving product storage location, and x be the heat frequency value of the i-th slow-moving product storage location. i Let ′ be the initial value of the heat frequency for the i-th slow-moving product storage location, min{x i '} represents the minimum initial value of the heat frequency among all slow-moving goods storage locations, max{x} i ′} represents the maximum value among the initial heat frequency values ​​of all slow-moving goods storage locations.

[0150] In one possible implementation, the selection module further includes:

[0151] Select multiple storage locations corresponding to the heat level of the goods to be transferred;

[0152] Select a target storage location from the plurality of storage locations based on the heat frequency value and inventory level of the storage location, such that the sum of the heat frequency value and / or inventory level of all selected target storage locations is maximized.

[0153] In one possible implementation, in the selection module, target goods and target storage locations are selected from the goods to be transferred and the plurality of storage locations based on the heat frequency value and inventory level of the storage location, using the following expression, such that the sum of the heat frequency value and / or inventory level of all selected target storage locations is maximized:

[0154]

[0155]

[0156] Where h is the thermal frequency of the nth storage location, and m n Let y be the inventory quantity of the nth storage location. j x can be either 0 or 1. n Take 0 or 1, m j Let α be the inventory quantity to be transferred in the j-th storage location, where α + β = 1, 0 ≤ α, β ≤ 1.

[0157] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0158] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of the present invention according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0159] In the second embodiment described above, any one of the classification module 11, calculation module 12, and selection module 13 can be divided into multiple modules. Alternatively, at least a portion of the functionality of one or more of these modules can be combined with at least a portion of the functionality of other modules and implemented in a single module. At least one of the classification module 11, calculation module 12, and selection module 13 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable method of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three methods. Alternatively, at least one of the classification module 11, calculation module 12, and selection module 13 can be at least partially implemented as a computer program module, which, when run, can perform corresponding functions.

[0160] Based on the same inventive concept, referring to Figure 6 As shown, the electronic device provided in this embodiment includes a processor 1110, a communication interface 1120, a memory 1130, and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 communicate with each other through the communication bus 1140.

[0161] Memory 1130 is used to store computer programs;

[0162] When processor 1110 executes a program stored in memory 1130, it implements the following warehouse management method:

[0163] Based on historical order data, various products to be stored are sorted by popularity to obtain a product sequence list;

[0164] Based on historical order data, products in the target warehouse are categorized by popularity to determine the product's popularity level.

[0165] Calculate the heat frequency value of the storage location corresponding to the product based on its heat content;

[0166] A target storage location is selected from the target warehouse based on the storage location's heat frequency value and inventory level, so that the goods to be transferred can be moved to the target storage location.

[0167] The aforementioned communication bus 1140 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus 1140 can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, it is represented by only one thick line in the figure, but this does not indicate that there is only one bus or one type of bus.

[0168] The communication interface 1120 is used for communication between the above-mentioned electronic device and other devices.

[0169] The memory 1130 may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory 1130 may also be at least one storage device located remotely from the aforementioned processor 1110.

[0170] The processor 1110 mentioned above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0171] Based on the same inventive concept, this disclosure also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the warehouse management method described above.

[0172] The computer-readable storage medium may be included in the device / apparatus described in the above embodiments; or it may exist independently and not assembled into the device / apparatus. The computer-readable storage medium carries one or more programs that, when executed, implement the warehouse management method according to embodiments of this disclosure.

[0173] According to embodiments of this disclosure, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0174] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0175] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A warehouse management method, characterized in that, Includes the following steps: Based on historical order data, products in the target warehouse are categorized by popularity to determine the product's popularity level. Collect the storage locations corresponding to each type of popular product, and obtain the corresponding storage location type; The heat frequency value of the storage location is calculated based on the type of storage location and the frequency of the quantity of goods stored in the storage location within a preset period. Select multiple storage locations corresponding to the heat level of the goods to be transferred; Based on the heat frequency value and inventory level of the storage location, a target storage location is selected from the plurality of storage locations to maximize the sum of the heat frequency value and inventory level of all selected target storage locations, so as to transfer the goods to be transferred to the target storage location.

2. The method according to claim 1, characterized in that, The process of classifying products in the target warehouse based on historical order data to determine product popularity categories includes: Based on the historical sales data of the products in the historical order data, a preset prediction algorithm is used to predict the sales volume of the products in the target warehouse within a preset time period. The popularity category of a product is determined by its sales volume within a preset time period.

3. The method according to claim 1, characterized in that, The calculation of the hot frequency value of a storage location based on the type of storage location and the frequency of the quantity of goods stored in that location within a preset period includes: The initial value of the heat frequency of the storage location is calculated based on the quantity and frequency of the goods stored in the storage location within a preset period. The thermal frequency value of the storage location is calculated based on the type of storage location and the initial thermal frequency value.

4. The method according to claim 3, characterized in that, The initial value of the thermal frequency of the storage location is calculated using the following expression: ； in, Let the initial value of the thermal frequency be the i-th storage location. Let i be the frequency of the quantity of goods stored in the i-th storage location within a preset period. The maximum value among the frequency of the quantity of goods stored in all storage locations within a preset period, when .

5. The method according to claim 3, characterized in that, The storage locations include best-selling product storage locations. The heat frequency value of the storage location is calculated based on the storage location type and the initial heat frequency value using the following expression: ； in, Let i be the heat frequency value of the i-th best-selling product storage location. Let i be the initial value of the thermal frequency for the i-th best-selling product storage location. The minimum initial value of the thermal frequency among all best-selling product storage locations. The maximum value among the initial thermal frequencies of all best-selling product storage locations.

6. The method according to claim 3, characterized in that, The storage locations include those for slow-moving goods. The heat frequency value of the storage location is calculated based on the type of storage location and the initial heat frequency value using the following expression: ； in, Let be the heat frequency value of the i-th slow-moving product storage location. Let the initial value of the heat frequency be the i-th slow-moving product storage location. The minimum initial value of the heat frequency among all slow-moving goods storage locations. The maximum value among the initial heat frequency values ​​of all slow-moving goods storage locations.

7. The method according to claim 1, characterized in that, The following expression is used to select target goods and target storage locations from the goods to be transferred and the plurality of storage locations based on the heat frequency value and inventory level of the storage locations, such that the sum of the heat frequency value and inventory level of all selected target storage locations is maximized: max ; s.t. ; in, For the storage thermal frequency of the nth storage location, Let n be the inventory level of the nth storage location. Take 0 or 1, Take 0 or 1, Let J represent the inventory quantity to be transferred in the j-th storage location. α+β=1， α,β , The storage capacity limit for the nth storage location.

8. A warehouse management device, characterized in that, include: The classification module is used to classify products in the target warehouse based on their popularity data and determine the popularity categories of the products. The calculation module is used to collect the storage locations corresponding to each type of product in terms of popularity, obtain the type of storage location, and calculate the popularity frequency value of the storage location based on the type of storage location and the frequency of the quantity of products stored in the storage location within a preset period. The selection module is used to select multiple storage locations corresponding to the heat level of the goods to be transferred; and select a target storage location from the multiple storage locations based on the heat frequency value and inventory of the storage locations, so as to maximize the sum of the heat frequency value and inventory of all selected target storage locations, so as to transfer the goods to be transferred to the target storage location.

9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the warehouse management method according to any one of claims 1-7.

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

Images