Method, device and equipment for dynamically distributing storage locations

Abstract

The invention provides a storage location dynamic allocation method, device and equipment. The method comprises the following steps: carrying out attribute identification on storage materials; based on the attribute identifier, clustering materials with consistent attribute characteristics to generate a correlation sequence; carrying out storage location coarse distribution on the materials according to the correlation sequence, and determining an initial storage area of the materials; dividing the initial storage area into fine placement areas with different frequency grades according to in-out storage big data; and according to the correlation sequence and the fine placement area, the accurate storage location of the materials is determined. Attribute identification is carried out on the warehousing materials, the correlation sequence is generated through clustering, the storage location coarse distribution is firstly completed to determine the initial storage area, then the fine storage areas of different frequency grades are divided according to the warehousing and ex-warehouse big data, the accurate storage location of the materials is determined in combination with the correlation sequence, and the warehousing and ex-warehouse efficiency of the three-dimensional warehouse is effectively improved through the dynamic distribution mode.

Description

TECHNICAL FIELD

[0001] The present application relates to the warehouse management technical field, and particularly relates to a warehouse position dynamic allocation method, device and equipment. BACKGROUND

[0002] Under the background of intelligent manufacturing, automated stereoscopic warehouse becomes the core facility of manufacturing enterprises warehouse with high space utilization, the market scale expands rapidly, the factory's efficiency of in-out warehouse rhythm, the requirement of fine division of goods storage area is significantly improved, and efficient warehouse partition is the key to improve the efficiency of in-out warehouse and meet the precise storage of goods.

[0003] The current stereoscopic warehouse position adopts a static division mode, relies on manual division according to goods types or initial turnover rate, and lacks dynamic adjustment capability for actual in-out warehouse data; some partition schemes only consider the turnover speed of goods, and do not comprehensively adapt to the storage characteristics of goods, resulting in insufficient rationality of partition.

[0004] Therefore, the existing stereoscopic warehouse position partition method has the problem of low in-out warehouse efficiency. SUMMARY

[0005] Embodiments of the present application provide a warehouse position dynamic allocation method, device and equipment, aiming at solving the problem of low in-out warehouse efficiency of the existing stereoscopic warehouse position partition method.

[0006] In a first aspect, the embodiments of the present application provide a warehouse position dynamic allocation method, which comprises: attribute identification of in-warehouse materials; based on the attribute identification, clustering materials with consistent attribute characteristics to generate a related sequence; performing rough allocation of warehouse positions for materials according to the related sequence to determine the initial storage area of the materials; dividing the initial storage area into fine storage areas of different frequency levels according to in-out warehouse big data; determining the precise warehouse position of the materials according to the related sequence and the fine storage area.

[0007] In a second aspect, the embodiments of the present application also provide a warehouse position dynamic allocation device, which comprises: an identification unit configured to perform attribute identification of in-warehouse materials; a clustering unit configured to cluster materials with consistent attribute characteristics based on the attribute identification to generate a related sequence; a rough allocation unit configured to perform rough allocation of warehouse positions for materials according to the related sequence to determine the initial storage area of the materials; a division unit configured to divide the initial storage area into fine storage areas of different frequency levels according to in-out warehouse big data; A determining unit is configured to determine the accurate storage location of the material according to the correlation sequence and the fine storage area.

[0008] In a third aspect, an embodiment of the present application further provides an electronic device, which is a server or a terminal. The electronic device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor of the server and the processor of the terminal implement the method of the first aspect when executing the computer program.

[0009] In a fourth aspect, an embodiment of the present application further provides a computer readable storage medium, which stores a computer program. The computer program comprises program instructions executable by a processor to implement the method of the first aspect.

[0010] The present application provides a storage location dynamic allocation method, device and equipment. The method comprises: identifying the attributes of the materials entering the warehouse; clustering the materials with consistent attribute characteristics based on the attribute identification to generate a correlation sequence; performing coarse storage location allocation on the materials according to the correlation sequence to determine the initial storage area of the materials; dividing the initial storage area into fine storage areas of different frequency levels according to the warehouse entry and exit big data; and determining the accurate storage location of the materials according to the correlation sequence and the fine storage area. The present application identifies the attributes of the materials entering the warehouse, clusters the materials to generate a correlation sequence, performs coarse storage location allocation to determine the initial storage area, divides the initial storage area into fine storage areas of different frequency levels according to the warehouse entry and exit big data, and determines the accurate storage location of the materials in combination with the correlation sequence. The dynamic allocation method effectively improves the warehouse entry and exit efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0012] Figure 1 The flowchart of the storage location dynamic allocation method provided by the embodiment of the present application is shown in the figure. Figure 2 The schematic block diagram of the storage location dynamic allocation device provided by the embodiment of the present application is shown in the figure. Figure 3 The schematic block diagram of the electronic device provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0013] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of the present application.

[0014] It should be understood that the terms "comprising" and "including" as used in the specification and the appended claims indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0015] It should also be understood that the terms used in the present application specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the present application specification and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0016] It should be further understood that the term "and / or" used in the present application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0017] Figure 1 A flowchart of a warehouse position dynamic allocation method provided by the embodiments of the present application is shown in FIG. 1. As shown in FIG. 1, the method comprises the following steps S110-S150. Figure 1

[0018] S110, attribute identification is performed on the warehousing materials.

[0019] In the present embodiment, attribute identification is performed on the warehousing materials, and the attribute identification at least contains one or more of the following: material category relevance, storage environment requirement, and warehouse-in / out frequency characteristics; wherein the storage environment requirement includes temperature and humidity requirements.

[0020] Specifically, the attribute identification corresponding to the warehousing materials is given by defining the first letter of the bar code of the warehousing materials. Taking dental floss as an example, the first letter of the bar code is YS, and the corresponding attribute identification is "category = oral care category, storage requirement = normal temperature, warehouse-in / out frequency = high frequency, and quantity = many". In this way, the core attribute characteristics of the materials can be accurately defined, and data basis for subsequent clustering sequence generation is provided.

[0021] S120, based on the attribute identification, materials with consistent attribute characteristics are clustered to generate a relevant sequence.

[0022] ​In this embodiment, the attribute identifier includes at least one or more of the following: material type association, storage environment requirements, and inbound / outbound frequency characteristics. Based on the attribute identifier, materials with consistent attribute characteristics are clustered to generate related sequences. For example, based on the attribute identifier, oral care materials, materials requiring room temperature storage, and materials with high inbound / outbound frequency are grouped together by similarity. For example, YS-dental floss, YS-toothbrush, and YKS-mouthwash are grouped into the same related sequence to provide a classification basis for subsequent storage location allocation.

[0023] During clustering, it is necessary to avoid functionally conflicting zones (such as low-temperature storage materials and high-frequency flow materials at room temperature) to prevent differences in storage environment and inbound / outbound cycle time from affecting overall operational efficiency. Specifically, "functionally conflicting zones" can be understood as: the core functions of a certain zone (such as storage environment, operational cycle time, and load-bearing capacity) do not match the attribute requirements of the materials to be clustered (such as the required temperature and humidity, inbound / outbound frequency, and load-bearing adaptability). If such materials are assigned to this zone, it will cause storage safety risks (such as overloading and crushing shelves), decreased operational efficiency (such as placing high-frequency materials in a long-distance, low-frequency zone), or damage to material characteristics (such as low-temperature materials deteriorating when placed in a room-temperature zone). In other words, "avoiding functionally conflicting zones" is essentially to ensure that the clustered materials are "compatible" with the zone functions: for example, low-temperature materials are only assigned to low-temperature functional zones, high-frequency materials are only assigned to high-flow operational zones, and heavy materials are only assigned to high-load-bearing zones to avoid damaging material characteristics or reducing the efficiency of automated warehouses due to functional contradictions.

[0024] S130. Perform coarse allocation of storage locations for materials according to the relevant sequence to determine the initial storage area of ​​the materials.

[0025] In this embodiment, the coarse allocation of storage locations is based on the generated relevant sequences and combined with the basic functional zoning layout of the automated storage and retrieval system to lock the initial storage area of ​​the materials. This process does not involve the precise delineation of specific storage location coordinates, but only completes the general category definition of the material storage range.

[0026] Specifically, for the clustered "oral care category room temperature high frequency" related sequences (including YS-dental floss, YS-toothbrush, YKS-mouthwash), based on the "storage requirement = room temperature" feature in their attribute identifiers, all materials under this related sequence are coarsely allocated to the "room temperature functional area" of the automated warehouse; for the "skincare category low temperature low frequency" related sequences (including MS-face cream, YS-eye cream, JH-serum), based on their core attribute "storage requirement = low temperature", the materials in this sequence are coarsely allocated to the "low temperature functional area" of the automated warehouse.

[0027] This invention completes coarse allocation by matching relevant sequences with the basic functional zoning of the automated warehouse, which can quickly determine the initial storage area of ​​materials and avoid materials with different functional attributes from being mixed into unsuitable areas, laying the foundation for subsequent fine allocation of storage areas based on big data of inbound and outbound operations.

[0028] In one embodiment, step S130 includes: obtaining the corresponding core attributes of the material according to the relevant sequence; using the warehouse functional area that satisfies the core attributes of the material as the initial storage area of ​​the corresponding material; the warehouse functional area includes at least a normal temperature storage area and a low temperature storage area.

[0029] In this embodiment, for each group of related sequences generated by clustering, the core material attributes corresponding to the related sequence are extracted. The core material attributes are based on the key attribute identifiers during clustering, focusing on the storage environment requirements dimension. Subsequently, the preset warehouse functional areas in the automated warehouse (the warehouse functional areas include at least ambient temperature storage areas and low temperature storage areas) are retrieved, and the warehouse functional areas that can meet the core material attributes are directly designated as the initial storage areas for all materials under the corresponding related sequence.

[0030] Taking practical applications as an example, for the oral care series consisting of "YS-dental floss, YS-toothbrush, YKS-mouthwash", the core attribute of its materials is "storage environment requirement = room temperature". Therefore, the room temperature storage area is determined as the initial storage area for all materials in this series. For the skincare series consisting of "MS-face cream, YS-eye cream, JH-serum", the core attribute of its materials is "storage environment requirement = low temperature". Therefore, the low temperature storage area is taken as its initial storage area. This method can quickly define the broad categories of material storage, avoid materials from being mixed into functionally conflicting areas, and lay the foundation for subsequent detailed area division.

[0031] S140. Based on the big data of inbound and outbound data, the initial storage area is divided into fine storage areas of different frequency levels.

[0032] In this embodiment, the fine-grained storage area is divided with the initial storage area as the boundary. Based on the inbound and outbound big data analysis of the material inbound and outbound frequency, flow rate and other inbound and outbound characteristics, each initial storage area is further divided into fine-grained storage areas of different frequency levels.

[0033] Specifically, the historical and real-time inbound and outbound data of all materials in the initial storage area (i.e., inbound and outbound big data) are statistically analyzed, focusing on core inbound and outbound characteristics such as material inbound and outbound frequency and flow rate. Based on this, three fine-grained storage areas of frequency levels are defined: high-frequency fast flow area, medium-frequency medium flow area, and low-frequency slow flow area.

[0034] Taking the ambient temperature storage area (the initial storage area for oral care materials) as an example, based on big data on inbound and outbound movements, the core materials with highly concentrated inbound and outbound flows within this area are identified. Storage locations suitable for high-frequency operations are designated as high-frequency, fast-flow zones, specifically for these high-frequency materials. The remaining storage locations are then divided into medium-frequency, medium-flow zones and low-frequency, slow-flow zones based on differences in material flow rates, corresponding to materials with different turnover frequencies. Similarly, the low-temperature storage area (the initial storage area for skincare materials) is divided into finely defined zones based on historical and real-time inbound and outbound data. This approach maintains the functional boundaries of the initial storage area while achieving refined zoning within the area, providing a precise spatial basis for subsequent accurate storage location matching.

[0035] In one embodiment, step S140 includes: dividing the initial storage area into fine-grained areas of different frequency levels according to a preset storage location ratio rule and the inbound and outbound big data.

[0036] In this embodiment, the storage location ratio rule is a rule that allocates storage location ratios to materials with different turnover speeds based on the inbound and outbound characteristics such as the frequency of material entry and exit and the proportion of flow rate. The core is that high-frequency materials have a high flow rate but a low storage location ratio, while medium-frequency and low-frequency materials are matched in turn.

[0037] For example, in the initial storage area for oral care products (corresponding to the "oral care products room temperature high frequency" related sequence), the inflow and outflow of high-frequency materials (toothpaste, toothbrush) accounts for 70% of the total flow in this area, and 20% of the storage space is allocated (i.e., the high-frequency fast flow zone); the flow of medium-frequency materials (dental floss) accounts for 20% of the total flow in this area, and 30% of the storage space is allocated (i.e., the medium-frequency medium flow zone); and the flow of low-frequency materials (mouthwash refills) accounts for 10% of the total flow in this area, and 50% of the storage space is allocated (i.e., the low-frequency slow flow zone).

[0038] This invention divides the initial storage area into finely divided areas of different frequency levels based on preset storage location ratio rules and the big data of inbound and outbound data, thereby achieving storage location optimization for materials with different turnover rates within the same related sequence.

[0039] In one embodiment, after dividing the initial storage area into finely divided areas of different frequency levels according to the preset storage location ratio rule and the inbound and outbound big data, the method further includes: dynamically adjusting the relevant sequence and the storage location ratio rule based on the inbound data.

[0040] In this embodiment, the entry and exit characteristics of materials are extracted based on the inbound data; the relevant sequences to which the materials belong are dynamically adjusted based on the entry and exit characteristics of the materials, and the storage location ratio rules of the corresponding initial storage area are updated synchronously to adapt to the real-time changes in material flow and improve the utilization rate of storage location resources and the efficiency of storage and retrieval operations.

[0041] In one embodiment, step S140 includes: analyzing the entry and exit characteristics of materials in the initial storage area based on the inbound and outbound big data to obtain analysis results; dividing the materials into three frequency levels: high frequency, medium frequency, and low frequency, based on the analysis results; and defining corresponding spatial boundaries for each frequency level within the initial storage area to form three fine storage areas: high frequency fast flow area, medium frequency medium flow area, and low frequency slow flow area.

[0042] In this embodiment, the division of the fine storage area is based on the initial storage area as the boundary, and the frequency level division and spatial definition are completed according to the big data of inbound and outbound data. Specifically, firstly, based on the big data of inbound and outbound data, the inbound and outbound characteristics of materials in the initial storage area (covering core dimensions such as inbound and outbound frequency and flow rate) are statistically analyzed in multiple dimensions to form analysis results. Then, based on the analysis results, all materials in the initial storage area are divided into three frequency levels: high frequency, medium frequency, and low frequency. In the initial storage area, a corresponding spatial boundary is defined for each frequency level to form three fine storage areas: high frequency fast flow area, medium frequency medium flow area, and low frequency slow flow area.

[0043] Taking the ambient temperature storage area (the initial storage area for oral care materials) as an example, based on big data on inbound and outbound movements, the core materials with highly concentrated inbound and outbound flows within this area are identified. Storage locations suitable for high-frequency operations are designated as high-frequency, fast-flow zones, specifically for these high-frequency materials. The remaining storage locations are then divided into medium-frequency, medium-flow zones and low-frequency, slow-flow zones based on the material flow ratios, corresponding to materials with different turnover frequencies. Similarly, the low-temperature storage area (the initial storage area for skincare materials) is divided into finely defined zones based on historical and real-time inbound and outbound data. This approach maintains the functional boundaries of the initial storage area while achieving refined zoning within the area, providing a precise spatial basis for subsequent accurate storage location matching.

[0044] S150. Determine the precise storage location of the material based on the relevant sequence and the fine distribution area.

[0045] In this embodiment, the determination of precise storage location is based on two core criteria: related sequence and fine storage area. In specific operation, the core attribute characteristics of the related sequence to which the material belongs (including type correlation, storage environment requirements, and frequency level) are retrieved first, and then the fine storage area corresponding to the material is located. Subsequently, following the principle of "adjacent storage of related materials", adjacent and suitable precise storage locations are delineated for materials of the same related sequence within the fine storage area.

[0046] Taking the "oral care category room temperature high frequency" related sequence (including YS-dental floss, YS-toothbrush, YKS-mouthwash) as an example, firstly, based on the core material attributes of this related sequence, the high frequency and fast flow zone under the room temperature storage area is accurately matched; then, within this zone, following the principle of "adjacent storage of related materials", adjacent and precise storage locations are allocated for dental floss, toothbrush, and mouthwash.

[0047] For the "skincare low-temperature, low-frequency" related sequences (including MS-face cream, YS-eye cream, JH-serum), we first match them to the low-frequency, slow-flow zone under the low-temperature storage area. Then, within this zone, following the principle of "adjacent storage of related materials", we assign adjacent and precise storage locations to face cream, eye cream, and serum.

[0048] In one embodiment, step S150 includes: within the fine-grained area, determining the available storage location that matches the core attribute characteristics corresponding to the relevant sequence as the precise storage location of the corresponding material.

[0049] In this embodiment, within the specified storage area, the core attribute features corresponding to the relevant sequence are first obtained (covering three core dimensions: category correlation, storage environment requirements, and frequency level). Then, the available storage locations within the specified storage area are searched, and the available storage locations that match the aforementioned core attribute features such as category correlation, storage environment requirements, and frequency level are determined as the precise storage locations for the corresponding materials.

[0050] Taking the "oral care category room temperature high frequency" related sequence (including YS-dental floss, YS-toothbrush, YKS-mouthwash) as an example, firstly, based on the core material attributes of this related sequence, the high frequency and fast flow zone under the room temperature storage area is accurately matched; then, within this zone, following the principle of "adjacent storage of related materials", adjacent and precise storage locations are allocated for dental floss, toothbrush, and mouthwash.

[0051] For the "skincare low-temperature, low-frequency" related sequences (including MS-face cream, YS-eye cream, JH-serum), we first match them to the low-frequency, slow-flow zone under the low-temperature storage area. Then, within this zone, following the principle of "adjacent storage of related materials", we assign adjacent and precise storage locations to face cream, eye cream, and serum.

[0052] In one embodiment, after determining the idle storage location that matches the core attribute features corresponding to the relevant sequence as the precise storage location of the corresponding material within the fine storage area, the method further includes: if a non-target material is placed in the precise storage location, then performing a storage transfer operation on the non-target material based on the attribute identifier of the non-target material or its relevant sequence.

[0053] In this embodiment, if non-target materials are placed in the precise storage location (i.e., a small amount of materials are not completely removed from the precise storage location), the non-target materials are transferred to a suitable storage location based on the attribute identifiers of the non-target materials (such as type association, storage environment requirements, and inbound / outbound frequency characteristics) or their related sequences.

[0054] In summary, this invention identifies the attributes of incoming materials and generates relevant sequences through clustering. It first completes the coarse allocation of storage locations to determine the initial storage area, and then divides the storage areas into fine-grained areas of different frequency levels based on the big data of inbound and outbound operations. Combined with the relevant sequences, it determines the precise storage location of the materials. This dynamic allocation method effectively improves the inbound and outbound efficiency of automated warehouses.

[0055] Figure 2 This is a schematic block diagram of a dynamic storage location allocation device provided in an embodiment of the present invention. Figure 2 As shown, corresponding to the above-described dynamic storage location allocation method, the present invention also provides a dynamic storage location allocation device, which is configured in the controller of the dynamic storage location allocation system. For details, please refer to... Figure 2 The dynamic allocation device 700 for storage locations includes: Identification unit 701 is used to identify the attributes of incoming materials; Clustering unit 702 is used to cluster materials with consistent attribute characteristics based on the attribute identifier, and generate related sequences; The coarse allocation unit 703 is used to perform coarse allocation of storage locations for materials according to the relevant sequence, and to determine the initial storage area of ​​the materials; The partitioning unit 704 is used to divide the initial storage area into fine-grained areas of different frequency levels based on the big data of inbound and outbound data; The determining unit 705 is used to determine the precise storage location of the material based on the relevant sequence and the fine distribution area.

[0056] In some embodiments, when the partitioning unit 704 performs the step of dividing the initial storage area into finer-grained areas of different frequency levels based on the large amount of data entering and leaving the warehouse, it is specifically used for: Based on the preset storage location ratio rules and the inbound and outbound big data, the initial storage area is divided into fine-grained areas with different frequency levels.

[0057] In some embodiments, after performing the step of dividing the initial storage area into finely divided areas of different frequency levels according to preset storage location ratio rules and the large amount of inbound and outbound data, the partitioning unit 704 is further configured to: The relevant sequences and storage location ratio rules are dynamically adjusted based on the inbound data.

[0058] In some embodiments, when the coarse allocation unit 703 performs the step of performing coarse allocation of storage locations for materials according to the relevant sequence to determine the initial storage area of ​​the materials, it is specifically used for: Obtain the corresponding core attributes of the material according to the relevant sequence; use the warehouse functional area that satisfies the core attributes of the material as the initial storage area of ​​the corresponding material; the warehouse functional area includes at least a normal temperature storage area and a low temperature storage area.

[0059] In some embodiments, when the determining unit 705 performs the step of determining the precise storage location of the material based on the relevant sequence and the fine distribution area, it is specifically used for: Within the specified area, the available storage locations that match the core attribute characteristics corresponding to the relevant sequence are determined as the precise storage locations for the corresponding materials.

[0060] In some embodiments, after performing the step of determining the available storage location that matches the core attribute features corresponding to the relevant sequence as the precise storage location of the corresponding material within the fine-grained area, the determining unit 705 is further configured to: If a non-target material is placed in the precise storage location, the non-target material will be transferred to another storage location based on its attribute identifier or related sequence.

[0061] In some embodiments, when the partitioning unit 704 performs the step of dividing the initial storage area into finer-grained areas of different frequency levels based on the large amount of data entering and leaving the warehouse, it is specifically used for: Based on the inbound and outbound big data, the inbound and outbound characteristics of materials in the initial storage area are analyzed to obtain analysis results. Based on the analysis results, the materials are divided into three frequency levels: high frequency, medium frequency, and low frequency. In the initial storage area, corresponding spatial boundaries are defined for each frequency level to form three fine storage areas: high frequency fast flow area, medium frequency medium flow area, and low frequency slow flow area.

[0062] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned dynamic allocation device for storage locations and each unit can be referred to the corresponding description in the foregoing method embodiments. For the sake of convenience and brevity, it will not be repeated here.

[0063] The aforementioned dynamic storage location allocation device can be implemented as a computer program, which can, for example... Figure 3 It runs on the electronic device shown.

[0064] Please see Figure 3 , Figure 3This is a schematic block diagram of an electronic device provided in an embodiment of the present invention. The electronic device 800 can be a terminal or a server. The terminal can be an electronic device with communication functions. The server can be a standalone server or a server cluster composed of multiple servers.

[0065] See Figure 3 The electronic device 800 includes a processor 802, a memory, and a network interface 805 connected via a system bus 801. The memory may include a non-volatile storage medium 803 and internal memory 804.

[0066] The non-volatile storage medium 803 may store an operating system 8031 ​​and a computer program 8032. The computer program 8032 includes program instructions that, when executed, cause the processor 802 to perform a dynamic allocation method for storage locations.

[0067] The processor 802 provides computing and control capabilities to support the operation of the entire electronic device 800.

[0068] The internal memory 804 provides an environment for the execution of the computer program 8032 in the non-volatile storage medium 803. When the processor 802 corresponding to the server and the processor 802 corresponding to the terminal execute the computer program 8032 at the same time, a dynamic allocation method for storage space is implemented.

[0069] This network interface 805 is used for network communication with other devices. Those skilled in the art will understand that... Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the electronic device 800 to which the present invention is applied. The specific electronic device 800 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0070] The processor 802 is used to run a computer program 8032 stored in the memory to perform the following steps: The incoming materials are identified by attributes; based on the attribute identification, materials with consistent attribute characteristics are clustered to generate relevant sequences; coarse allocation of storage locations is performed on the materials according to the relevant sequences to determine the initial storage area of ​​the materials; the initial storage area is divided into fine storage areas of different frequency levels according to the big data of inbound and outbound data; the precise storage location of the materials is determined according to the relevant sequences and the fine storage areas.

[0071] In some embodiments, when the processor 802 divides the initial storage area into finely divided areas of different frequency levels based on the large amount of data entering and leaving the database, the specific steps are as follows: Based on the preset storage location ratio rules and the inbound and outbound big data, the initial storage area is divided into fine-grained areas with different frequency levels.

[0072] In some embodiments, after the processor 802 performs the step of dividing the initial storage area into finely divided areas of different frequency levels according to the preset storage location ratio rules and the inbound and outbound big data, it also performs the following steps: The relevant sequences and storage location ratio rules are dynamically adjusted based on the inbound data.

[0073] In some embodiments, when the processor 802 performs the step of coarse allocation of storage locations for materials based on the relevant sequence to determine the initial storage area of ​​the materials, the following steps are specifically implemented: Obtain the corresponding core attributes of the material according to the relevant sequence; use the warehouse functional area that satisfies the core attributes of the material as the initial storage area of ​​the corresponding material; the warehouse functional area includes at least a normal temperature storage area and a low temperature storage area.

[0074] In some embodiments, when the processor 802 determines the precise storage location of the material based on the relevant sequence and the fine-grained area, it specifically implements the following steps: Within the specified area, the available storage locations that match the core attribute characteristics corresponding to the relevant sequence are determined as the precise storage locations for the corresponding materials.

[0075] In some embodiments, after determining the available storage location corresponding to the core attribute features of the relevant sequence as the precise storage location of the corresponding material within the fine-grained area, the processor 802 further implements the following steps: If a non-target material is placed in the precise storage location, the non-target material will be transferred to another storage location based on its attribute identifier or related sequence.

[0076] In some embodiments, when the processor 802 divides the initial storage area into finely divided areas of different frequency levels based on the large amount of data entering and leaving the database, the specific steps are as follows: Based on the inbound and outbound big data, the inbound and outbound characteristics of materials in the initial storage area are analyzed to obtain analysis results. Based on the analysis results, the materials are divided into three frequency levels: high frequency, medium frequency, and low frequency. In the initial storage area, corresponding spatial boundaries are defined for each frequency level to form three fine storage areas: high frequency fast flow area, medium frequency medium flow area, and low frequency slow flow area.

[0077] It should be understood that, in this embodiment of the invention, the processor 802 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0078] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program includes program instructions and can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0079] Therefore, the present invention also provides a storage medium. This storage medium can be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program includes program instructions. When executed by a processor, the program instructions cause the processor to perform the following steps: The incoming materials are identified by attributes; based on the attribute identification, materials with consistent attribute characteristics are clustered to generate relevant sequences; coarse allocation of storage locations is performed on the materials according to the relevant sequences to determine the initial storage area of ​​the materials; the initial storage area is divided into fine storage areas of different frequency levels according to the big data of inbound and outbound data; the precise storage location of the materials is determined according to the relevant sequences and the fine storage areas.

[0080] In one embodiment, when the processor executes the program instructions to divide the initial storage area into finer regions of different frequency levels based on the large amount of data entering and leaving the database, the specific steps are as follows: Based on the preset storage location ratio rules and the inbound and outbound big data, the initial storage area is divided into fine-grained areas with different frequency levels.

[0081] In one embodiment, after the processor executes the program instructions to divide the initial storage area into finely divided areas of different frequency levels according to preset storage location ratio rules and the large amount of data entering and leaving the warehouse, the processor further implements the following steps: The relevant sequences and storage location ratio rules are dynamically adjusted based on the inbound data.

[0082] In one embodiment, when the processor executes the program instructions to perform coarse allocation of storage locations for materials according to the relevant sequence and determines the initial storage area of ​​the materials, the processor specifically implements the following steps: Obtain the corresponding core attributes of the material according to the relevant sequence; use the warehouse functional area that satisfies the core attributes of the material as the initial storage area of ​​the corresponding material; the warehouse functional area includes at least a normal temperature storage area and a low temperature storage area.

[0083] In one embodiment, when the processor executes the program instructions to determine the precise location of the material based on the relevant sequence and the fine-grained area, it specifically implements the following steps: Within the specified area, the available storage locations that match the core attribute characteristics corresponding to the relevant sequence are determined as the precise storage locations for the corresponding materials.

[0084] In one embodiment, after the processor executes the program instructions to determine the available storage location corresponding to the core attribute characteristics of the relevant sequence as the precise storage location of the corresponding material within the fine-grained area, it further performs the following steps: If a non-target material is placed in the precise storage location, the non-target material will be transferred to another storage location based on its attribute identifier or related sequence.

[0085] In one embodiment, when the processor executes the program instructions to divide the initial storage area into finer regions of different frequency levels based on the large amount of data entering and leaving the database, the specific steps are as follows: Based on the inbound and outbound big data, the inbound and outbound characteristics of materials in the initial storage area are analyzed to obtain analysis results. Based on the analysis results, the materials are divided into three frequency levels: high frequency, medium frequency, and low frequency. In the initial storage area, corresponding spatial boundaries are defined for each frequency level to form three fine storage areas: high frequency fast flow area, medium frequency medium flow area, and low frequency slow flow area.

[0086] The storage medium can be any computer-readable storage medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0087] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0088] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is merely a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0089] The steps in the method of this invention can be adjusted, merged, or reduced in order according to actual needs. The units in the device of this invention can be merged, divided, or reduced according to actual needs. Furthermore, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0090] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0091] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for dynamically allocating storage locations, characterized in that, The method comprises: attribute identification is performed on the incoming materials; materials with consistent attribute characteristics are clustered based on the attribute identification to generate a relevant sequence; initial storage areas of the materials are determined by performing coarse allocation of storage locations to the materials according to the relevant sequence; the initial storage areas are divided into fine storage areas of different frequency levels according to the incoming and outgoing warehouse big data; accurate storage locations of the materials are determined according to the relevant sequence and the fine storage areas.

2. The storage location dynamic allocation method according to claim 1, characterized in that, The initial storage areas are divided into fine storage areas of different frequency levels according to the incoming and outgoing warehouse big data, which comprises: The initial storage areas are divided into fine storage areas of different frequency levels according to the incoming and outgoing warehouse big data and the preset storage location proportion rule.

3. The dynamic storage allocation method according to claim 2, wherein, After the initial storage areas are divided into fine storage areas of different frequency levels according to the incoming and outgoing warehouse big data and the preset storage location proportion rule, it further comprises: The relevant sequence and the storage location proportion rule are dynamically adjusted based on the incoming data.

4. The dynamic storage allocation method according to claim 1, wherein, The initial storage areas of the materials are determined by performing coarse allocation of storage locations to the materials according to the relevant sequence, which comprises: The corresponding material core attributes are obtained according to the relevant sequence; The warehouse functional areas that meet the material core attributes are taken as the initial storage areas of the corresponding materials; the warehouse functional areas at least include normal-temperature storage areas and low-temperature storage areas.

5. The dynamic storage allocation method according to claim 1, wherein, The accurate storage locations of the materials are determined according to the relevant sequence and the fine storage areas, which comprises: In the fine storage areas, the idle storage locations that adapt to the core attribute characteristics corresponding to the relevant sequence are determined as the accurate storage locations of the corresponding materials.

6. The dynamic storage allocation method according to claim 5, wherein, After the idle storage locations that adapt to the core attribute characteristics corresponding to the relevant sequence are determined as the accurate storage locations of the corresponding materials in the fine storage areas, it further comprises: If the non-target materials are placed on the accurate storage locations, the non-target materials are moved based on the attribute identification of the non-target materials or the relevant sequence to which the non-target materials belong.

7. The dynamic storage allocation method according to claim 1, wherein, The initial storage areas are divided into fine storage areas of different frequency levels according to the incoming and outgoing warehouse big data, which comprises: The outgoing and incoming characteristics of the materials in the initial storage areas are analyzed according to the incoming and outgoing warehouse big data to obtain an analysis result; The materials are divided into three frequency levels, i.e., high-frequency level, medium-frequency level and low-frequency level, according to the analysis result; In the initial storage areas, the corresponding space boundaries are demarcated for each of the frequency levels to form three fine storage areas, i.e., high-frequency fast-flow area, medium-frequency medium-flow area and low-frequency slow-flow area.

8. A storage location dynamic allocation apparatus, characterized by comprising: The device comprises: An identification unit configured to perform attribute identification on the incoming materials; A clustering unit configured to cluster materials with consistent attribute characteristics based on the attribute identification to generate a relevant sequence; A coarse allocation unit configured to determine initial storage areas of the materials by performing coarse allocation of storage locations to the materials according to the relevant sequence; A division unit configured to divide the initial storage areas into fine storage areas of different frequency levels according to the incoming and outgoing warehouse big data; A determination unit configured to determine accurate storage locations of the materials according to the relevant sequence and the fine storage areas.

9. An electronic device, which is a server or a terminal, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processors of the server and the processors of the terminal simultaneously execute the computer program to implement the storage location dynamic allocation method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the warehouse location dynamic allocation method in any one of claims 1-7.

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