Intelligent warehouse management method, device and equipment and storage medium

Abstract

The invention belongs to the technical field of warehouse management, and discloses an intelligent warehouse management method and device, equipment and a storage medium. The method comprises the steps of obtaining ex-warehouse data of electric power materials, and determining an association rule between the electric power materials based on the ex-warehouse data; clustering the electric power materials according to the association rule; establishing a storage location optimization model based on a clustering result; using the storage location optimization model to adjust the storage location when the electric power materials are stored; and carrying out optimization management on the adjusted storage positions. According to the method, the storage location optimization model established after clustering of the association rules is utilized to adjust the storage location of the electric power, so that the picking path is reduced, the internal association of material delivery is mined through the association rules, the associated materials are stored in the similar storage locations, the picking path is compressed, and warehouse management is accurate and efficient.

Description

Technical Field

[0001] The present invention relates to the field of warehouse management technology, and in particular to an intelligent warehouse management method, device, equipment and storage medium. Background Art

[0002] Due to problems in the internal management model of power companies, the company's material management is not standardized, such as unreasonable storage space layout, weak intensive material management, and the lack of extensive application of advanced logistics technology in warehousing, which ultimately leads to problems such as low warehousing and logistics efficiency.

[0003] The current main means is to rely on the material management model of manual operation, but this method can no longer meet the current needs for accurate, efficient and convenient management of power materials, and there are problems with chaotic and inefficient warehouse management. Summary of the Invention

[0004] The main purpose of the present invention is to provide an intelligent warehouse management method, device, equipment and storage medium, aiming to solve the technical problem of the current chaotic and inefficient warehouse management.

[0005] To achieve the above object, the present invention provides an intelligent warehouse management method, which includes the following steps:

[0006] Obtaining outbound data of electric power materials, and determining association rules between electric power materials based on the outbound data, wherein the association rules represent associations between the electric power materials and are used to classify the electric power materials;

[0007] Clustering the electric power materials according to the association rules;

[0008] Establishing a storage optimization model based on the clustering results, wherein the optimization model is used to adjust the storage location of electric power materials;

[0009] Using the storage optimization model to adjust the storage location of power materials;

[0010] Optimize the management of the adjusted storage locations.

[0011] In some embodiments, determining association rules between electric power materials based on the outbound data includes:

[0012] Determine all initial frequent item sets based on the outbound data;

[0013] Filtering a target frequent item set from the initial frequent item set according to a preset minimum support threshold;

[0014] The association rules between the electric power materials are determined according to the target frequent item set and a preset minimum confidence threshold.

[0015] In some embodiments, clustering the electric power materials according to the association rules includes:

[0016] Set an object point for each power material and select any object point from all the object points;

[0017] Determine whether the selected object point is a core point based on a preset radius and a threshold value of the number of object points;

[0018] If the selected object point is a core point, the distance between the selected object point and other object points is determined, and all object points that are density-reachable with the selected object point are determined based on the distance and form a cluster;

[0019] If the selected object point is an edge point, reselect another object point.

[0020] In some embodiments, establishing a storage optimization model based on the clustering results includes:

[0021] Determine the delivery time of each power material based on the delivery data and establish an objective function to minimize the delivery time of materials;

[0022] Based on the clustering results, the objective function of minimizing the distance between materials within the cluster is established;

[0023] A storage optimization model is established based on the objective function of minimizing the time of materials entering and leaving the warehouse and the objective function of minimizing the distance of materials within the class.

[0024] In some embodiments, optimizing the management of the adjusted storage space includes:

[0025] Determining duplicate materials in the storage location based on the adjusted storage location, and determining initial storage location coordinates corresponding to the duplicate materials;

[0026] Determine the importance of the frequency of duplicate materials being shipped out;

[0027] Sort the importance and delete the most important materials with duplicate corresponding storage locations from the duplicate set;

[0028] The remaining duplicate materials are sorted again according to their importance, and a number of target remaining storage locations corresponding to the remaining duplicate materials are determined according to the sorting results;

[0029] A storage location closest to each remaining duplicate material is selected from a number of target remaining storage locations, and the storage location coordinates of the storage location are used to replace the initial storage location coordinates corresponding to each remaining duplicate material.

[0030] In some embodiments, determining the importance of the frequency of duplicate materials leaving the warehouse includes:

[0031] Identify associated materials related to duplicate materials;

[0032] Obtaining a first confidence level and a second confidence level between the duplicate material and the associated material, and a delivery frequency of the associated material;

[0033] The importance of the delivery frequency of the duplicate materials is determined according to the first confidence level, the second confidence level, and the delivery frequency of the associated materials.

[0034] In some embodiments, determining a number of target remaining storage locations corresponding to the remaining duplicate materials according to the sorting results includes:

[0035] After completing the setting of the initial parameter values, randomly generate the particle population and initialize the position and velocity of each particle;

[0036] Calculate the fitness of each particle according to the defined fitness function;

[0037] Based on the importance of the material's delivery frequency, duplicate coordinates in the particles are removed using Euclidean distance.

[0038] Determine the inertia weight according to the fitness of each particle, and update the learning factor of the particle according to the number of iterations;

[0039] According to the fitness of each particle, update the individual extreme value and global extreme value of each particle, as well as the position and velocity of the particle;

[0040] When the maximum number of iterations is reached, the iteration is stopped and several target remaining storage locations corresponding to the remaining duplicate materials are output.

[0041] In addition, to achieve the above-mentioned purpose, the present invention further proposes an intelligent warehouse management device, which includes:

[0042] an acquisition module, configured to acquire outbound data of electric power materials, and determine association rules between electric power materials based on the outbound data, wherein the association rules represent associations between the electric power materials and are used to classify the electric power materials;

[0043] A classification module, configured to cluster power materials according to the association rules;

[0044] A creation module is used to establish a storage optimization model based on the clustering results, wherein the optimization model is used to adjust the storage location of power materials;

[0045] An optimization module, configured to adjust the storage location of electric power materials using the storage location optimization model;

[0046] The optimization module is used to optimize the management of the adjusted storage space.

[0047] In addition, to achieve the above-mentioned purpose, the present invention also proposes an intelligent warehouse management device, which includes: a memory, a processor, and an intelligent warehouse management program stored on the memory and runnable on the processor, and the intelligent warehouse management program is configured to implement the steps of the intelligent warehouse management method described above.

[0048] In addition, to achieve the above-mentioned purpose, the present invention also proposes a storage medium, on which an intelligent warehouse management program is stored. When the intelligent warehouse management program is executed by a processor, the steps of the intelligent warehouse management method described above are implemented.

[0049] The present invention obtains the outbound data of electric power materials, determines the association rules between electric power materials based on the outbound data, clusters the electric power materials according to the association rules, establishes a storage location optimization model based on the clustering results, adjusts the storage locations of electric power materials using the storage location optimization model, and optimizes and manages the adjusted storage locations. The above method uses the storage location optimization model established after clustering the association rules to adjust the storage locations of electric power, thereby reducing the picking distance. The association rules also mine the inherent associations of material outbound shipments, store related materials in similar storage locations, compress the picking path, and make warehouse management accurate and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flow chart of the first embodiment of the intelligent warehouse management method of the present invention;

[0051] Figure 2 This is a structural block diagram of the first embodiment of the intelligent warehouse management device of the present invention.

[0052] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0053] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0054] The embodiment of the present invention provides an intelligent warehouse management method, referring to Figure 1 , Figure 1 This is a flow chart of a first embodiment of an intelligent warehouse management method of the present invention.

[0055] In this embodiment, the intelligent warehouse management method includes the following steps:

[0056] Step S10: Obtaining the outbound data of electric power materials, and determining association rules between electric power materials based on the outbound data.

[0057] In this embodiment, the executing entity of this embodiment is an intelligent warehouse management device, wherein the intelligent warehouse management device has functions such as data processing, data communication and program running. The intelligent warehouse management device can be a computer terminal device or other network device, and of course it can also be other devices with similar functions. This embodiment does not limit this.

[0058] It should be noted that problems with the internal management model of power companies have led to irregularities in corporate material management, such as irrational storage layouts, weak intensive material management, and limited application of advanced logistics technologies in warehousing. This ultimately leads to problems such as low warehousing and logistics efficiency. Currently, the main method of material management relies on manual operations, but this method no longer meets the current demand for accurate, efficient, and convenient management of power materials, resulting in chaotic and inefficient warehousing management.

[0059] In order to solve the above technical problems, this embodiment obtains the outbound data of electric power materials, determines the association rules between electric power materials based on the outbound data, clusters the electric power materials according to the association rules, establishes a storage location optimization model based on the clustering results, uses the storage location optimization model to adjust the storage locations of electric power materials during storage, and optimizes the management of the adjusted storage locations. The above method can store materials with high outbound frequency in locations close to the entrance and exit by optimizing the storage locations, thereby reducing the picking distance. The association rules also mine the inherent associations of material outbound, store related materials in similar storage locations, compress the picking path, and make warehouse management accurate and efficient. Specifically, it can be achieved as follows.

[0060] In a specific implementation, in this embodiment, it is necessary to first obtain the outbound data of electric power materials, and the association rules between the electric power materials can be obtained through the outbound data. The process of determining the association rules in this embodiment specifically includes determining all initial frequent item sets based on the outbound data; filtering out target frequent item sets from the initial frequent item sets based on a preset minimum support threshold; and determining the association rules between the electric power materials based on the target frequent item sets and a preset minimum confidence threshold.

[0061] It should be noted that association rules are based on support and confidence. For outbound data, support is the ratio of the number of outbound materials that contain both power material A and power material B to the total number of outbound materials, and confidence is the percentage of materials that contain both power material A and power material B, that is, the ratio of items that contain both power material A and B to items that contain only power material A. If the support is greater than or equal to a support threshold, then the item set is the initial frequent item set. The support threshold can be set accordingly based on actual needs. The target frequent item set is then further filtered according to the set minimum support threshold. Finally, frequent item sets with less than the preset minimum confidence threshold are removed from the target frequent item set. The remaining frequent item sets that meet the preset minimum confidence threshold correspond to the association rules between power materials. The above associations can represent the association relationship between the various power materials.

[0062] Step S20: clustering the electric power materials according to the association rules.

[0063] In a specific implementation, an object point is set for each electric power material, and an object point is randomly selected from all the object points; whether the selected object point is a core point is determined based on a preset radius and a threshold value of the number of object points; if the selected object point is a core point, based on the distance between the selected object point and other object points, all object points that are densely reachable from the selected object point are determined according to the distance and form a cluster; if the selected object point is an edge point, other object points are reselected.

[0064] It should be noted that after selecting an object point, a range is determined based on the object point and the preset radius. If the number of object points in this range reaches the threshold of the number of object points, the object point is determined to be the core point. After determining the core point, the confidence calculated above is used as the distance between the selected object point and other object points. Assuming that the selected point is i and any other object point is j, the corresponding confidences are C ij and C ji , then the distance is (C ij +C ji ) / 2. The closer the distance, the more similar it is. Several similar points are grouped into a cluster. A reachable object point density means that the similar points are exactly within the preset radius. In other words, not only must they be similar, but they must also be within the range of the core point. In this embodiment, edge points are not considered core points, so other object points need to be reselected. The preset radius and object point number threshold can be set as needed and are not limited in this embodiment.

[0065] Step S30: establishing a storage optimization model based on the clustering results.

[0066] In a specific implementation, the outbound time of each electric power material is determined based on the outbound data to establish an objective function for minimizing the inbound and outbound time of materials; based on the clustering results, an objective function for minimizing the distance between materials within a class is established; and a storage optimization model is established based on the objective function for minimizing the inbound and outbound time of materials and the objective function for minimizing the distance between materials within a class.

[0067] It should be noted that the final storage optimization model is Among them, f1 and f2 are the objective functions of minimizing the time of materials entering and leaving the warehouse and minimizing the distance of materials within the class, respectively. f1' and f2' are the values after the function is dimensionlessly processed. Assume that the time of each material leaving the warehouse is t i (i=1,2,…,n). Our goal is to minimize the total delivery time of all materials. The objective function n represents the type of material. Assume that n types of materials are divided into k categories, and the objective function Among them, C j represents the jth category, and d(i,l) represents the distance between material i and material l.

[0068] Step S40: using the storage optimization model to adjust the storage location of electric power materials.

[0069] Step S50: Optimize and manage the adjusted storage locations.

[0070] In a specific implementation, after the storage location optimization model is constructed, the storage location of the current power materials can be optimized and adjusted through the storage location optimization model. In order to further improve efficiency, the adjusted storage locations can also be optimized and managed in this embodiment, specifically, duplicate materials in the storage locations are determined based on the adjusted storage locations, and the initial storage location coordinates corresponding to the duplicate materials are determined; the importance corresponding to the delivery frequency of duplicate materials is determined; the importance is sorted, and the materials with the greatest importance of corresponding storage location duplication are deleted from the duplicate set; the remaining duplicate materials are sorted again according to the importance, and several target remaining storage locations corresponding to the remaining duplicate materials are determined according to the sorting results; the storage location closest to each remaining duplicate material is selected from the several target remaining storage locations, and the storage location coordinates of the storage location replace the initial storage location coordinates corresponding to each remaining duplicate material.

[0071] Among them, the target remaining storage locations corresponding to the remaining duplicate materials are determined according to the sorting results. Specifically, after completing the setting of the initial parameter values, a particle population is randomly generated, and the position and velocity of each particle are initialized; the fitness of each particle is calculated according to the defined fitness function; the duplicate coordinates in the particles are deduplicated using the Euclidean distance according to the importance of the frequency of material delivery; the inertia weight is determined according to the fitness of each particle, and the learning factor of the particle is updated according to the number of iterations; the individual extreme value and global extreme value of each particle, as well as the position and velocity of the particle are updated according to the fitness of each particle; when the maximum number of iterations is reached, the iteration is stopped, and the target remaining storage locations corresponding to the remaining duplicate materials are output.

[0072] It should be noted that determining the importance corresponding to the delivery frequency of duplicate materials specifically includes: determining the associated materials related to the duplicate materials; obtaining the first confidence and the second confidence between the duplicate materials and the associated materials, as well as the delivery frequency of the associated materials; determining the importance corresponding to the delivery frequency of the duplicate materials based on the first confidence, the second confidence and the delivery frequency of the associated materials, for example, I=P(C1+C2), where P is the delivery frequency of the associated materials, and C1 and C2 are the first confidence and the second confidence, respectively.

[0073] In this embodiment, the outbound data of electric power materials is obtained, and the association rules between the electric power materials are determined based on the outbound data; the electric power materials are clustered according to the association rules; a storage location optimization model is established based on the clustering results; the storage locations of the electric power materials are adjusted using the storage location optimization model; and the adjusted storage locations are optimized and managed. The above method uses the storage location optimization model established after clustering the association rules to adjust the storage locations of electric power, thereby reducing the picking distance. The association rules also mine the inherent associations of the outbound materials, storing the related materials in similar storage locations, compressing the picking path, and making the warehouse management accurate and efficient.

[0074] In addition, an embodiment of the present invention further proposes a storage medium on which an intelligent warehouse management program is stored. When the intelligent warehouse management program is executed by a processor, the steps of the intelligent warehouse management method described above are implemented.

[0075] Reference Figure 2 , Figure 2 This is a structural block diagram of the first embodiment of the intelligent warehouse management device of the present invention.

[0076] like Figure 2 As shown, the intelligent warehouse management device proposed in the embodiment of the present invention includes:

[0077] The acquisition module 10 is configured to acquire the outbound data of the electric power materials and determine the association rules between the electric power materials based on the outbound data.

[0078] The classification module 20 is configured to cluster the electric power materials according to the association rules.

[0079] A creation module 30 is used to establish a storage optimization model based on the clustering results.

[0080] The optimization module 40 is used to adjust the storage location of electric power materials using the storage location optimization model.

[0081] The optimization module 40 is used to optimize the management of the adjusted storage location.

[0082] In this embodiment, data on the dispatch of power materials is obtained, and association rules between power materials are determined based on the dispatch data; power materials are clustered according to the association rules; a storage location optimization model is established based on the clustering results; the storage locations of power materials are adjusted using the storage location optimization model; and the adjusted storage locations are optimized and managed. The above method can optimize storage locations to store materials with high dispatch frequency near entrances and exits, thereby reducing the picking distance. Furthermore, the association rules mine the inherent associations between material dispatches, storing related materials in similar storage locations, shortening the picking path and making warehouse management accurate and efficient.

[0083] In some embodiments, the acquisition module 10 is configured to determine all initial frequent itemsets based on the outbound data;

[0084] Filtering a target frequent item set from the initial frequent item set according to a preset minimum support threshold;

[0085] The association rules between the electric power materials are determined according to the target frequent item set and a preset minimum confidence threshold.

[0086] In some embodiments, the classification module 20 is used to set an object point for each power material and select an object point from all the object points;

[0087] Determine whether the selected object point is a core point based on a preset radius and a threshold value of the number of object points;

[0088] If the selected object point is a core point, the distance between the selected object point and other object points is determined, and all object points that are density-reachable with the selected object point are determined based on the distance and form a cluster;

[0089] If the selected object point is an edge point, reselect another object point.

[0090] In some embodiments, the creation module 30 is configured to determine the outbound time of each power material based on the outbound data and establish an objective function for minimizing the inbound and outbound time of the materials;

[0091] Based on the clustering results, the objective function of minimizing the distance between materials within the cluster is established;

[0092] A storage optimization model is established based on the objective function of minimizing the time of materials entering and leaving the warehouse and the objective function of minimizing the distance of materials within the class.

[0093] In some embodiments, the optimization module 40 is configured to determine duplicate materials in the storage location based on the adjusted storage location, and determine initial storage location coordinates corresponding to the duplicate materials;

[0094] Determine the importance of the frequency of duplicate materials being shipped out;

[0095] Sort the importance and delete the most important materials with duplicate corresponding storage locations from the duplicate set;

[0096] The remaining duplicate materials are sorted again according to their importance, and a number of target remaining storage locations corresponding to the remaining duplicate materials are determined according to the sorting results;

[0097] A storage location closest to each remaining duplicate material is selected from a number of target remaining storage locations, and the storage location coordinates of the storage location are used to replace the initial storage location coordinates corresponding to each remaining duplicate material.

[0098] In some embodiments, the optimization module 40 is used to determine associated materials related to duplicate materials;

[0099] Obtaining a first confidence level and a second confidence level between the duplicate material and the associated material, and a delivery frequency of the associated material;

[0100] The importance of the delivery frequency of the duplicate materials is determined according to the first confidence level, the second confidence level, and the delivery frequency of the associated materials.

[0101] In some embodiments, the optimization module 40 is used to randomly generate a particle population and initialize the position and velocity of each particle after completing the setting of the initial parameter values;

[0102] Calculate the fitness of each particle according to the defined fitness function;

[0103] Based on the importance of the material's delivery frequency, duplicate coordinates in the particles are removed using Euclidean distance.

[0104] Determine the inertia weight according to the fitness of each particle, and update the learning factor of the particle according to the number of iterations;

[0105] According to the fitness of each particle, update the individual extreme value and global extreme value of each particle, as well as the position and velocity of the particle;

[0106] When the maximum number of iterations is reached, the iteration is stopped and several target remaining storage locations corresponding to the remaining duplicate materials are output.

[0107] An embodiment of the present application also provides an intelligent warehouse management 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 through the communication bus, the memory is used to store the intelligent warehouse management program; the processor is used to implement the above-mentioned intelligent warehouse management method when executing the program stored in the memory.

[0108] The communication bus mentioned in the above-mentioned intelligent warehouse management device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. This communication bus can be divided into an address bus, a data bus, a control bus, etc.

[0109] The communication interface is used for communication between the above-mentioned intelligent warehouse management equipment and other equipment.

[0110] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage. Alternatively, the memory may be at least one storage device located remote from the processor.

[0111] The above-mentioned processor 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.

[0112] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When software is used for implementation, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrations. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0113] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0114] Each embodiment in this specification is described in a related manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiment is generally similar to the method embodiment, so the description is relatively simple. For related parts, refer to the description of the method embodiment.

[0115] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

[0116] It should be understood that the above is only an example and does not constitute any limitation to the technical solution of the present invention. In specific applications, those skilled in the art can make settings as needed, and the present invention does not impose any limitation on this.

[0117] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present invention. In practical applications, technicians in this field can select part or all of it according to actual needs to achieve the purpose of the embodiment scheme, and no limitation is made here.

[0118] In addition, for technical details not fully described in this embodiment, please refer to the intelligent warehouse management method provided in any embodiment of the present invention, and will not be repeated here.

[0119] In addition, it should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0120] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0121] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, or of course by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as a read-only memory (ROM) / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0122] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

[0123] It is understandable that the system provided by the embodiment of the present invention corresponds to the method provided by the embodiment of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts of the above method.

Claims

1. An intelligent warehouse management method, characterized in that: The intelligent warehouse management method includes: Obtaining outbound data of electric power materials, and determining association rules between electric power materials based on the outbound data, wherein the association rules represent associations between the electric power materials and are used to classify the electric power materials; Clustering the electric power materials according to the association rules; Establishing a storage optimization model based on the clustering results, wherein the optimization model is used to adjust the storage location of electric power materials; Using the storage optimization model to adjust the storage location of power materials; Optimize the management of the adjusted storage locations.

2. The intelligent warehouse management method according to claim 1, characterized in that: The determining of association rules between electric power materials based on the outbound data includes: Determine all initial frequent item sets based on the outbound data; Filtering a target frequent item set from the initial frequent item set according to a preset minimum support threshold; The association rules between the electric power materials are determined according to the target frequent item set and a preset minimum confidence threshold.

3. The intelligent warehouse management method according to claim 1, characterized in that: Clustering the electric power materials according to the association rules includes: Set an object point for each power material and select any object point from all the object points; Determine whether the selected object point is a core point based on a preset radius and a threshold value of the number of object points; If the selected object point is a core point, the distance between the selected object point and other object points is determined, and all object points that are density-reachable with the selected object point are determined based on the distance and form a cluster; If the selected object point is an edge point, reselect another object point.

4. The intelligent warehouse management method according to claim 1, characterized in that: The method of establishing a storage optimization model based on clustering results includes: Determine the delivery time of each power material based on the delivery data and establish an objective function to minimize the delivery time of materials; Based on the clustering results, the objective function of minimizing the distance between materials within the cluster is established; A storage optimization model is established based on the objective function of minimizing the time of materials entering and leaving the warehouse and the objective function of minimizing the distance of materials within the class.

5. The intelligent warehouse management method according to claim 1, characterized in that: The optimized management of the adjusted storage space includes: Determining duplicate materials in the storage location based on the adjusted storage location, and determining initial storage location coordinates corresponding to the duplicate materials; Determine the importance of the frequency of duplicate materials being shipped out; Sort the importance and delete the most important materials with duplicate corresponding storage locations from the duplicate set; The remaining duplicate materials are sorted again according to their importance, and a number of target remaining storage locations corresponding to the remaining duplicate materials are determined according to the sorting results; A storage location closest to each remaining duplicate material is selected from a number of target remaining storage locations, and the storage location coordinates of the storage location are used to replace the initial storage location coordinates corresponding to each remaining duplicate material.

6. The intelligent warehouse management method according to claim 5, characterized in that: The importance of determining the frequency of duplicate material shipments includes: Identify associated materials related to duplicate materials; Obtaining a first confidence level and a second confidence level between the duplicate material and the associated material, and a delivery frequency of the associated material; The importance of the delivery frequency of the duplicate materials is determined according to the first confidence level, the second confidence level, and the delivery frequency of the associated materials.

7. The intelligent warehouse management method according to claim 5, characterized in that: Determining a number of target remaining storage locations corresponding to the remaining duplicate materials according to the sorting results includes: After completing the setting of the initial parameter values, randomly generate the particle population and initialize the position and velocity of each particle; Calculate the fitness of each particle according to the defined fitness function; Based on the importance of the material's delivery frequency, duplicate coordinates in the particles are removed using Euclidean distance. Determine the inertia weight according to the fitness of each particle, and update the learning factor of the particle according to the number of iterations; According to the fitness of each particle, update the individual extreme value and global extreme value of each particle, as well as the position and velocity of the particle; When the maximum number of iterations is reached, the iteration is stopped and several target remaining storage locations corresponding to the remaining duplicate materials are output.

8. An intelligent warehouse management device, characterized in that: The intelligent warehouse management device includes: an acquisition module, configured to acquire outbound data of electric power materials, and determine association rules between electric power materials based on the outbound data, wherein the association rules represent associations between the electric power materials and are used to classify the electric power materials; A classification module, configured to cluster power materials according to the association rules; A creation module is used to establish a storage optimization model based on the clustering results, wherein the optimization model is used to adjust the storage location of power materials; An optimization module, configured to adjust the storage location of electric power materials using the storage location optimization model; The optimization module is used to optimize the management of the adjusted storage space.

9. An intelligent warehouse management device, characterized in that: The intelligent warehouse management device includes: a memory, a processor, and an intelligent warehouse management program stored in the memory and executable on the processor, wherein the intelligent warehouse management program is configured to implement the steps of the intelligent warehouse management method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores an intelligent warehouse management program, and when the intelligent warehouse management program is executed by the processor, the steps of the intelligent warehouse management method according to any one of claims 1 to 7 are implemented.

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