Warehousing resource quantity scale optimization method and device

Abstract

The invention discloses a storage resource quantity scale optimization method and apparatus. The method comprises the steps of obtaining a storage resource information set; constructing a storage resource quantity scale optimization model; and processing the storage resource information set by using the storage resource quantity scale optimization model to obtain storage resource quantity scale information. According to the method, on the basis of target task information, a storage resource quantity scale optimization model is used for processing a storage resource information set, storage resource quantity scale information is obtained, the quantity and scale of storage resources are optimized, and the relation between military benefits and economic benefits is balanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of warehousing, in particular to a warehousing resource quantity scale optimization method and device. BACKGROUND

[0002] The warehousing resource quantity scale optimization of the present application is a "critical scale" problem, which is affected by multiple factors, that is, the quantity scale cannot be expanded indefinitely, nor can it be excessively contracted. How to realize the optimization processing of the warehouse quantity and scale in the warehousing resource based on the target task information, and balance the relationship between military benefit and economic benefit, is a technical problem to be solved by the present application. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a warehousing resource quantity scale optimization method and device, which realizes the optimization processing of the quantity and scale of the warehousing resource based on the target task information, and balances the relationship between military benefit and economic benefit.

[0004] In order to solve the above technical problem, the first aspect of the embodiment of the present application discloses a warehousing resource quantity scale optimization method, which comprises:

[0005] S1, obtaining a warehousing resource information set;

[0006] The warehousing resource information set comprises a demand point position information set, transportation cost information, inventory cost information, material throughput information and transportation and distribution capacity information;

[0007] S2, constructing a warehousing resource quantity scale optimization model;

[0008] The warehousing resource quantity scale optimization model comprises a first optimization model and a second optimization model;

[0009] S3, processing the warehousing resource information set by using the warehousing resource quantity scale optimization model to obtain warehousing resource quantity information and warehousing resource scale information.

[0010] As an optional implementation manner, in the first aspect of the embodiment of the present application, the constructing of the warehousing resource quantity scale optimization model comprises:

[0011] S21, determining first resource optimization factor information and second resource optimization factor information;

[0012] S22, constructing the first optimization model and the second optimization model based on the first resource optimization factor information and the second resource optimization factor information.

[0013] As an optional implementation, in the first aspect of the embodiment of the present application, the first optimization model and the second optimization model are constructed based on the first resource optimization factor information and the second resource optimization factor information, including:

[0014] S221, determining first optimization target information;

[0015] S222, constructing a first optimization model based on the first optimization target information and the first resource optimization factor information;

[0016] S223, determining second optimization target information;

[0017] S224, constructing a second optimization model based on the second optimization target information and the first resource optimization factor information.

[0018] As an optional implementation, in the first aspect of the embodiment of the present application, the first optimization model expression is:

[0019]

[0020]

[0021] Wherein, n represents the first warehouse optimization quantity; m represents the index of the first warehouse; n1 represents the down-round value; n2 represents the up-round value; C m (n) represents the continuous function of n; θ represents the area characteristic parameter; β represents the road detour coefficient; a represents the effective area of material support; k represents the transportation rate; μ represents the material purchase mode coefficient; A m represents the total area of the material support area; G m represents the total annual demand of the mth warehouse material support.

[0022] As an optional implementation, in the first aspect of the embodiment of the present application, the warehouse resource information set is processed by using the warehouse resource quantity scale optimization model to obtain warehouse resource quantity scale information, including:

[0023] S31, the first warehouse resource quantity scale information is obtained by processing the warehouse resource information set by using the first optimization model;

[0024] S32, the second warehouse resource quantity scale information is obtained by processing the warehouse resource information set by using the second optimization model;

[0025] S33, the first warehouse resource quantity scale information and the second warehouse resource quantity scale information are fused to obtain the warehouse resource quantity scale information.

[0026] As an optional implementation, in the first aspect of the embodiment of the present application, the first optimization model is used to process the warehouse resource information set to obtain first warehouse resource quantity scale information, including:

[0027] S311, the warehouse resource information set is parsed and processed to obtain a demand point position information set, transportation cost information, inventory cost information, material throughput information and transportation and distribution capacity information;

[0028] S312, the demand point position information set is processed to obtain guarantee area information;

[0029] S313, the transportation cost information, the inventory cost information, the material throughput information and the transportation and distribution capacity information are processed to obtain transportation total cost information and inventory total cost information;

[0030] S314, the first optimization model is used to process the guarantee area information, the transportation total cost information and the inventory total cost information to obtain the first warehouse resource quantity scale information.

[0031] As an optional implementation, in the first aspect of the embodiment of the present application, the demand point position information set is processed to obtain guarantee area information, including;

[0032] S3121, the demand point position information set is clustered and processed to obtain a grid area information set;

[0033] S3122, the grid area information set is processed to obtain a grid area area information set;

[0034] S3123, the sum of all grid area areas of the grid area area information set is obtained to obtain guarantee area information.

[0035] The second aspect of the embodiment of the present application discloses a warehouse resource quantity scale optimization device, and the device comprises:

[0036] An acquisition module is configured to acquire a warehouse resource information set;

[0037] The warehouse resource information set comprises a demand point position information set, transportation cost information, inventory cost information, material throughput information and transportation and distribution capacity information;

[0038] A model construction module is configured to construct a warehouse resource quantity scale optimization model;

[0039] The warehouse resource quantity scale optimization model comprises a first optimization model and a second optimization model;

[0040] A data processing module is configured to process the warehouse resource information set by using the warehouse resource quantity and scale optimization model to obtain warehouse resource quantity information and warehouse resource scale information.

[0041] The third aspect of the present application discloses another warehouse resource quantity and scale optimization device, which comprises:

[0042] a memory storing executable program codes;

[0043] a processor coupled to the memory;

[0044] The processor invokes the executable program codes stored in the memory to execute part or all of the steps of the warehouse resource quantity and scale optimization method disclosed in the first aspect of the present application.

[0045] The fourth aspect of the present application discloses a computer readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of the warehouse resource quantity and scale optimization method disclosed in the first aspect of the present application.

[0046] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0047] In the embodiments of the present application, the warehouse resource information set is processed by using the warehouse resource quantity and scale optimization model based on the target task information to obtain the warehouse resource quantity and scale optimization result information, so that the rapid optimization processing of the warehouse resource quantity and scale is realized, and the processing efficiency of the warehouse resource quantity and scale optimization is improved. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in 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 only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort.

[0049] Figure 1 is a scene schematic diagram of the warehouse resource management system provided by the embodiments of the present application;

[0050] Figure 2 is a flow schematic diagram of the warehouse resource quantity and scale optimization method disclosed by the embodiments of the present application;

[0051] Figure 3 is a structure schematic diagram of the warehouse resource quantity and scale optimization device disclosed by the embodiments of the present application;

[0052] Figure 4is a structural schematic view of another warehouse resource quantity scale optimization device disclosed by the embodiment of the present application. DETAILED DESCRIPTION

[0053] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0054] The terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish different objects, rather than to describe a particular order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product, or equipment including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product, or equipment.

[0055] In this document, reference to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. It is explicitly contemplated that embodiments described herein can be combined with each other.

[0056] In this application, the word "exemplary" is used to mean "serving as an example, instance, or illustration." Any implementation described as "exemplary" is not necessarily to be construed as preferred or advantageous over other implementations. The following description is presented to enable any person skilled in the art to make and use the application. In the following description, for purposes of explanation, specific details are set forth. It is apparent to those skilled in the art that the present application can be practiced without using these specific details. In other instances, well-known structures and processes are not elaborated in order not to obscure the description of the application with unnecessary details. Thus, the present application is not intended to be limited by the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

[0057] It should be noted that the method of the embodiments of the present application is executed in the computer device, and the processing object of each computer device exists in the form of data or information, for example, time, which is actually time information. It can be understood that if the size, quantity, position, etc. are mentioned in the subsequent embodiments, they are corresponding data, so that the computer device can process, and the specific details are not described here.

[0058] It should be noted that the artificial intelligence related technology involved in the present application is briefly described. Artificial intelligence (AI) is to use digital computers or digital computer controlled machines to simulate, extend and expand human intelligence, perceive environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology of computer science, which tries to understand the essence of intelligence and produce a new intelligent machine that can react in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines, so that the machine has the functions of perception, reasoning and decision making.

[0059] Artificial intelligence technology is a comprehensive discipline, involving a wide range of fields, both hardware and software technologies. Artificial intelligence basic technologies generally include technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction system, mechatronics, etc. Artificial intelligence software technology mainly includes computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning, etc. several major directions.

[0060] Computer vision technology (CV) Computer vision is a science that studies how to make machines "see". Further, it refers to using cameras and computers to replace human eyes to identify and measure targets, and further to do image processing, so that the computer processing becomes more suitable for human eye observation or image transmission to instrument detection. As a scientific discipline, computer vision researches related theories and technologies, trying to establish artificial intelligence systems that can obtain information from images or multidimensional data. Computer vision technology usually includes image processing, image recognition, image semantic understanding, image retrieval, OCR, video processing, video semantic understanding, video content / behavior recognition, three-dimensional object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous localization and mapping, etc. It also includes common face recognition, fingerprint recognition and other biometric identification technologies.

[0061] Single-modal information is only one type of data, such as text, image, audio, video, electromagnetic signal, etc. Multi-modal information is data information including at least two single-modal information. Further, multi-modal information is suitable for complex tasks that require the integration of multiple information sources, such as sentiment analysis, robot interaction, autonomous driving, etc. By integrating information from multiple modalities, higher performance and accuracy on tasks can usually be achieved.

[0062] A large model refers to an artificial neural network model with a very large number of parameters. In the field of artificial intelligence, a large model usually refers to a model with hundreds of millions to trillions of parameters. The model usually needs to be trained on a large-scale dataset and requires a large amount of computing resources for optimization and adjustment. Large models are usually used to solve complex natural language processing, computer vision, and speech recognition tasks. Generative AI is an AI that can create new content and ideas, including conversations, stories, images, videos, and music. In the embodiments of the present application, the large model can be a large language model such as ChatGPT, BERT, XLNet, Zhibu model, Claude, Moonshot AI model, ChatGLM model, Qianyitong question model, MiniMax model, Xinghuo model, Llama model, 360GPT model, Qwen model, Baichuan model, Yunding model, vivoLM model, DeepSeek model, and Wenxin Yanyan, etc. The embodiments of the present application are not limited.

[0063] The embodiments of the present application provide a warehouse resource quantity scale optimization method and device, computer equipment and computer readable storage medium, which are described in detail below.

[0064] Please refer to Figure 1 , Figure 1 The scene diagram of the warehouse resource management system provided by the embodiments of the present application can include a computer equipment 100, and the computer equipment 100 is integrated with a warehouse resource quantity scale optimization device, such as Figure 1 The computer equipment in the above.

[0065] In the embodiments of the present application, the computer equipment 100 can be a standalone server, or a server network or server cluster composed of servers. For example, the computer equipment 100 described in the embodiments of the present application includes but is not limited to a computer, a network host, a single network server, a plurality of network server sets, or a cloud server composed of a plurality of servers. The cloud server is composed of a large number of computers or network servers based on cloud computing.

[0066] It is understood that the computer device 100 used in the embodiments of the present application can be a device that includes both receiving and transmitting hardware, that is, a device that has receiving and transmitting hardware capable of performing two-way communication over a two-way communication link. Such a device may include: a cellular or other communication device that has a single-line display, a multi-line display, or a cellular or other communication device without a multi-line display. The specific computer device 100 can be a desktop terminal or a mobile terminal. The computer device 100 can also be a mobile phone, a tablet computer, a laptop computer, etc.

[0067] Those skilled in the art will understand that Figure 1 The application environment shown in the figure is only one application scenario of the present application solution and does not constitute a limitation on the application scenario of the present application solution. Other application environments may also include Figure 1 More or fewer computer devices as shown in Figure 1 Only one computer device is shown in the figure. It can be understood that the data processing system can also include one or more other services, which are not limited here.

[0068] In addition, if Figure 1 As shown, the warehouse resource management system may further include a memory 200 for storing warehouse resource data, such as Google Maps data, security point location information, warehouse location information, obstacle location information, and stored material information.

[0069] It should be noted that Figure 1 The scenario diagram of the warehouse resource management system shown is only an example. The warehouse resource management system and scenario described in the embodiment of this application are intended to more clearly illustrate the technical solution of the embodiment of this application, and do not constitute a limitation on the technical solution provided by the embodiment of this application. Ordinary technicians in this field can know that with the evolution of the warehouse resource management system and the emergence of new business scenarios, the technical solution provided by the embodiment of this application is also applicable to similar technical problems.

[0070] This invention discloses a method and device for optimizing the quantity and scale of storage resources. Based on target mission information, this method utilizes a storage resource quantity and scale optimization model to optimize the quantity and scale of storage resources, balancing military and economic benefits. These are described in detail below.

[0071] Example 1

[0072] See also Figure 2 , Figure 2 This is a flow chart of a method for optimizing the quantity and scale of storage resources disclosed in an embodiment of the present invention. Figure 2The described warehouse resource quantity scale optimization method is applied to a warehouse resource management system, such as a local server or a cloud server for a warehouse resource management system, and embodiments of the present application are not limited. As shown in Figure 1 The warehouse resource quantity scale optimization method can include the following operations:

[0073] S1, obtaining a warehouse resource information set;

[0074] It should be noted that the warehouse resource information set includes a demand point location information set, transportation cost information, inventory cost information, material throughput information, and transportation and distribution capacity information.

[0075] S2, constructing a warehouse resource quantity scale optimization model;

[0076] It should be noted that the warehouse resource quantity scale optimization model includes a first optimization model and a second optimization model.

[0077] S3, processing the warehouse resource information set using the warehouse resource quantity scale optimization model to obtain warehouse resource quantity information and warehouse resource scale information.

[0078] As can be seen, by implementing the warehouse resource quantity scale optimization method described in the embodiments of the present application, the warehouse resource quantity scale optimization model is used to process the warehouse resource information set to obtain warehouse resource quantity information and warehouse resource scale information, which realizes rapid optimization processing of the warehouse resource quantity and scale, and improves the processing efficiency of the warehouse resource quantity scale optimization.

[0079] In an optional embodiment, in the step S2 described above, the construction of the warehouse resource quantity scale optimization model includes:

[0080] S21, determining first resource optimization factor information and second resource optimization factor information;

[0081] S22, based on the first resource optimization factor information and the second resource optimization factor information, constructing the first optimization model and the second optimization model.

[0082] As can be seen, by implementing the warehouse resource quantity scale optimization method described in the embodiments of the present application, the warehouse resource quantity scale optimization model is constructed to provide support for subsequent data processing, which improves the overall operation efficiency of the warehouse resource and balances the relationship between the military benefit and the economic benefit of the warehouse resource.

[0083] In another optional embodiment, in the step S22 described above, the construction of the first optimization model and the second optimization model based on the first resource optimization factor information and the second resource optimization factor information includes:

[0084] S221, determine first optimization target information;

[0085] It should be noted that the first optimization target information represents the optimization target of the first warehouse.

[0086] It should be noted that the first warehouse represents a storage and supply base.

[0087] S222, based on the first optimization target information and the first resource optimization factor information, a first optimization model is constructed;

[0088] S223, determine second optimization target information;

[0089] It should be noted that the second optimization target information represents the optimization target of the second warehouse.

[0090] It should be noted that the second warehouse represents a regional warehouse.

[0091] S224, based on the second optimization target information and the first resource optimization factor information, a second optimization model is constructed.

[0092] It can be seen that the warehouse resource quantity scale optimization method described in the embodiment of the application is constructed, and the warehouse resource quantity scale optimization model is constructed to provide support for subsequent data processing, improve the overall operation efficiency of the warehouse resource, and balance the relationship between the military benefit and the economic benefit of the warehouse resource.

[0093] In another optional embodiment, in the step S222, the first optimization model expression is:

[0094]

[0095] Wherein, n represents the first warehouse optimization quantity; m represents the index of the first warehouse; n1 represents the down rounding value; n2 represents the up rounding value; C m (n) represents the continuous function of n; θ represents the area characteristic parameter; β represents the road detour coefficient; a represents the effective area of material support; k represents the transportation rate; μ represents the material purchase mode coefficient; A m represents the total area of the material support area; G m represents the total annual demand of the mth warehouse material support;

[0096] It should be noted that in the embodiment, the transportation rate k is set to 0.45 yuan / ton kilometer.

[0097] The material purchase mode coefficient μ is between 200 and 400, small batch continuous purchase should take a smaller value, and large batch intermittent purchase should take a larger value; the area characteristic parameter θ is set to 0.376 when the material support area is close to a circle, 0.383 when the material support area is close to a square, and 0.420 when the material support area is close to a rectangle; and the road detour coefficient β is set to 1.2.

[0098] It can be seen that the warehouse resource quantity and scale optimization method described in the embodiment of the application is implemented, the first optimization model is constructed, the subsequent rapid optimization processing of the warehouse resource quantity and scale is supported, and the processing efficiency of the warehouse resource quantity and scale optimization is improved.

[0099] In another optional embodiment, the first optimization model in the step S224 includes a task analysis module, a data acquisition module, a first data processing module, a second data processing module, a third data processing module and a data output module.

[0100] The task analysis module is configured to analyze the task analysis to obtain material support channel information, material support target region information and target material demand quantity information.

[0101] The data acquisition module is configured to acquire current warehouse resource information and target condition information.

[0102] It should be noted that the target condition information includes warehouse resource guarantee timeliness information, warehouse resource radiation radius information, warehouse resource throughput capacity information and target region material demand quantity information.

[0103] The first data processing module is configured to analyze and process the acquired warehouse resource information to obtain first candidate point information.

[0104] It should be noted that the analysis and processing means that according to the data type, the warehouse resource candidate point position information in the existing warehouse resource is acquired to obtain the first candidate point information.

[0105] The second data processing module is configured to process the material support channel information, the material support target region information, the target material demand quantity information and the first candidate point information to obtain second candidate point information.

[0106] It should be noted that the second candidate point information means that the planning warehouse resource candidate point information, i.e., the second candidate point information, is obtained according to the material support channel information, the material support target region information, the target material demand quantity information and in combination with the first candidate point information.

[0107] The third data processing module is configured to process the second candidate point information by using the target condition information to obtain the warehouse resource scale information.

[0108] It should be noted that the processing means processing the target condition information and the second candidate point information by using the improved set covering model to obtain the warehouse resource scale information.

[0109] It should be noted that the improved set covering model expression is as follows:

[0110]

[0111] 0≤yi j ≤1, x j ∈{0, 1}.

[0112] Wherein, H represents a position set of the warehouse resource candidate point; A(j) represents a position set of a target guarantee region that can be covered by the jth warehouse resource candidate point; B(i) represents a position set of the warehouse resource candidate point that can cover the ith target guarantee region; d i . represents the material demand of the ith target region; C j represents the material throughput capacity of the jth warehouse resource candidate point; x j represents the selected variable of the jth warehouse resource candidate point; yi j represents the proportion of the material quantity of the ith target guarantee region guaranteed by the jth warehouse resource candidate point to the total material demand of the ith target guarantee region.

[0113] It should be noted that xj=1 represents that the jth warehouse resource candidate point is selected; xj=0 represents that the jth warehouse resource candidate point is not selected,

[0114] It should be noted that the coverage radius of A(j) is a time radiation radius, which comprehensively considers the time effectiveness and the radiation radius.

[0115] The data output module is configured to output the warehouse resource scale information.

[0116] The output end of the task analysis module is data-connected with the input end of the data acquisition module, the input end of the second data processing module respectively; the output end of the data acquisition module is data-connected with the input end of the first data processing module; the output end of the first data processing module is data-connected with the input end of the second data processing module; the output end of the second data processing module is data-connected with the input end of the third data processing module; the output end of the third data processing module is data-connected with the input end of the data output module.

[0117] It can be seen that the warehouse resource quantity and scale optimization method described in the embodiment of the application is implemented, the second optimization model is constructed, support is provided for subsequent rapid optimization processing of the warehouse resource quantity and scale, and the processing efficiency of the warehouse resource quantity and scale optimization is improved.

[0118] In another optional embodiment, in the step S3, the warehouse resource quantity and scale optimization model is used to process the warehouse resource information set to obtain warehouse resource quantity and scale information.

[0119] S31, the first optimization model is used to process the warehouse resource information set to obtain first warehouse resource quantity and scale information.

[0120] S32, the second optimization model is used to process the warehouse resource information set to obtain second warehouse resource quantity and scale information.

[0121] S33, the first warehouse resource quantity and scale information and the second warehouse resource quantity and scale information are fused to obtain warehouse resource quantity and scale information.

[0122] It should be noted that the fusion processing means that the first warehouse resource quantity and scale information and the second warehouse resource quantity and scale information are processed to remove duplicates and then combined in order from small to large according to the number.

[0123] It can be seen that the warehouse resource quantity and scale optimization method described in the embodiment of the application is implemented, the warehouse resource quantity and scale optimization model is used to process the warehouse resource information set to obtain warehouse resource quantity and scale information, rapid optimization processing of the warehouse resource quantity and scale is achieved, and the processing efficiency of the warehouse resource quantity and scale optimization is improved.

[0124] In another optional embodiment, in the step S31, the first optimization model is used to process the warehouse resource information set to obtain first warehouse resource quantity and scale information.

[0125] S311, the warehouse resource information set is parsed to obtain a demand point location information set, transportation cost information, inventory cost information, material throughput information, and transportation and distribution capability information.

[0126] S312, the demand point location information set is processed to obtain guarantee area area information.

[0127] S313, the transportation cost information, the inventory cost information, the material throughput information, and the transportation and distribution capability information are processed to obtain transportation total cost information and inventory total cost information.

[0128] S314, processing the guarantee area information, the total transportation cost information and the total inventory cost information by using the first optimization model to obtain first warehouse resource quantity and scale information.

[0129] It can be seen that the warehouse resource quantity and scale optimization method described in the embodiment of the application is used to process the warehouse resource guarantee information set and the warehouse resource information set by using the first optimization model to obtain first warehouse resource quantity and scale information, so that the warehouse resource quantity and scale are quickly optimized and processed, and the processing efficiency of the warehouse resource quantity and scale optimization is improved.

[0130] In another optional embodiment, the step S312 of processing the demand point location information set to obtain guarantee area information includes:

[0131] S3121, performing clustering processing on the demand point location information set to obtain a grid area information set;

[0132] It should be noted that in the embodiment, the clustering processing means that the BIRCH algorithm capable of realizing fast clustering is used to perform spatial clustering on the demand point location information set to obtain a grid area information set;

[0133] S3122, processing the grid area information set to obtain a grid area area information set;

[0134] It should be noted that the processing of the grid area information set means obtaining the peripheral demand point coordinate information of the grid area, using the peripheral demand point coordinate information to obtain the effective area of the corresponding target guarantee area, and then obtaining the grid area area information set;

[0135] S3123, summing all grid area areas of the grid area area information set to obtain guarantee area information.

[0136] It can be seen that the warehouse resource quantity and scale optimization method described in the embodiment of the application is used to process the demand point location information set to obtain guarantee area information, which provides support for subsequent construction of a warehouse resource optimization model, improves the overall operation efficiency of the warehouse resource, and balances the relationship between the military benefit and the economic benefit of the warehouse resource.

[0137] In another optional embodiment, the step S32 of processing the warehouse resource information set by using the second optimization model to obtain second warehouse resource quantity and scale information includes:

[0138] S321, using a task analysis module of the second optimization model, the warehouse resource guarantee information set is parsed and processed to obtain material guarantee channel information, material guarantee target region information and target material demand quantity information;

[0139] It should be noted that the warehouse resource guarantee information set includes a plurality of warehouse resource guarantee information;

[0140] It should be noted that the parsing process means that according to the data type, the material guarantee channel information, the material guarantee target region information and the target material demand quantity information are obtained;

[0141]

[0142] The target condition information includes warehouse resource guarantee time limit information, warehouse resource radiation radius information, warehouse resource throughput capacity information and target area material demand quantity information;

[0143] S323, using the first data processing module of the second optimization model, the warehouse resource information is parsed and processed to obtain first candidate point information;

[0144] It should be noted that the warehouse resource information represents the existing warehouse resource information;

[0145] The parsing process means that according to the data type, the warehouse resource candidate point position information of the warehouse resource information is obtained to obtain the first candidate point information;

[0146] S324, using the second data processing module of the second optimization model, the material guarantee channel information, the material guarantee target region information, the target material demand quantity information and the first candidate point information are processed to obtain the second candidate point information;

[0147] It should be noted that the processing means that according to the material guarantee channel information, the material guarantee target region information, the target material demand quantity information, and in combination with the first candidate point information, the planning warehouse resource candidate point information, that is, the second candidate point information, is obtained;

[0148] S325, using the third data processing module of the second optimization model, for using target condition information to process the second candidate point information to obtain second warehouse resource quantity scale information;

[0149] It should be noted that the processing means that the improved set covering model is used to process the target condition information and the second candidate point information to obtain the warehouse resource scale information; ​

[0150] It can be seen that the warehouse resource quantity and scale optimization method described in the embodiment of the present application uses the second optimization model to process the warehouse resource information set to obtain the second warehouse resource scale information, realizes the rapid optimization processing of the warehouse resource quantity and scale, and improves the processing efficiency of the warehouse resource quantity and scale optimization.

[0151] Embodiment two

[0152] Please refer to Figure 3 , Figure 3 is a structural schematic diagram of a warehouse resource quantity and scale optimization device disclosed by the embodiment of the present application. Wherein, Figure 3 The device described can be applied to a warehouse resource management system, such as a local server or a cloud server of a warehouse resource management system, and the embodiment of the present application does not make any limitation. As shown in the figure, Figure 3 The device can include:

[0153] The second aspect of the embodiment of the present application discloses a warehouse resource quantity and scale optimization device, and the device includes:

[0154] The acquisition module 101 is configured to acquire a warehouse resource information set.

[0155] The warehouse resource information set includes a demand point position information set, transportation cost information, inventory cost information, material throughput information, and transportation and distribution capacity information.

[0156] The model construction module 102 is configured to construct a warehouse resource quantity and scale optimization model.

[0157] The warehouse resource quantity and scale optimization model includes a first optimization model and a second optimization model.

[0158] The data processing module 103 is configured to use the warehouse resource quantity and scale optimization model to process the warehouse resource information set to obtain warehouse resource quantity information and warehouse resource scale information.

[0159] It can be seen that the warehouse resource quantity and scale optimization device described in the embodiment of the present application uses the warehouse resource quantity and scale optimization model to process the warehouse resource information set to obtain the warehouse resource quantity information and the warehouse resource scale information, realizes the optimization processing of the warehouse resource quantity and scale, and balances the relationship between the military benefit and the economic benefit of the warehouse resource. Figure 3 The warehouse resource quantity and scale optimization device described uses the warehouse resource quantity and scale optimization model to process the warehouse resource information set to obtain the warehouse resource quantity information and the warehouse resource scale information, realizes the optimization processing of the warehouse resource quantity and scale, and balances the relationship between the military benefit and the economic benefit of the warehouse resource.

[0160] Embodiment three

[0161] Please refer to Figure 4 , Figure 4 is a structural schematic diagram of another warehouse resource quantity and scale optimization device disclosed by the embodiment of the present application. Wherein, Figure 4The described apparatus can be applied to a warehouse resource management system, such as a local server or a cloud server for warehouse resource management system management, and the like, and embodiments of the present application are not limited. As shown in Figure 4 The apparatus can include:

[0162] a memory 201 storing executable program codes;

[0163] a processor 202 coupled with the memory 201;

[0164] The processor 202 invokes the executable program codes stored in the memory 201 for executing the steps in the warehouse resource quantity scale optimization method described in embodiment one.

[0165] Embodiment four

[0166] Embodiments of the present application disclose a computer readable storage medium storing a computer program for electronic data exchange, wherein the computer program causes a computer to execute the steps in the warehouse resource quantity scale optimization method described in embodiment one.

[0167] Embodiment five

[0168] Embodiments of the present application disclose a computer program product including a non-transitory computer readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute the steps in the warehouse resource quantity scale optimization method described in embodiment one.

[0169] The apparatus embodiments described above are only schematic, wherein the modules illustrated as separate components can or can not be physically separate, and the components illustrated as modules can or can not be physical modules, i.e., can be located in one place, or can be distributed to multiple network modules. Part or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment scheme. Those skilled in the art can understand and implement without creative labor.

[0170] Those skilled in the art can clearly understand the implementation of the various embodiments by means of software and the necessary general hardware platform through the above specific description of the embodiments, and of course, the embodiments can also be implemented by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part of the prior art that contributes to the present application can be embodied in the form of a software product. The computer software product can be stored in a computer readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, a magnetic disk storage, a magnetic tape storage, or any other computer readable medium that can be used to carry or store data.

[0171] Finally, it should be noted that: the warehouse resource quantity scale optimization method and device disclosed by the embodiments of the present application are only the preferred embodiments of the present application, and are used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that; it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing the quantity and scale of storage resources, characterized in that: The method comprises: S1, obtaining a warehouse resource information set; the warehouse resource information set includes a demand point location information set, transportation cost information, inventory cost information, material throughput information, and transportation and distribution capacity information; S2, constructing a storage resource quantity and scale optimization model; the storage resource quantity and scale optimization model includes a first optimization model and a second optimization model; S3: Using the storage resource quantity and scale optimization model, the storage resource information set is processed to obtain storage resource quantity and scale information.

2. The method for optimizing the quantity and scale of storage resources according to claim 1, characterized in that: The construction of the storage resource quantity and scale optimization model includes: S21, determining first resource optimization factor information and second resource optimization factor information; S22: Construct the first optimization model and the second optimization model based on the first resource optimization factor information and the second resource optimization factor information.

3. The method for optimizing the quantity and scale of storage resources according to claim 2, characterized in that: The constructing of the first optimization model and the second optimization model based on the first resource optimization factor information and the second resource optimization factor information includes: S221, determining first optimization target information; S222: construct a first optimization model based on the first optimization target information and the first resource optimization factor information; S223, determining second optimization target information; S224: Construct a second optimization model based on the second optimization target information and the first resource optimization factor information.

4. The method for optimizing the quantity and scale of storage resources according to claim 3, characterized in that: The first optimization model expression is: Where n represents the optimized quantity of the first warehouse; m represents the index of the first warehouse; n1 represents the value rounded down; n2 represents the value rounded up; C m (n) represents the continuous function of n; θ represents the area characteristic parameter; β represents the road circuit coefficient; a represents the effective area of ​​material support; k represents the transportation rate; μ represents the material purchase mode coefficient; A m Represents the total area of ​​the material support area; G m Indicates the total annual demand for material support in the mth warehouse.

5. The method for optimizing the quantity and scale of storage resources according to claim 1, characterized in that: The method of processing the warehousing resource information set using the warehousing resource quantity and scale optimization model to obtain warehousing resource quantity and scale information includes: S31, using the first optimization model, processing the storage resource information set to obtain first storage resource quantity and scale information; S32, using the second optimization model to process the storage resource information set to obtain second storage resource quantity and scale information; S33: Fusing the first storage resource quantity and scale information with the second storage resource quantity and scale information to obtain storage resource quantity and scale information.

6. The method for optimizing the quantity and scale of storage resources according to claim 5, characterized in that: The first optimization model is used to process the storage resource information set to obtain storage resource quantity information, including: S311, parsing the warehouse resource information set to obtain a demand point location information set, transportation cost information, inventory cost information, material throughput information, and transportation and distribution capacity information; S312, processing the demand point location information set to obtain guaranteed area information; S313, processing the transportation cost information, inventory cost information, material throughput information, and transportation and distribution capacity information to obtain total transportation cost information and total inventory cost information; S314: Using the first optimization model, process the security region area information, the total transportation cost information, and the total inventory cost information to obtain first storage resource quantity and scale information.

7. The method for optimizing the quantity and scale of storage resources according to claim 6, characterized in that: The processing of the demand point location information set to obtain the guaranteed area information includes: S3121, clustering the demand point location information set to obtain a grid area information set; S3122, processing the grid region information set to obtain a grid region area information set; S3123: Sum up the areas of all grid regions in the grid region area information set to obtain the guaranteed region area information.

8. A device for optimizing the quantity and scale of storage resources, characterized in that: The device comprises: Acquisition module, used to obtain storage resource information set; The storage resource information set includes a demand point location information set, transportation cost information, inventory cost information, material throughput information, and transportation and distribution capacity information; Model building module, used to build a warehouse resource quantity and scale optimization model; The storage resource quantity and scale optimization model includes a first optimization model and a second optimization model; The data processing module is used to process the storage resource information set using the storage resource quantity and scale optimization model to obtain storage resource quantity information and storage resource scale information.

9. A device for optimizing the quantity and scale of storage resources, characterized in that: The device comprises: a memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the warehouse resource quantity and scale optimization method as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program for electronic data exchange, wherein: The computer program enables a computer to execute the steps in the method for optimizing the quantity and scale of storage resources as described in any one of claims 1 to 7.

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