Storage resource library point layout optimization model construction method and device

Abstract

The invention discloses a storage resource library point layout optimization model construction method and device, and the method comprises the steps: carrying out the first analysis processing of obtained optimization demand information, and obtaining first optimization factor information and second optimization factor information; performing second analysis processing on the first optimization factor information and the second optimization factor information to obtain a first layout optimization information set and a second layout optimization information set; and performing third processing on the first layout optimization information set and the second layout optimization information set to obtain a storage resource library point layout optimization model and storage resource library point layout optimization result data. According to the method, the storage resource library point layout optimization demand information is obtained on the basis of the target task information, the storage resource library point layout optimization model is constructed, the storage resource library point deployment pertinence and the key demand satisfaction capability are improved, and the storage resource library point layout benefits are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the warehouse technology field, and in particular to a warehouse resource library point layout optimization model construction method and device. BACKGROUND

[0002] The research on warehouse resource library point layout optimization has formed a relatively complete system, but as for warehouse resource library point layout optimization, professional and quantitative research is still less. The reasonable selection of warehouse resource library point layout optimization solving algorithm can not only speed up the corresponding operation, but also improve the accuracy of the result. According to the quality of the solution, the solving algorithm can be divided into accurate algorithm and heuristic algorithm: the accurate algorithm can obtain the optimal solution, but the requirement for the input condition is extremely high, and it is often difficult to obtain in practice; the heuristic algorithm gradually approaches the optimal solution through certain iteration rules, although the accuracy is not as good as the accurate algorithm, but the calculation is simple and fast.

[0003] The various heuristic algorithms in the warehouse resource library point layout optimization solving algorithm have been widely applied due to their convenience in solving, and the improvement work of various algorithms and the nested use of each other have become more and more mature, laying a rich technical foundation for the solution of the theoretical model. Various algorithms are not uniformly superior or inferior, and different backgrounds, different tasks and different data have their most suitable algorithms. Therefore, the warehouse resource library point layout optimization model constructed according to the characteristics of the target task in the present research can improve the warehouse resource library point deployment pertinence and key demand satisfaction ability, so as to improve the warehouse resource library point layout benefit. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a warehouse resource layout adjustment method and device. The present application obtains warehouse resource library point layout optimization demand information based on target task information, constructs a warehouse resource library point layout optimization model, improves the warehouse resource library point deployment pertinence and key demand satisfaction ability, and improves the warehouse resource library point layout benefit.

[0005] To solve the above technical problems, the first aspect of the embodiment of the present application discloses a warehouse resource library point layout optimization model construction method, which comprises:

[0006] S1, obtaining first optimization demand information;

[0007] S2, performing first analysis and processing on the first optimization demand information to obtain first optimization factor information and second optimization factor information;

[0008] S3, performing second analysis and processing on the first optimization factor information and the second optimization factor information to obtain a first layout optimization information set and a second layout optimization information set;

[0009] S4, performing third processing on the first layout optimization information set and the second layout optimization information set to obtain a warehouse resource depot layout optimization model and warehouse resource depot layout optimization result data.

[0010] As an optional implementation, in the first aspect of the embodiment of the present application, the first analysis processing on the first optimization demand information to obtain the first optimization factor information and the second optimization factor information comprises:

[0011] S21, performing parsing processing on the first optimization demand information to obtain first function demand information and second function demand information;

[0012] S22, performing demand analysis on the first function demand information and the second function demand information to obtain second optimization demand information;

[0013] S23, performing parsing processing on the second optimization demand information to obtain the first optimization factor information and the second optimization factor information.

[0014] As an optional implementation, in the first aspect of the embodiment of the present application, the second analysis processing on the first optimization factor information and the second optimization factor information to obtain the first layout optimization information set and the second layout optimization information set comprises:

[0015] S31, performing processing on the first optimization factor information based on a first preprocessing model to obtain first preprocessing optimization information and second preprocessing optimization information;

[0016] S32, performing processing on the second optimization factor information based on a second preprocessing model to obtain third preprocessing optimization information;

[0017] S33, obtaining current warehouse resource data information;

[0018] S34, performing parsing processing on the current warehouse resource data information to obtain first depot location information, second depot location information, third depot location information and fourth depot location information;

[0019] S35, performing fusion processing on the first preprocessing optimization information, the second preprocessing optimization information, the third preprocessing optimization information, the first depot location information, the second depot location information, the third depot location information and the fourth depot location information to obtain the first layout optimization information set;

[0020] S36, performing fusion processing on the second preprocessing optimization information, the first depot location information, the second depot location information, the third depot location information and the fourth depot location information to obtain the second layout optimization information set.

[0021] As an optional implementation, in the first aspect of the embodiment of the present application, the processing of the first optimization factor information based on the first preprocessing model to obtain the first preprocessing optimization information and the second preprocessing optimization information comprises:

[0022] S311, obtaining the first optimization factor information;

[0023] S312, performing parsing processing on the first optimization factor information to obtain depot capacity information, depot location information and service point location information;

[0024] S313, processing the depot location information and the service point location information based on the depot capacity information to obtain coverage relationship information;

[0025] S314, constructing a coverage relationship matrix using the coverage relationship information;

[0026] S315, processing the coverage relationship matrix using the first preprocessing model to obtain the first preprocessing optimization information and the second preprocessing optimization information.

[0027] As an optional implementation, in the first aspect of the embodiment of the present application, the third processing of the first layout optimization information set and the second layout optimization information set to obtain a warehouse resource depot layout optimization model comprises:

[0028] S41, obtaining a warehouse resource information set;

[0029] S42, performing intensive processing on the first layout optimization information set, the second layout optimization information set and the warehouse resource information set to obtain a warehouse resource depot layout optimization model and warehouse resource depot layout optimization result data.

[0030] As an optional implementation, in the first aspect of the embodiment of the present application, the intensive processing of the first layout optimization information set, the second layout optimization information set and the warehouse resource information set to obtain a warehouse resource depot layout optimization model and warehouse resource depot layout optimization result data comprises:

[0031] S421, processing the first layout optimization information set and the second layout optimization information set to obtain a depot layout optimization constraint information set and a depot layout parameter set;

[0032] S422, constructing a warehouse resource depot layout model based on the depot layout optimization constraint information set;

[0033] S423, processing the warehouse resource site layout model by using the warehouse resource information set, to obtain a warehouse resource site layout optimization model;

[0034] S424, processing the site layout parameter set by using the warehouse resource site layout optimization model, to obtain warehouse resource site layout optimization result data.

[0035] As an optional implementation, in the first aspect of the embodiment of the application, the processing of the first layout optimization information set and the second layout optimization information set to obtain the site layout optimization constraint information set and the site layout parameter information set comprises:

[0036] S4211, obtaining the first layout optimization information set and the second layout optimization information set;

[0037] The first layout optimization information set comprises a plurality of first layout optimization information.

[0038] The second layout optimization information set comprises a plurality of second layout optimization information.

[0039] S4212, performing analysis processing on any first layout optimization information to obtain first site layout optimization constraint information and first site layout parameter information;

[0040] S4213, performing analysis processing on any second layout optimization information to obtain second site layout optimization constraint information and second site layout parameter information;

[0041] S4214, performing fusion processing on all the first site layout optimization constraint information and the second site layout optimization constraint information to obtain a site layout optimization constraint information set;

[0042] Performing fusion processing on all the first site layout parameter information and the second site layout parameter information to obtain a site layout parameter information set.

[0043] The second aspect of the embodiment of the application discloses a warehouse resource site layout optimization model construction device, characterized in that the device comprises:

[0044] An acquisition module is configured to acquire first optimization demand information.

[0045] A first processing module is configured to perform first analysis processing on the first optimization demand information to obtain first optimization factor information and second optimization factor information.

[0046] A second processing module is configured to perform second analysis processing on the first optimization factor information and the second optimization factor information to obtain a first layout optimization information set and a second layout optimization information set.

[0047] The third processing module is configured to perform third processing on the first set of layout optimization information and the second set of layout optimization information to obtain a warehouse resource depot layout optimization model and warehouse resource depot layout optimization result data.

[0048] The third aspect of the present application discloses another warehouse resource depot layout optimization model construction device, and the device comprises:

[0049] A memory storing executable program codes;

[0050] A processor coupled with the memory;

[0051] The processor invokes the executable program codes stored in the memory to perform part or all of the steps of the warehouse resource depot layout optimization model construction method disclosed in the first aspect of the present application.

[0052] 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 perform part or all of the steps of the warehouse resource depot layout optimization model construction method disclosed in the first aspect of the present application.

[0053] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0054] In the embodiments of the present application, the warehouse resource depot layout optimization demand information is obtained based on the target task information, and the warehouse resource depot layout optimization model is constructed, so that the warehouse resource depot deployment pertinence and the key demand satisfaction capability are improved, and the warehouse resource depot layout benefit is improved. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0056] Figure 1 is a scene schematic diagram of the warehouse resource management system provided by the embodiments of the present application;

[0057] Figure 2 is a flowchart of the warehouse resource depot layout optimization model construction method disclosed by the embodiments of the present application;

[0058] Figure 3 is a structure schematic diagram of the warehouse resource depot layout optimization model construction device disclosed by the embodiments of the present application;

[0059] Figure 4 is a structural schematic view of another warehouse resource depot layout optimization model construction device disclosed by the embodiment of the present application. DETAILED DESCRIPTION

[0060] 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.

[0061] The terms "first", "second", and the like in the specification 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.

[0062] In this document, reference to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearance of the phrase in various places in the specification is not necessarily all referring to the same embodiment, nor is it necessarily referring to a particular alternative embodiment or set of embodiments. It is explicitly understood that the embodiments described herein can be combined with one another, explicitly or implicitly.

[0063] In this application, the word "exemplary" is used to mean "serving as an example, instance, or illustration." Any implementation described herein 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, numerous details are set forth. It should be appreciated, however, that the present application can be practiced in a variety of configurations other than the embodiments described herein without resorting to undue

[0064] It should be noted that the method of the embodiments of the present application is executed in the computer device, and the processing objects of each computer device exist in the form of data or information, such as time, which is actually time information. It can be understood that if the size, quantity, position and the like are mentioned in the subsequent embodiments, they are corresponding data, so that the computer device can process, and details are not described here.

[0065] 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.

[0066] 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.

[0067] Computer vision (CV) 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.

[0068] Single-modal information refers to only one type of data, such as text, image, audio, video, electromagnetic signal, etc. Multi-modal information refers to 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 can usually be achieved in tasks.

[0069] 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, QWwen model, Baichuan model, Yuniu model, vivoLM model, DeepSeek model, and Wenxin Yanyan, etc. The embodiments of the present application are not limited.

[0070] The embodiments of the present application provide a warehouse resource library point layout optimization model construction method and device, computer equipment and computer readable storage medium, which are described in detail below.

[0071] Please refer to Figure 1 , Figure 1 The warehouse resource management system provided by the embodiments of the present application is a scene schematic diagram, which can include a computer device 100, and the computer device 100 is integrated with a warehouse resource library point layout optimization model construction device, such as Figure 1 The computer device in the above embodiment.

[0072] In the embodiments of the present application, the computer device 100 can be a standalone server, or a server network or server cluster composed of servers. For example, the computer device 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.

[0073] It can be understood that the computer device 100 used in the embodiments of the present application can be a device that includes receiving and transmitting hardware, that is, a device with receiving and transmitting hardware capable of performing bidirectional communication on a bidirectional communication link. Such a device can include a cellular or other communication device with a single-line display or a multi-line display or a cellular or other communication device without a multi-line display. The computer device 100 can be a desktop terminal or a mobile terminal, and the computer device 100 can also be one of a mobile phone, a tablet computer, a notebook computer, etc.

[0074] Those skilled in the art can understand that, Figure 1 The application environment shown in the above Figure 1 The application environment shown in the above Figure 1 Only one computer device is shown in the above It can be understood that the data processing system can also include one or more other services, which are not limited here.

[0075] In addition, as shown in the above Figure 1 The storage resource management system can also include a memory 200 for saving storage resource data, such as Google Maps data, guarantee point location information, warehouse location information, obstacle location information, and storage material information.

[0076] It should be noted that Figure 1 The scenario diagram of the storage resource management system shown in the above is only an example, and the storage resource management system and the scenario described in the embodiments of the present application are used to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, as the evolution of the storage resource management system and the emergence of new business scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0077] The application discloses a kind of storage resource library point layout optimization model construction method and device, to target task information is based on obtaining storage resource library point layout optimization demand information, constructs storage resource library point layout optimization model, improves the deployment of storage resource library point and key demand satisfaction ability, improves the benefit of storage resource library point layout. The following are described in detail.

[0078] Embodiment one

[0079] Please refer to Figure 2 , Figure 2 It is a flow diagram of the storage resource library point layout optimization model construction method disclosed in the embodiments of the present application. Among them, Figure 2The described warehouse resource depot layout optimization model construction method is applied to a warehouse resource management system, such as a local server or a cloud server of the warehouse resource management system, and embodiments of the present application are not limited. As shown in the figure, the warehouse resource depot layout optimization model construction method can include the following operations: Figure 1

[0080] S1, obtaining first optimization demand information;

[0081] It should be noted that the first optimization demand information represents the warehouse resource depot layout optimization demand information proposed by the user;

[0082] It should be noted that obtaining the first optimization demand information includes:

[0083] S11, obtaining target task information;

[0084] It should be noted that the target task information represents the warehouse resource optimization target task information proposed by the user;

[0085] It should be noted that the warehouse resource optimization target task information includes: function demand information, target site selection information, constraint condition information;

[0086] S12, analyzing and processing the target task information to obtain the first optimization demand information;

[0087] It should be noted that the first optimization demand information includes depot type information, depot function demand information, depot alternative address information and support point address information;

[0088] S2, first analyzing and processing the first optimization demand information to obtain first optimization factor information and second optimization factor information;

[0089] S3, second analyzing and processing the first optimization factor information and the second optimization factor information to obtain a first layout optimization information set and a second layout optimization information set;

[0090] S4, third processing the first layout optimization information set and the second layout optimization information set to obtain a warehouse resource depot layout optimization model and a warehouse resource depot layout optimization result data.

[0091] It can be seen that the warehouse resource depot layout optimization model construction method described in the embodiments of the present application obtains warehouse resource depot layout optimization demand information based on target task information, constructs a warehouse resource depot layout optimization model, improves the warehouse resource depot deployment pertinence and key demand satisfaction capability, and improves the warehouse resource depot layout benefit.

[0092] ​In an optional embodiment, in step S2, the first analysis and processing of the first optimization demand information to obtain the first optimization factor information and the second optimization factor information comprises:

[0093] S21, performing parsing processing on the first optimization demand information to obtain first function demand information and second function demand information;

[0094] It should be noted that the parsing processing means obtaining the first function demand information and the second function demand information according to the flag bit;

[0095] It should be noted that the first function demand information means first warehouse function demand information;

[0096] It should be noted that the first warehouse means a user-defined storage and supply base;

[0097] It should be noted that the first warehouse function demand information comprises address demand information,

[0098] It should be noted that the second function demand information means second warehouse function demand information;

[0099] It should be noted that the second warehouse means a user-defined temporary warehouse;

[0100] It should be noted that the storage and supply base is a hub, and the temporary warehouse is a node;

[0101] S22, performing demand analysis on the first function demand information and the second function demand information to obtain second optimization demand information;

[0102] S23, performing parsing processing on the second optimization demand information to obtain the first optimization factor information and the second optimization factor information.

[0103] It can be seen that the warehouse resource hub and node layout optimization model construction method described in the embodiments of the present application performs first analysis and processing on hub and node layout optimization demand information to obtain first optimization factor information and second optimization factor information, which provides data support for subsequent data processing, and is beneficial to improve the overall operation efficiency and flexibility of the warehouse, and further improve the intelligent degree of warehouse resource layout adjustment and the optimal configuration of warehouse resources.

[0104] In another optional embodiment, in step S22, the demand analysis on the first function demand information and the second function demand information to obtain the second optimization demand information comprises:

[0105] S221, obtaining the first function demand information;

[0106] S222, performing analysis processing on the first function requirement information to obtain a first flag bit effective value;

[0107] It should be noted that the analysis processing represents obtaining the value of the effective identification bit in the first function requirement information;

[0108] S223, judging whether the first flag bit effective value is equal to a first set value to obtain a first requirement judgment result;

[0109] It should be noted that the first set value is 1, which represents that the first flag bit effective value is 1, and the first warehouse function requirement information analysis is performed;

[0110] S224, when the first requirement judgment result is no, performing S225-S228;

[0111] When the first requirement judgment result is yes, the first function requirement information is updated to target warehouse function information, and S229 is performed;

[0112] S225, obtaining the second function requirement information;

[0113] S226, performing analysis processing on the second function requirement information to obtain a second flag bit effective value;

[0114] It should be noted that the analysis processing represents obtaining the value of the effective identification bit in the second function requirement information;

[0115] S227, judging whether the second flag bit effective value is equal to a first set value to obtain a second requirement judgment result;

[0116] It should be noted that the first set value is 1, which represents that the second flag bit effective value is 1, and the second warehouse function requirement information analysis is performed;

[0117] S228, when the second requirement judgment result is no, performing S221;

[0118] When the second requirement judgment result is yes, the second function requirement information is updated to target warehouse function information, and S229 is performed;

[0119] S229, performing analysis processing on the target warehouse function information to obtain second optimization requirement information;

[0120] It should be noted that the analysis processing represents obtaining target warehouse type, target warehouse address information, target warehouse performance parameter information and target area data information according to field type;

[0121] It should be noted that the second optimization requirement information includes target warehouse type, target warehouse address information, target warehouse performance parameter information and target area data information.

[0122] It can be seen that the warehouse resource point layout optimization model construction method described in the embodiment of the application performs requirement analysis on the first function requirement information and the second function requirement information to obtain warehouse resource point layout optimization requirement information, which provides data support for subsequent data processing, is conducive to improving the overall operation efficiency and flexibility of the warehouse, and further improves the intelligent degree of warehouse resource layout adjustment and the optimal configuration of warehouse resources.

[0123] In another optional embodiment, the second optimization requirement information is parsed in step S23 to obtain first optimization factor information and second optimization factor information, including:

[0124] S231, obtaining the second optimization requirement information;

[0125] S232, parsing the second optimization requirement information to obtain target warehouse type, target warehouse address information, target warehouse performance parameter information and target area data information;

[0126] It should be noted that the parsing process means obtaining target warehouse type, target warehouse address information, target warehouse performance parameter information and target area data information according to field type;

[0127] S232, judging whether the target warehouse type is equal to the second preset value to obtain a warehouse type judgment result;

[0128] It should be noted that the second preset value is 0, indicating that the target warehouse is a first-level warehouse;

[0129] S233, when the warehouse type judgment result is yes, executing S234;

[0130] When the warehouse type judgment result is no, executing S235;

[0131] S234, performing first parsing processing on the second optimization requirement information to obtain first optimization factor information;

[0132] It should be noted that the first parsing processing means extracting data of corresponding fields according to field type based on the data structure of first-level warehouse requirement information to obtain first optimization factor information;

[0133] It should be noted that the first optimization factor information represents first-level warehouse optimization factor information;

[0134] It should be noted that the first warehouse optimization factor information includes first warehouse capacity information, first warehouse location information and first warehouse service area data information.

[0135] S235, the second optimization demand information is analyzed to obtain the second optimization factor information;

[0136] It should be noted that the second analysis process means that based on the data structure of the second warehouse demand information, the data of the corresponding field is extracted according to the field type to obtain the first optimization factor information.

[0137] It should be noted that the second optimization factor information represents the second warehouse optimization factor information.

[0138] It should be noted that the second warehouse optimization factor information includes second warehouse capacity information, second warehouse location information and second warehouse service area data information.

[0139] It can be seen that the warehouse resource point layout optimization model construction method described in the embodiment of the application analyzes the warehouse resource point layout optimization demand information to obtain the first optimization factor information and the second optimization factor information, which provides data support for subsequent data processing, and is beneficial to improve the overall operation efficiency and flexibility of the warehouse, and further improve the intelligent degree of warehouse resource layout adjustment and the optimal configuration of warehouse resources.

[0140] In another optional embodiment, the second analysis processing of the first optimization factor information and the second optimization factor information in the above step S3 to obtain the first layout optimization information set and the second layout optimization information set includes:

[0141] S31, based on the first preprocessing model, the first optimization factor information is processed to obtain the first preprocessing optimization information and the second preprocessing optimization information;

[0142] S32, based on the second preprocessing model, the second optimization factor information is processed to obtain the third preprocessing optimization information;

[0143] S33, the current warehouse resource data information is obtained;

[0144] S34, the current warehouse resource data information is analyzed to obtain the first information, the second information, the third information and the fourth information;

[0145] It should be noted that the analysis process means that the first information, the second information, the third information and the fourth information are obtained according to the field type.

[0146] It should be noted that the first information is used to represent the candidate point position information of the first warehouse; the second information represents the candidate point position information of the second warehouse; the third position information represents the service area position information, including the position information of the first warehouse guarantee area and the position information of the second warehouse guarantee area; and the fourth information represents the target area material demand information.

[0147] S35, the first preprocessing optimization information, the second preprocessing optimization information, the third preprocessing optimization information, the first information, the second information, the third information and the fourth information are fused to obtain a first layout optimization information set;

[0148] S36, the second preprocessing optimization information, the first warehouse point position information, the second warehouse point position information, the third warehouse point position information and the fourth warehouse point position information are fused to obtain a second layout optimization information set;

[0149] It should be noted that the fusion processing means that the second preprocessing optimization information, the first warehouse point position information, the second warehouse point position information, the third warehouse point position information and the fourth warehouse point position information are combined in time sequence.

[0150] It can be seen that the warehouse resource warehouse point layout optimization model construction method described in the embodiment of the application performs second analysis processing on the first optimization factor information and the second optimization factor information to obtain a first layout optimization information set and a second layout optimization information set, which provides data support for subsequent data set integration processing, and is beneficial to improve the overall operation efficiency and flexibility of the warehouse, and further improve the intelligent degree of warehouse resource layout adjustment and the optimal configuration of warehouse resources.

[0151] In another optional embodiment, in the step S31, the first optimization factor information is processed based on the first preprocessing model to obtain the first preprocessing optimization information and the second preprocessing optimization information, which includes:

[0152] S311, the first optimization factor information is obtained;

[0153] It should be noted that the first optimization factor information represents the first warehouse optimization factor information.

[0154] S312, the first optimization factor information is analyzed to obtain warehouse point capacity information, warehouse point position information and service area data information;

[0155] It should be noted that the analysis processing means that the warehouse point capacity information, the warehouse point position information and the service area data information are obtained according to the field type.

[0156] S313, processing the depot location information and the service area data information based on the depot capability information to obtain coverage relationship information;

[0157] S314, constructing a coverage relationship matrix using the coverage relationship information;

[0158] S315, processing the coverage relationship matrix using the first preprocessing model to obtain first preprocessing optimization information and second preprocessing optimization information;

[0159] It should be noted that the first preprocessing model expression is:

[0160]

[0161] 0≤y ij ≤1, xj∈{0,1};

[0162] Wherein, N represents the number of target depots; j represents the current depot index; i represents; x j represents; H represents the position set of candidate depots; B m represents; A(j) represents the position set of the target region that can be covered by the jth candidate depot; B(i) represents the candidate depot position set that can cover the ith target region; represents the material demand of the ith front ad region; C j represents the material throughput capacity of the jth candidate depot; represents the selected decision scalar of the jth candidate depot; y ij represents the proportion of the material quantity of the ith target region implemented by the jth candidate depot to the total demand of the material;

[0163] It should be noted that when the x j =1, indicates that the jth candidate depot is selected; when the x j =0, indicates that the jth candidate depot is not selected;

[0164] It should be noted that when the y ij =1, indicates that the jth candidate depot implements material supply service for the ith service region;

[0165] It should be noted that when the y ij =0, indicates that the jth candidate depot does not implement material supply service for the ith service region;

[0166] It should be noted that the A(j) is the time radiation radius, which comprehensively considers the time efficiency and radiation radius;

[0167] It should be noted that the objective function N = min∑ j∈H xj The number of the warehouse points is the smallest, which meets the general requirement of intensive construction of the warehouse point layout and the number optimization target;

[0168] The The material demand of each target region can be fully met;

[0169] The i∈A(j) d i y ij ≤C j x j The material demand of each target region can be fully met;

[0170] The 0≤y ij ≤1, x j ∈{0,1} is used to allow the selected first-level warehouse point to provide partial material supply service for the target region, and the unselected first-level warehouse point cannot provide material supply service for the target region, which not only meets the characteristics of the user material supply, but also achieves the purpose of selecting the first-level candidate warehouse point.

[0171] It can be seen that the warehouse resource warehouse point layout optimization model construction method described in the embodiment of the application is based on the first preprocessing model, the first optimization factor information is processed to obtain first preprocessing optimization information and second preprocessing optimization information, which provides data support for subsequent data intensive processing, is beneficial to improve the overall operation efficiency and flexibility of the warehouse, and further improves the intelligent degree of the warehouse resource layout adjustment and the optimal configuration of the warehouse resource.

[0172] In another optional embodiment, in the step S32, the second optimization factor information is processed based on the second preprocessing model to obtain third preprocessing optimization information, which includes:

[0173] S321, the second optimization factor information is obtained;

[0174] It should be noted that the second optimization factor information represents the secondary warehouse optimization factor information

[0175] S322, the second optimization factor information is processed by a function to obtain first preprocessing data;

[0176] It should be noted that the function processing expression is:

[0177] C=f(n, G, A);

[0178] Wherein, n represents the target area guarantee warehouse quantity; G represents the target area guarantee material total amount; A represents the target area area;

[0179] It should be noted that C=f(n, G, A) represents the constructed objective function;

[0180] S323, using a second preprocessing model, processing the first preprocessing data to obtain third preprocessing optimization information;

[0181] It should be noted that the second preprocessing model is expressed as:

[0182]

[0183] q i ≥0;

[0184]

[0185] Wherein M represents a service area position set; L represents a candidate depot position set; q i represents the material demand of the i-th service area; c represents the unit material transportation rate; d ij represents the actual distance from the i-th service area to the j-th candidate depot; p represents the optimization result of the candidate depot quantity scale; x j represents the j-th candidate depot decision variable; y ij represents the i-th service area whether the j-th candidate depot implements material supply service decision variable;

[0186] It should be noted that when the x j =1, it means that the j-th candidate depot is selected; when the x j =0, it means that the j-th candidate depot is not selected;

[0187] It should be noted that when the y ij =1, it means that the j-th candidate depot implements material supply service for the i-th service area;

[0188] It should be noted that when the y ij =0, it means that the j-th candidate depot does not implement material supply service for the i-th service area.

[0189] It should be noted that the is an objective function, which means that the material transportation cost is minimized, and the traffic factor is considered as the key factor for the first warehouse site selection;

[0190] It should be noted that represents that the demand of the same type of material in each service area can only be implemented by one first warehouse depot;

[0191] It should be noted that the y ij ≤x j , For ensuring that the unselected candidate warehouse points cannot provide material supply service for the service area;

[0192] It should be noted that the ∑ j∈L x j = p for limiting the number of selected warehouse points to be equal to the preset economic reasonable number value;

[0193] It should be noted that the y ij , x j ∈ {0, 1}, For establishing the "convenient and nearby" supply and supplied relationship between the warehouse points and the service area;

[0194] It should be noted that the second preprocessing model is an improved P-median model, and by increasing multi-objective optimization, dynamic demand and other actual factors, a warehouse layout optimization model more suitable for the real scene is constructed.

[0195] It can be seen that the warehouse resource warehouse point layout optimization model construction method described in the embodiment of the application is based on the second preprocessing model, the second optimization factor information is processed to obtain third preprocessing optimization information, which provides data support for subsequent data set intensive processing, and is beneficial to improve the overall operation efficiency and flexibility of the warehouse, and further improve the intelligent degree of warehouse resource layout adjustment and the optimal configuration of warehouse resources.

[0196] In still another optional embodiment, in the step S4, the third processing of the first layout optimization information set and the second layout optimization information set to obtain warehouse resource warehouse point layout optimization result data comprises:

[0197] S41, obtaining a warehouse resource information set;

[0198] It should be noted that the warehouse resource information set comprises a plurality of warehouse resource information;

[0199] It should be noted that the warehouse resource information comprises first warehouse candidate point information, second warehouse candidate point information and target area information;

[0200] It should be noted that the first warehouse candidate point information comprises first warehouse candidate point position information and first warehouse candidate point capacity information;

[0201] It should be noted that the second warehouse candidate point information comprises second warehouse candidate point position information and second warehouse candidate point capacity information;

[0202] It should be noted that the target area information comprises target area position information and target area demand information;

[0203] S42, intensively processing the first layout optimization information set, the second layout optimization information set and the warehouse resource information set to obtain a warehouse resource depot layout optimization model and warehouse resource depot layout optimization result data.

[0204] It can be seen that the warehouse resource depot layout optimization model construction method described in the embodiments of the present application is beneficial to improving the overall operation efficiency and flexibility of the warehouse, and further improving the intelligent degree of warehouse resource layout adjustment and the optimal configuration of warehouse resources.

[0205] In another optional embodiment, in the step S42, the intensively processing the first layout optimization information set, the second layout optimization information set and the warehouse resource information set to obtain a warehouse resource depot layout optimization model and warehouse resource depot layout optimization result data, comprises:

[0206] S421, processing the first layout optimization information set and the second layout optimization information set to obtain a depot layout optimization constraint information set and a depot layout parameter set;

[0207] S422, constructing a warehouse resource depot layout model based on the depot layout optimization constraint information set;

[0208] It should be noted that the warehouse resource depot layout model expression is:

[0209]

[0210] Wherein, L represents the total path from the target warehouse to the target area; j represents the jth target warehouse; N represents the total number of target areas; i represents the index of the target area; x j represents the jth candidate depot decision variable; D j represents the set of target areas covered by the jth target warehouse; D lij represents the distance from the ith target area to the jth target area on the path l; d i represents the demand of the ith target area; R j represents the maximum capacity of the jth target warehouse; T represents the total guarantee period; x it represents the decision variable of whether the jth target warehouse supplies the target area at time t; α represents the first coefficient; β represents the second coefficient;

[0211] It should be noted that the first coefficient represents the un-covered loss weight coefficient, which is set to α = 1.5 in this embodiment;

[0212] It should be noted that the second coefficient represents the uncovered loss, and the embodiment is set to β=0.03;

[0213] It should be noted that the A multi-objective function is constructed by combining coverage rate and intensive index, single-bin average utilization rate, unit cost coverage, and shortest total transportation distance.

[0214] It should be noted that the The capacity intensive constraint increases the warehouse capacity limit to avoid waste of facility resources.

[0215] It should be noted that the ∑ t∈T (∑R j x jt +α·β) is a dynamic demand intensive, which introduces a time dimension to optimize the flexibility of warehouse layout in different periods (such as peak season / off-season).

[0216] S423, using the warehouse resource information set, processing the warehouse resource point layout model to obtain a warehouse resource point layout optimization model.

[0217] It should be noted that the optimization processing means verifying the warehouse resource point layout model using the warehouse resource information set to obtain a warehouse resource point layout optimization model.

[0218] S424, using the warehouse resource point layout optimization model to process the point layout parameter set to obtain warehouse resource point layout optimization result data.

[0219] It should be noted that the processing means inputting the point layout parameter set into the second point layout model to obtain warehouse resource point layout optimization result data.

[0220] It can be seen that the warehouse resource point layout optimization model construction method described in the embodiment of the application performs intensive processing on the first layout optimization information set, the second layout optimization information set, and the warehouse resource information set to obtain a warehouse resource point layout optimization model, which is beneficial to improve the overall operation efficiency and flexibility of the warehouse, and further improve the intelligent degree of warehouse resource layout adjustment and the optimal configuration of warehouse resources.

[0221] In another optional embodiment, in the step S421, the processing of the first layout optimization information set and the second layout optimization information set to obtain the point layout optimization constraint information set and the point layout parameter information set comprises:

[0222] S4211, obtaining the first layout optimization information set and the second layout optimization information set.

[0223] The first layout optimization information set includes a plurality of first layout optimization information.

[0224] The second layout optimization information set includes a plurality of second layout optimization information.

[0225] S4212, any of the first layout optimization information is parsed and processed to obtain first library point layout optimization constraint information and first library point layout parameter information.

[0226] It should be noted that the parsing and processing means that the first library point layout optimization constraint information and the first library point layout parameter information are obtained according to the field type.

[0227] S4213, any of the second layout optimization information is parsed and processed to obtain second library point layout optimization constraint information and second library point layout parameter information.

[0228] It should be noted that the parsing and processing means that the second library point layout optimization constraint information and the second library point layout parameter information are obtained according to the field type.

[0229] S4214, all of the first library point layout optimization constraint information and the second library point layout optimization constraint information are fused to obtain a library point layout optimization constraint information set.

[0230] All of the first library point layout parameter information and the second library point layout parameter information are fused to obtain a library point layout parameter information set.

[0231] It can be seen that the warehouse resource library point layout optimization model construction method described in the embodiment of the application processes the first layout optimization information set and the second layout optimization information set to obtain the library point layout optimization constraint information set and the library point layout parameter information set, which provides data support for subsequent data set standardization processing, and is beneficial to improve the overall operation efficiency and flexibility of the warehouse, and further improve the intelligent degree of warehouse resource layout adjustment and the optimal configuration of warehouse resources.

[0232] Embodiment two

[0233] Please refer to Figure 3 , Figure 3 is a structure diagram of a warehouse resource library point layout optimization model construction device disclosed by the embodiment of the 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 application does not limit it. As Figure 3 As shown in the figure, the device can include:

[0234] The second aspect of the embodiment of the application discloses a warehouse resource library point layout optimization model construction device, and the device includes:

[0235] An acquisition module 101 is configured to acquire first optimization requirement information;

[0236] A first processing module 102 is configured to perform a first analysis on the first optimization requirement information to obtain first optimization factor information and second optimization factor information;

[0237] A second processing module 103 is configured to perform a second analysis on the first optimization factor information and the second optimization factor information to obtain a first layout optimization information set and a second layout optimization information set;

[0238] A third processing module 104 is configured to perform a third processing on the first layout optimization information set and the second layout optimization information set to obtain a warehouse resource storage point layout optimization model and warehouse resource storage point layout optimization result data;

[0239] It can be seen that implementation Figure 3 The described storage resource storage point layout optimization model construction device analyzes and processes the storage resource storage point layout optimization demand information to obtain first optimization factor information and second optimization factor information, providing data support for subsequent data processing, which is conducive to improving the overall operation efficiency and flexibility of the warehouse, and thereby improving the intelligence level of storage resource layout adjustment and the optimal configuration of storage resources.

[0240] Example 3

[0241] See also Figure 4 , Figure 4 This is a structural diagram of another storage resource point layout optimization model construction device disclosed in an embodiment of the present invention. Figure 4 The described device can be applied to a warehouse resource management system, such as a local server or cloud server for managing a warehouse resource management system, and the embodiment of the present invention does not limit this. Figure 4 As shown, the device may include:

[0242] A memory 201 storing executable program code;

[0243] a processor 202 coupled to the memory 201;

[0244] The processor 202 calls the executable program code stored in the memory 201 to execute the steps in the method for constructing the warehouse resource storage point layout optimization model described in the first embodiment.

[0245] Example 4

[0246] The embodiment of the present application discloses a computer readable storage medium, which stores a computer program for electronic data exchange, wherein the computer program causes a computer to execute steps in the warehouse resource library point layout optimization model construction method described in embodiment one.

[0247] Embodiment five

[0248] The embodiment of the present application discloses a computer program product, which comprises a non-transitory computer readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute steps in the warehouse resource library point layout optimization model construction method described in embodiment one.

[0249] The above-described apparatus embodiments 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 on multiple network modules. Part or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment. Those skilled in the art can understand and implement without creative labor.

[0250] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software and necessary general hardware platform, and of course can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of software products, and the computer software product can be stored in a computer readable storage medium, and the storage medium includes a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a programmable read-only memory (Programmable Read-only Memory, PROM), an erasable programmable read-only memory (Erasable Programmable Read Only Memory, EEPROM), a one-time programmable read-only memory (One-time Programmable Read-Only Memory, OTPROM), an electrically erasable programmable read-only memory (Electrically-Erasable Programmable Read-Only Memory, EEPROM), a compact disc read-only memory (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.

[0251] It should be pointed out finally that: the warehouse resource depot layout optimization model construction 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: the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced equivalently; and these modifications or replacements do not make the essence of 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 constructing a warehouse resource point layout optimization model, characterized in that: The method comprises: S1, obtaining first optimization requirement information; S2, performing a first analysis process on the first optimization requirement information to obtain first optimization factor information and second optimization factor information; S3, performing a second analysis process on the first optimization factor information and the second optimization factor information to obtain a first layout optimization information set and a second layout optimization information set; S4: Perform a third processing on the first layout optimization information set and the second layout optimization information set to obtain a warehouse resource storage point layout optimization model and warehouse resource storage point layout optimization result data.

2. The method for constructing a warehouse resource storage point layout optimization model according to claim 1, characterized in that: The first analysis and processing of the first optimization requirement information to obtain the first optimization factor information and the second optimization factor information includes: S21, parsing the first optimization requirement information to obtain first function requirement information and second function requirement information; S22, performing demand analysis on the first function requirement information and the second function requirement information to obtain second optimized demand information; S23: Analyze the second optimization requirement information to obtain first optimization factor information and second optimization factor information.

3. The method for constructing a warehouse resource storage point layout optimization model according to claim 2, characterized in that: The performing a second analysis on the first optimization factor information and the second optimization factor information to obtain a first layout optimization information set and a second layout optimization information set includes: S31, processing the first optimization factor information based on a first preprocessing model to obtain first preprocessing optimization information and second preprocessing optimization information; S32, processing the second optimization factor information based on the second preprocessing model to obtain third preprocessing optimization information; S33, obtaining current storage resource data information; S34, parsing the current storage resource data information to obtain first storage point location information, second storage point location information, third storage point location information, and fourth storage point location information; S35: Fusing the first preprocessing optimization information, the second preprocessing optimization information, the third preprocessing optimization information, the first storage point location information, the second storage point location information, the third storage point location information, and the fourth storage point location information to obtain a first layout optimization information set; S36: Fusing the second pre-processing optimization information, the first storage point location information, the second storage point location information, the third storage point location information, and the fourth storage point location information to obtain a second layout optimization information set.

4. The method for constructing a warehouse resource storage point layout optimization model according to claim 3, characterized in that: The processing of the first optimization factor information based on the first preprocessing model to obtain first preprocessing optimization information and second preprocessing optimization information includes: S311, obtaining the first optimization factor information; S312: Analyze the first optimization factor information to obtain storage point capacity information, storage point location information, and service point location information; S313, based on the storage point capability information, processing the storage point location information and the service point location information to obtain coverage relationship information; S314, constructing a coverage relationship matrix using the coverage relationship information; S315: Process the coverage relationship matrix using the first preprocessing model to obtain first preprocessing optimization information and second preprocessing optimization information.

5. The method for constructing a warehouse resource storage point layout optimization model according to claim 1, characterized in that: The third processing of the first layout optimization information set and the second layout optimization information set to obtain a warehouse resource storage point layout optimization model includes: S41, obtaining a storage resource information set; S42, performing intensive processing on the first layout optimization information set, the second layout optimization information set and the storage resource information set to obtain a storage resource storage point layout optimization model and storage resource storage point layout optimization result data.

6. The method for constructing a warehouse resource storage point layout optimization model according to claim 4, characterized in that: The intensive processing of the first layout optimization information set, the second layout optimization information set and the storage resource information set to obtain a storage resource storage point layout optimization model and storage resource storage point layout optimization result data includes: S421: Process the first layout optimization information set and the second layout optimization information set to obtain a storage point layout optimization constraint information set and a storage point layout parameter set; S422: constructing a warehouse resource storage point layout model based on the storage point layout optimization constraint information set; S423, using the storage resource information set, processing the storage resource storage point layout model to obtain a storage resource storage point layout optimization model; S424: Process the storage point layout parameter set using a storage resource storage point layout optimization model to obtain storage resource storage point layout optimization result data.

7. The method for constructing a warehouse resource storage point layout optimization model according to claim 5, characterized in that: The processing of the first layout optimization information set and the second layout optimization information set to obtain a storage point layout optimization constraint information set and a storage point layout parameter information set includes: S4211, obtaining the first layout optimization information set and the second layout optimization information set; The first layout optimization information set includes a plurality of first layout optimization information; The second layout optimization information set includes a plurality of pieces of second layout optimization information; S4212: Analyze any of the first layout optimization information to obtain first storage point layout optimization constraint information and first storage point layout parameter information; S4213: Analyze any of the second layout optimization information to obtain second storage point layout optimization constraint information and second storage point layout parameter information; S4214: Fusing all of the first storage point layout optimization constraint information and the second storage point layout optimization constraint information to obtain a storage point layout optimization constraint information set; All the first storage point layout parameter information and the second storage point layout parameter information are fused to obtain a storage point layout parameter information set.

8. A device for constructing a warehouse resource storage point layout optimization model, characterized in that: The device comprises: An acquisition module, configured to acquire first optimization requirement information; a first processing module, configured to perform a first analysis on the first optimization requirement information to obtain first optimization factor information and second optimization factor information; a second processing module, configured to perform a second analysis process on the first optimization factor information and the second optimization factor information to obtain a first layout optimization information set and a second layout optimization information set; The third processing module is used to perform a third processing on the first layout optimization information set and the second layout optimization information set to obtain a warehouse resource storage point layout optimization model and warehouse resource storage point layout optimization result data.

9. A device for constructing a warehouse resource storage point layout optimization model, 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 storage point layout optimization model construction method according to any one of claims 1 to 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 constructing a warehouse resource storage point layout optimization model as described in any one of claims 1 to 7.

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