Sorting plan making method, system, electronic device and storage medium

Abstract

This application provides a sorting plan formulation method, system, electronic device, and storage medium. Based on user-inputted sorting demand information and target sorting data from the target transfer station, it formulates an operations optimization process whose optimization direction aligns with the direction represented by the sorting demand information. Based on the sorting data and the operations optimization process, it simulates and optimizes the sorting plan for the target transfer station to obtain the target sorting plan. In this way, without manual formulation of optimization schemes, a reasonable operations optimization process can be formulated based on the sorting demand information and sorting data of the target transfer station, and the sorting plan for the target transfer station can be simulated and optimized to formulate the target sorting plan, thereby achieving the effect of quickly determining the sorting plan for each transfer station.

Description

Technical Field

[0001] This application relates to the field of logistics technology, specifically to a sorting plan formulation method, system, electronic device, and storage medium. Background Technology

[0002] Logistics transit hubs are important nodes in the life cycle of express delivery. After the express delivery arrives at the transit hub, it is sorted according to its destination by automated assembly line equipment and manual labor. The speed of sorting affects the delivery time of the express delivery.

[0003] Generally, logistics transit centers include sorting equipment such as swing wheel sorters, small-item sorters, and sorting cabinets. After unloading from the unloading port, large packages are directly sorted by the swing wheel sorter to the corresponding loading port, while small packages are either sent to the small-item sorter or to the sorting cabinets for manual sorting. The sorting equipment in the transit center sorts packages according to a sorting plan, which includes a swing wheel sorting plan, a small-item sorting machine sorting plan, and a sorting cabinet sorting plan. The quality of the sorting plan determines the production efficiency of the sorting equipment in the transit center, i.e., the efficiency of sorting packages.

[0004] In existing technologies, the formulation of sorting plans for transit hubs relies on manual experience, which is insufficient to cope with complex and ever-changing logistics demands. How to quickly develop sorting plans that meet the specific needs of each transit hub has become a pressing issue. Summary of the Invention

[0005] Based on the defects and shortcomings of the existing technology, this application proposes a sorting plan formulation method, system, electronic device and storage medium, which can formulate an operations optimization process based on the sorting demand information and target sorting data of the target transfer station, and perform simulation optimization on the sorting plan of the target transfer station based on the operations optimization process and the target sorting data, so as to quickly obtain the target sorting plan.

[0006] According to a first aspect of the embodiments of this application, a sorting plan formulation method is provided, applied to an operating system, including:

[0007] Receive sorting requirements information for the target transit area input by the user;

[0008] Based on the sorting demand information and the target sorting data of the target transit site, an operations optimization process is formulated, wherein the optimization direction of the operations optimization process is consistent with the demand direction represented by the sorting demand information.

[0009] Based on the target sorting data and the operations optimization process, the sorting plan of the target transfer station is simulated and optimized to obtain the target sorting plan.

[0010] According to a second aspect of the embodiments of this application, a sorting plan formulation apparatus is provided, comprising:

[0011] The receiving unit is used to receive the sorting requirement information of the target transfer site input by the user;

[0012] The determining unit is used to formulate an operations optimization process based on the sorting demand information and the target sorting data of the target transit site, wherein the optimization direction of the operations optimization process is consistent with the demand direction represented by the sorting demand information.

[0013] The simulation optimization unit is used to perform simulation optimization on the sorting plan of the target transfer station based on the target sorting data and the operation optimization process, so as to obtain the target sorting plan.

[0014] According to a third aspect of the embodiments of this application, a sorting plan formulation system is provided, the sorting plan formulation system including an artificial intelligence module; wherein...

[0015] The artificial intelligence module is used to receive sorting demand information of the target transfer site input by the user, and formulate an operation optimization process based on the sorting demand information and the target sorting data of the target transfer site. The optimization direction of the operation optimization process is consistent with the demand direction represented by the sorting demand information.

[0016] The artificial intelligence module is used to simulate and optimize the sorting plan of the target transfer station based on the target sorting data and the operation optimization process, so as to obtain the target sorting plan.

[0017] According to a fourth aspect of the embodiments of this application, an electronic device is provided, including a memory and a processor;

[0018] The memory is connected to the processor and is used to store programs;

[0019] The processor is used to implement the sorting plan formulation method as described in the first aspect by running the program in the memory.

[0020] According to a fifth aspect of the present application, a storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the sorting plan formulation method as described in the first aspect.

[0021] The aforementioned sorting plan formulation method, system, electronic device, and storage medium can receive sorting demand information from the target transfer station input by the user. Based on this sorting demand information and the target sorting data of the target transfer station, an operations optimization process is formulated. The optimization direction of this operations optimization process is consistent with the demand direction represented by the sorting demand information. Subsequently, based on the target sorting data and the operations optimization process, the sorting plan for the target transfer station is simulated and optimized to obtain the target sorting plan. In this way, based on the sorting demand of the target transfer station and combined with the target sorting data of the target transfer station, a reasonable optimization scheme with an optimization direction consistent with the demand direction represented by the sorting demand information can be formulated, i.e., an operations optimization process. Simulation optimization is then performed based on this operations optimization process to determine the target sorting plan that can meet the sorting demand. This achieves the effect of quickly determining the sorting plan for each transfer station without the need for manual formulation of optimization schemes. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating a sorting plan formulation method as provided in an embodiment of this application.

[0024] Figure 2 This is a schematic diagram illustrating a sorting plan development process as provided in an embodiment of this application;

[0025] Figure 3 This application provides a schematic diagram of the topology of a sorting planning system.

[0026] Figure 4 This is a schematic diagram of the structure of a sorting plan formulation device proposed in an embodiment of this application;

[0027] Figure 5 This is a schematic diagram of the structure of an electronic device proposed in an embodiment of this application. Detailed Implementation

[0028] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] Overview

[0030] As described in the background section, the quality of the sorting plan determines the production efficiency of the sorting equipment in the transit center, i.e., the efficiency of sorting parcels. In the existing technology, the formulation of sorting plans for transit centers relies on manual experience, which is insufficient to cope with complex and ever-changing logistics demands. Therefore, how to quickly formulate sorting plans that meet the sorting needs of various transit centers has become an urgent problem to be solved.

[0031] Building upon this foundation, the inventors further discovered that by receiving sorting demand information from the target transfer center input by the user, and based on this sorting demand information and the target sorting data of the target transfer center, an operations optimization process is formulated. The optimization direction of this operations optimization process is consistent with the demand direction represented by the sorting demand information. Subsequently, based on the target sorting data and the operations optimization process, the sorting plan of the target transfer center is simulated and optimized to obtain the target sorting plan. In this way, based on the sorting demand of the target transfer center and combined with the target sorting data of the target transfer center, a reasonable optimization scheme can be formulated, and the optimization direction is consistent with the demand direction represented by the sorting demand information. This is the operations optimization process. Simulation optimization is then performed based on this operations optimization process to determine the target sorting plan that can meet the sorting demand. This eliminates the need for manual formulation of optimization schemes and achieves the effect of quickly determining the sorting plan for each transfer center.

[0032] Based on the above concept, this specification provides a sorting plan formulation method, which will be described exemplarily below with reference to the accompanying drawings.

[0033] Exemplary methods

[0034] Please see Figure 1 In one exemplary embodiment, a sorting plan formulation method is provided, applicable to any electronic device. For example... Figure 1 As shown, the sorting plan formulation method includes steps S101-S103:

[0035] S101: Receives sorting requirements information for the target transfer site input by the user.

[0036] Among them, the sorting demand information of the target transit center refers to the requirements for sorting volume (capacity), unloading volume, return rate, and express delivery flow of the target transit center as a whole or for each sorting equipment within the target transit center.

[0037] The interactive interface provides an intuitive user interface, through which users input language or data tables to submit sorting requests for the target transit center. This eliminates the need for specific commands or interfaces, allowing users to submit sorting requests directly to the target transit center, effectively simplifying the process and improving the user experience.

[0038] Then, the large AI model receives user input such as language or data tables, analyzes the user input, and determines the sorting requirements of the target transit area.

[0039] The sorting requirements information includes at least one sorting requirement, such as reducing the amount of unloading or increasing the capacity (i.e., sorting efficiency) of a single piece of equipment.

[0040] Understandably, the number of transit hubs is enormous, and the equipment, site structure, and other aspects of each transit hub may differ, resulting in different constraints and needs for each hub.

[0041] For example, considering the different constraints of transit hubs, some transit hubs only serve logistics flows towards Beijing, and all goods sorted through these hubs are arranged for transshipment towards Beijing. Some transit hubs can only handle air freight, meaning they only handle the logistics transshipment of air express shipments. Some transit hubs, due to their structural limitations, cannot serve logistics flows in certain directions, and all goods sorted through these hubs cannot be arranged for transshipment in those directions.

[0042] For example, taking the different needs of transit points as an example, some sites need to increase the capacity of a single sorting device, while other sites do not need to increase the capacity of a single sorting device but need to reduce the amount of goods transferred or the return rate, etc.

[0043] In addition, users can directly input language or data tables into the interactive interface to make sorting requests for the target transfer site without needing specific commands or interfaces, which simplifies the input process and improves the user experience.

[0044] S102: Based on sorting demand information and target sorting data of the target transfer site, formulate an operations optimization process.

[0045] Among them, the optimization direction of the operations optimization process is consistent with the demand direction represented by the sorting demand information.

[0046] The AI ​​big data model obtains target sorting data from the target transfer site from the data platform, analyzes the obtained target sorting data based on the received sorting demand information, and formulates an operational optimization process based on the analysis results.

[0047] For example, the sorting demand information includes a sorting demand that is to reduce the amount of goods transferred by 10%. In this case, if the previous amount of goods transferred was A, then the optimization objective of the operations optimization process is that the amount of goods transferred is no higher than B = 90% * A.

[0048] The target sorting data of the target transit site is processed and useful information is extracted, such as hidden patterns and trends. Based on the extracted useful information, a suitable solution is determined, and a suitable pipeline continuous processing flow is formulated using the created operations research optimization algorithm library, that is, the operations research optimization process.

[0049] The operations research optimization algorithm library contains various pre-defined algorithm schemes and function functions that utilize techniques such as operations research and reinforcement learning to optimize sorting plans within transfer stations.

[0050] For example, the sorting demand information includes a sorting demand to reduce the amount of goods transferred by 10%. In this case, the target sorting data is analyzed to determine that the amount of goods transferred decreases as the number of sorting devices activated increases. Based on this pattern, the amount of goods transferred can be reduced by increasing the number of sorting devices activated, and a corresponding operational optimization process can be formulated. The optimization objective of this operational optimization process is that the amount of goods transferred is no higher than B = 90% * A, where A is the amount of goods transferred corresponding to the sorting plan before optimization.

[0051] S103: Based on the target sorting data and the operation optimization process, the sorting plan of the target transfer site is simulated and optimized to obtain the target sorting plan.

[0052] Based on the target sorting data, the corresponding algorithms and functions are called according to the operations optimization process to perform at least one simulation optimization on the sorting plan of the target transfer site, and the sorting plan optimized by simulation is determined as the target sorting plan.

[0053] The sorting plan for the target transit site can be a preset sorting plan or a historical sorting plan.

[0054] Understandably, the sorting plan for the target transfer center is used to instruct the scheduling of various sorting devices within the target transfer center, and determines the sorting process of each sorting device within the target transfer center. The target sorting plan refers to the sorting plan that can meet the sorting requirements in the sorting demand information submitted by the user.

[0055] In this embodiment, based on the sorting requirements of the target transfer station and combined with the target sorting data of the target transfer station, a reasonable optimization scheme can be formulated, and the optimization direction is consistent with the demand direction represented by the sorting demand information. This is the operations optimization process. Based on the operations optimization process, simulation optimization can be performed to determine the target sorting plan that can meet the sorting requirements. This method has strong adaptability and generalization ability. For different transfer stations, different requirements, and different constraints, there is no need to manually formulate optimization schemes. Only production managers need to communicate and interact with the artificial intelligence model to automatically formulate appropriate operations optimization processes, so as to quickly determine the sorting plan for each transfer station.

[0056] Because the sorting needs of a transit point may be numerous, omissions may occur during the user's input process. To avoid omissions, in some embodiments, before formulating an operations optimization process based on the sorting demand information and the target sorting data of the target transit point, it is determined whether any necessary demand items are missing in the sorting demand information. Based on whether any necessary demand items are missing in the sorting demand information, different operations are performed, namely, performing supplementary input or formulating an operations optimization process.

[0057] If so, meaning the required information is missing, supplementary prompts are generated and sent to the user to remind them to add the missing items. These supplementary prompts guide the user to complete the missing information.

[0058] If not, then proceed with the steps of developing an operations optimization process based on sorting demand information and target sorting data from the target transit point.

[0059] For example, necessary requirements include the range of grid cells that need to be optimized, the range of grid cells that cannot be optimized, and the range of packages that cannot be optimized.

[0060] In this context, "grid" primarily refers to the various designated areas or units within a logistics transit center used for storage, sorting, and transfer. The range of grids requiring optimization refers to at least one grid in the transit center that needs improvement, while the range of grids that cannot be optimized refers to at least one grid in the transit center that cannot be optimized. "Package" refers to the parcels sorted within the transit center; the range of packages that cannot be optimized refers to packages in the transit center that cannot be optimized in terms of type, size, etc.

[0061] In this embodiment, after receiving the sorting requirement information from the target transfer station, before formulating the operational optimization process based on the sorting requirement information and the target sorting data of the target transfer station, it is determined whether the received sorting requirement information is missing any necessary requirements. If no necessary information is missing, the step of formulating the operational optimization process based on the sorting requirement information and the target sorting data is executed. If any necessary information is missing, supplementary prompts are generated and fed back to the user. This enhances the interactivity between the system and the user, effectively improves the user experience, and reminds the user to promptly complete the missing necessary information when necessary requirements are missing, thus obtaining complete sorting requirement information, and thereby determining a reasonable operational optimization process based on the sorting requirement information.

[0062] In some embodiments, the target sorting data for the target transit site includes historical sorting data, real-time sorting data, and predictive sorting data.

[0063] Sorting data includes data on the packages being sorted, data on the sorting plan required for production, and data on the rules for setting up packages in the production area. Historical sorting data refers to the sorting data up to the current moment, real-time sorting data refers to the sorting data at the current moment, and predictive sorting data refers to the predicted sorting data after the current moment.

[0064] Target sorting data is used to analyze the current site conditions, formulate operations optimization processes, and optimize and simulate the sorting plan according to these processes. Target sorting data includes at least one of historical sorting data, real-time sorting data, and predictive sorting data.

[0065] Specifically, the predicted sorting data can be obtained by the data platform based on historical sorting data and / or real-time sorting data. For details on the operation of predicting sorting data, please refer to existing technologies, which will not be elaborated here.

[0066] Because the sorting requirements proposed by users—that is, the sorting requirements represented by the sorting requirement information input by users—varie in degree of change compared to previous sorting requirements, different sorting data have different reference significance for formulating operations optimization processes. The smaller the degree of change in the sorting requirements proposed by users compared to previous sorting requirements, that is, the higher the similarity between the sorting requirements proposed by users and previous sorting requirements, the greater the reference significance of historical sorting data and real-time sorting data for formulating current operations optimization processes. Conversely, the greater the degree of change in the sorting requirements proposed by users compared to previous sorting requirements, that is, the lower the similarity between the sorting requirements proposed by users and previous sorting requirements, the less reference significance of historical sorting data and real-time sorting data for formulating current operations optimization processes.

[0067] Therefore, before developing an operations optimization process based on sorting demand information and target sorting data of the target transit point, when obtaining target sorting data, historical, real-time and / or predicted sorting data can be obtained as target sorting data based on the magnitude of changes in sorting demand.

[0068] The degree of change in sorting requirements refers to the extent to which the sorting requirements proposed by users have changed compared to previous sorting requirements.

[0069] Specifically, the degree of change in sorting requirements can be determined by comparing the sorting requirements submitted by users with previous sorting requirements, or by analyzing and understanding the sorting requirements submitted by users.

[0070] For example, if the sorting requirement information proposed by the user includes "the capacity of sorting equipment a reaches 1 piece / s", the degree of change in the sorting requirement can be determined by comparing the sorting requirement with the previous sorting requirement. If the sorting requirement information proposed by the user includes "the sorting efficiency of sorting equipment a increases by 10%", the degree of change in the sorting requirement can be determined directly by analyzing and understanding the sorting requirement information.

[0071] The standard for measuring the degree of change in sorting demand can be a preset degree of change. If the degree of change in sorting demand exceeds the preset degree of change, then the degree of change in sorting demand is large; if it does not exceed the preset degree of change, then the degree of change is small.

[0072] Specifically, target sorting data can be obtained based on whether the degree of change in sorting demand exceeds a preset degree of change.

[0073] If the change in sorting demand exceeds the preset change level, predictive sorting data is obtained as the target sorting data; if not, historical sorting data and real-time sorting data are obtained as the target sorting data.

[0074] More specifically, the preset degree of change can be determined based on actual needs.

[0075] For example, the preset degree of change could be the difference between certain indicators and their previous values, and / or the number of indicators that need improvement.

[0076] In this embodiment, the target sorting data for the target transfer site includes historical, real-time, and / or predicted sorting data. Based on the degree of change in sorting demand represented by the sorting demand information, when the degree of change is large and exceeds a preset degree of change, predicted sorting data is obtained as the target sorting data. When the degree of change is small and does not exceed the preset degree of change, historical sorting data and historical sorting data are obtained as the target sorting data. In this way, based on the degree of change in sorting demand relative to previous sorting demand, sorting data with greater reference significance can be selected as the target sorting data. At this time, based on the corresponding target sorting data, a more reasonable operational optimization process can be better formulated.

[0077] In some embodiments, when formulating an operations optimization process based on sorting demand information and target sorting data of the target transit site, the sorting demand information is analyzed to generate demand constraints. Then, the operations optimization process is formulated based on the demand constraints and the target sorting data.

[0078] Specifically, the sorting demand information is analyzed to obtain the sorting demand represented by the sorting demand information, and demand constraints are generated based on the sorting demand.

[0079] Among them, the sorting requirements represented by the sorting requirements information can correspond one-to-one with the requirements constraints, that is, a requirements constraint is generated based on each sorting requirement.

[0080] Understandably, in addition to the user-specified constraints, each transit point may have certain limitations due to its structure, geographical location, etc., and must adhere to certain basic constraints. When formulating a sorting plan for a transit point, these basic constraints must be considered to avoid situations where the final optimized sorting plan for the target transit point does not meet these constraints. The target sorting data from the target transit point is processed to extract useful information, such as hidden patterns and trends. Based on this extracted information, a suitable solution is determined, and a suitable pipeline continuous processing flow is formulated using a pre-built operations research optimization algorithm library—that is, the operations research optimization process.

[0081] Specifically, an operations optimization process is developed based on the basic constraints, demand constraints, and target sorting data of the target transfer site.

[0082] More specifically, based on the basic constraints, demand constraints, and target sorting data of the target transfer site, a target optimization algorithm and a target function are selected from the operations research optimization algorithm library. Then, based on the target optimization algorithm and the target function, an operations research optimization process is formulated.

[0083] Based on the basic and demand constraints of the target transfer area, the target sorting data is analyzed to determine the hidden trends in the target sorting data that can satisfy the basic and demand constraints, to determine a reasonable solution, and based on the solution, a target optimization algorithm and target function are selected from the operations research optimization algorithm library, and an operations research optimization process is formulated based on the target optimization algorithm and target function.

[0084] In this embodiment, the sorting requirement information input by the user is analyzed to generate requirement constraints. Based on the basic constraints of the target transfer center, the requirement constraints, and the target sorting data, a target optimization algorithm and a target function that meet the basic constraints and requirement constraints of the transfer center are selected. Based on the target optimization algorithm and the target function, a reasonable operations optimization process is formulated to obtain a reasonable and requirement-compliant target sorting plan.

[0085] In some embodiments, based on target sorting data and operations optimization process, the sorting plan of the target transfer center is simulated and optimized to obtain the target sorting plan. Then, a digital twin model of the target transfer center is obtained, and based on the sorting data, operations optimization process and digital twin model of the target transfer center, the sorting plan of the target transfer center is simulated and optimized to obtain the target sorting plan.

[0086] The digital twin model of the target transfer center refers to a digital twin model that replicates the equipment within the target transfer center in a 1:1 scale in the virtual world using digital twin technology, creating a multi-equipment coordinated scheduling model of the target transfer center. Based on the structure of the target transfer center, this digital twin model includes models of the unloading port, the swing wheel sorting machine, the small-item sorting machine, the sorting cabinet, the loading port, and various staff members. The completed digital twin model can completely reproduce all operations within the transfer center, including the operating process of each piece of equipment, the complete internal control system logic of the equipment, and the operational processes of the personnel.

[0087] Based on the target sorting data and the operational optimization process, the sorting plan of the target transfer center is optimized to obtain the initial sorting plan. Based on the digital twin model of the target transfer center, the initial sorting plan is simulated to obtain the sorting result of the initial sorting plan.

[0088] Specifically, when simulating the initial sorting plan based on the digital twin model of the target transfer site, the sorting is simulated in the digital twin model of the target transfer site according to the initial sorting plan to obtain the sorting results.

[0089] The sorting results include records of each package (each express delivery) passing through various nodes within the target transit area, the sorting slots (where it falls into the sorting machine), and the bundling results. Bundling refers to bundling multiple small packages into one large package.

[0090] Then, based on the sorting results and the initial sorting plan, the target sorting plan is determined.

[0091] Understandably, if the initial sorting plan is successfully executed in the digital twin model of the target transfer site, a sorting result will be obtained; otherwise, no sorting result will be obtained, or the sorting result will be empty.

[0092] If sorting results cannot be obtained, the initial sorting plan will continue to be optimized based on the operations optimization process; if sorting results are obtained, the operation of determining the target sorting plan based on the sorting results and the initial sorting plan will be executed.

[0093] If sorting results cannot be obtained after multiple attempts, the operational optimization process can be revised and subsequent operations executed.

[0094] In this embodiment, based on the target sorting data, the sorting plan is optimized according to the operations optimization process to obtain an initial sorting plan. Then, the initial sorting plan is simulated using a digital twin model of the target transfer site to obtain the sorting results. This can greatly improve the simulation accuracy and reduce the risk. Since the site model built using digital twin technology, i.e., the digital twin model of the target transfer site, has a high degree of simulation realism, the initial sorting plan, after being verified in the simulation environment, can ensure that the final target sorting plan can be directly applied to the site of the target transfer site, reducing the possibility of major accidents.

[0095] In some embodiments, when determining the target sorting plan based on the sorting results and the initial sorting plan, the sorting results are first evaluated to obtain the evaluation results, and then the target sorting plan is determined based on the evaluation results and the initial sorting plan.

[0096] The evaluation results include indicators such as staff scheduling and staff efficiency for each position, equipment capacity, sorting machine return rate, and unloading volume.

[0097] Specifically, when determining the target sorting plan based on the evaluation results and the initial sorting plan, the initial sorting plan is first judged based on the evaluation results to determine whether it meets the sorting requirements in the sorting requirements information. Then, the target sorting plan is determined based on whether the initial sorting plan meets the sorting requirements in the sorting requirements information.

[0098] If yes, meaning the initial sorting plan meets the sorting requirements in the sorting requirements information, then the initial sorting plan is determined as the target sorting plan; if no, meaning the initial sorting plan does not meet the sorting requirements in the sorting requirements information, then the initial sorting plan is optimized until an initial sorting plan that meets all sorting requirements is obtained, and this initial sorting plan that meets all sorting requirements is determined as the target sorting plan.

[0099] In other words, if the initial sorting plan does not meet the sorting requirements in the sorting requirements information, then the internal iteration continues to optimize the initial sorting plan. If the initial sorting plan meets the sorting requirements in the sorting requirements information, then the internal iteration stops and the initial sorting plan is taken as the target sorting plan.

[0100] If the initial sorting plan fails to meet the sorting requirements in the sorting requirements information multiple times in a row, the operations optimization process is re-formulated and subsequent steps are executed until the initial sorting plan meets the sorting requirements in the sorting requirements information, and the initial sorting plan is determined to be the target sorting plan.

[0101] In this embodiment, the sorting results are evaluated to obtain an evaluation result. Based on this evaluation result, it is determined whether the initial sorting plan meets the sorting requirements in the sorting demand information. If the sorting requirements are met, the initial sorting plan is directly determined as the target sorting plan. If the sorting requirements are not met, the initial sorting plan is further optimized to obtain a new initial sorting plan, until the new initial sorting plan can meet the sorting requirements. The initial sorting plan that meets the sorting requirements is then determined as the target sorting plan. In this way, it can be guaranteed that the final target sorting plan can meet all the sorting requirements proposed by the user.

[0102] In some embodiments, after a target sorting plan is determined, the sorting results and evaluation results of the target sorting plan are displayed.

[0103] Specifically, using digital twin technology to display 3D interfaces in real time can enhance user immersion and experience.

[0104] In this way, by displaying the sorting results and evaluation results of the target sorting plan, the target sorting plan can be fed back to the user so that the user can understand the target sorting plan. Based on the sorting results and evaluation results of the target sorting plan, the user can determine whether the target sorting plan is available, whether to use the target sorting plan, and whether to continue to supplement the requirements, thereby enhancing the user experience and further ensuring that the final target sorting plan meets the user's needs.

[0105] For example, the sorting plan development process can be as follows: Figure 2As shown, the user initiates a request, inputting their own needs (i.e., sorting requirements for the target transfer center), such as the transfer center (i.e., the target transfer center), shifts, times, and sorting equipment the user wants to optimize, and providing constraints, such as which can be modified, which cannot be modified, and which need to be grouped together. Then, based on the user's request, an AI-powered large-scale model is used to understand the needs and constraints, and to determine if any conditions (i.e., necessary requirements) are missing. If so, the user is asked to supplement the requirements; otherwise, relevant data (i.e., target sorting data) is retrieved and analyzed, including historical sorting data, real-time sorting data, and / or predicted sorting data. The sorting data includes sorting plan data and generated data. Based on the operations research optimization algorithm library, an appropriate module (i.e., the target optimization algorithm and target function) is selected to formulate an operations research optimization process. Based on this process, the sorting plan for the target transfer center is optimized to obtain an initial sorting plan. Based on the initial sorting plan, the pre-built digital twin model of the target transfer area is invoked for simulation to obtain sorting results. The evaluation module is then invoked to analyze the sorting results and determine whether internal iteration is needed for optimization or whether the results should be output to the user. If internal iteration is required, the simulation is re-optimized based on the operations research optimization process. Otherwise, the initial sorting plan is used as the target sorting plan, and the sorting and evaluation results are fed back to the user. The user then decides whether to use the target sorting plan or to supplement requirements (through user interaction). If supplementation is needed, the target sorting plan is re-formulated according to the above process.

[0106] Exemplary System

[0107] This application also provides a sorting plan formulation system, which includes an artificial intelligence module.

[0108] The artificial intelligence module receives sorting requirements from the target transfer center input by the user, and formulates an operational optimization process based on the target sorting data from the target transfer center and the sorting requirements input by the user. The optimization direction of the operational optimization process is consistent with the demand direction represented by the sorting requirements information.

[0109] The artificial intelligence module is also used to simulate and optimize the sorting plan of the target transfer site based on sorting data and the aforementioned operational optimization process, so as to obtain the target sorting plan.

[0110] The artificial intelligence module includes a large-scale artificial intelligence model, which is used to formulate operational optimization processes based on sorting data from the transfer station and sorting requirements input by users.

[0111] The sorting planning system also includes an interactive terminal, which receives sorting demand information from the target transit area input by the user and forwards the sorting demand information to the artificial intelligence module.

[0112] The interactive interface provides an intuitive user interface, allowing users to express their needs by inputting language and data tables, inputting sorting requirements, and receiving feedback, such as supplementary prompts or optimized target sorting plans.

[0113] The artificial intelligence module is also used to determine whether any necessary items are missing from the sorting demand information. If so, it generates supplementary prompts and sends them back to the user. If not, it executes steps to optimize the sorting process based on the sorting demand information and the target sorting data of the target transit point.

[0114] The interactive interface is also used to receive and display supplementary prompts to provide feedback to the user.

[0115] The sorting planning system also includes a data platform, which is used to store and send the target sorting data of the target transit site to the demand side, namely the artificial intelligence module and the interaction terminal.

[0116] The interactive terminal is also used to request target sorting data from the target transit area from the data platform, including historical, real-time and / or predictive sorting data.

[0117] The data platform is specifically used to respond to requests from the interactive end and distribute sorting data, including corresponding sorting results, such as return rate, capacity and other indicators and related data.

[0118] The interactive terminal is also used to receive and display the sorting results corresponding to the sorting data sent by the data platform, so that users can understand and supplement the needs of the target transit site.

[0119] The artificial intelligence module is specifically used to use a large artificial intelligence model to determine whether the sorting demand information is missing any necessary requirements, and based on whether the sorting demand information is missing any necessary requirements, to determine whether to execute the steps of formulating an operational optimization process.

[0120] If the sorting requirement information is complete and does not lack any necessary requirements, then the steps for developing an operational optimization process will be executed; if the sorting requirement information is missing any necessary requirements, then the steps for developing an operational optimization process will not be executed, supplementary prompts will be generated, and the supplementary prompts will be fed back to the user.

[0121] The supplementary prompts can be at least one question. In this way, the AI ​​model can obtain the necessary requirements missing from the user's supplementary input of sorting requirements by asking questions.

[0122] The interactive interface is specifically used to receive and display supplementary prompts to provide feedback to the user.

[0123] The interactive terminal is also used to receive the sorting requirement information supplemented by the user in response to the supplementary prompts, and to send the supplementary sorting prompts to the artificial intelligence module.

[0124] The artificial intelligence module is also used to use a large artificial intelligence model to determine the degree of change in sorting requirements represented by all sorting requirement information (input by the user). Then, the artificial intelligence module is also used to determine the required target sorting data based on the degree of change and to acquire the target sorting data.

[0125] If the degree of change in sorting demand represented by the sorting demand information exceeds the preset degree of change, then the obtained predicted sorting data will be determined as the target sorting data, and a request will be made to the data platform to obtain the predicted sorting data.

[0126] If the degree of change in sorting demand represented by the sorting demand information does not exceed the preset degree of change, then the obtained historical and real-time sorting data will be used as the target sorting data, and a request will be made to the data platform to obtain the historical and real-time sorting data.

[0127] The data platform is also used to respond to requests from the artificial intelligence module by distributing predictive sorting data or historical and real-time sorting data.

[0128] The sorting planning system also includes a library of operations research optimization algorithms.

[0129] The artificial intelligence module is specifically used to analyze sorting demand information using a large artificial intelligence model, generate demand constraints, select target optimization algorithms and target function functions from the operations research optimization algorithm library based on the basic constraints of the target transfer site, demand constraints and target sorting data, and formulate operations research optimization process based on the target optimization algorithm and target function functions.

[0130] The sorting planning system also includes a digital twin model module.

[0131] The artificial intelligence module is specifically used to optimize the sorting plan of the target transfer area based on target sorting data and operations optimization processes. It calls target optimization algorithms and target function functions from the operations optimization algorithm library to obtain an initial sorting plan. Then, it calls the digital twin model of the target transfer area from the digital twin model module to perform sorting according to the initial sorting plan, obtaining the sorting results. Finally, based on the sorting results and the initial sorting plan, the target sorting plan is determined.

[0132] The sorting planning system also includes an evaluation module, which is used to evaluate the sorting results after the simulation ends and obtain the evaluation results.

[0133] The evaluation module is specifically used to analyze various data in the sorting results after simulation and determine the evaluation result of the sorting results.

[0134] The artificial intelligence module is also used to determine, based on the evaluation results, whether the initial sorting plan meets the sorting requirements in the sorting requirements information. If so, the initial sorting plan is determined as the target sorting plan. If not, the initial sorting plan is optimized until a sorting plan that meets the sorting requirements is obtained, and the sorting plan that meets the sorting requirements is determined as the target sorting plan.

[0135] The artificial intelligence module is also used to feed back the sorting results and evaluation results of the target sorting plan to the interactive terminal after the target sorting plan is determined.

[0136] The interactive terminal is also used to receive and display the sorting results and evaluation results of the target sorting plan.

[0137] In other words, after the evaluation module determines the evaluation result, the artificial intelligence module is also used to analyze the evaluation result and, based on the analysis, to choose whether internal iteration is needed. It can also feed back the evaluation result to the user, so that the user can judge whether the target sorting plan meets the requirements based on the evaluation result and the target sorting plan. If the requirements are not met, the user can re-initiate the request or continue to increase the requirements and re-initiate the request.

[0138] In summary, the artificial intelligence module can utilize large-scale model technology to replicate and process user interactions, converting natural language into machine language and analyzing and processing user-input sorting requirements. It can also process large amounts of data and extract useful information, such as hidden patterns and trends in target sorting data, finding suitable solutions, and using a pre-built operations research optimization algorithm library to formulate appropriate operations research optimization processes and optimize sorting plans.

[0139] In other words, the training of this large-scale AI model focuses on understanding the user's needs for the target transfer center in the transfer center scenario, understanding the sorting constraints of the target transfer center, and finding a suitable process solution based on the constraints. This large-scale AI model and its associated AI module do not require building simulation scenarios or creating optimization code; they only need to call the digital twin model and the algorithms and functional modules corresponding to the selected operations research optimization process.

[0140] For example, such as Figure 3 As shown, the sorting plan formulation system includes an interactive terminal, a data platform, an artificial intelligence module, an operations research optimization algorithm library, a digital twin model module, and an evaluation module.

[0141] In this process, users input sorting requirements into the interactive terminal, providing their needs and constraints. The interactive terminal receives these requirements and constraints, uses the large-scale artificial intelligence model in the artificial intelligence module to understand the requirements, and asks for supplementary information through the interactive terminal when necessary requirements are missing. The interactive terminal then provides feedback to the user, prompting the user to respond to the question and input additional sorting requirements and constraints.

[0142] Before a user inputs sorting requirements into the interactive terminal, the terminal requests data from the data platform. The data platform returns results to the terminal, including sorting data and corresponding evaluation results. Afterward, the user can input sorting requirements based on the feedback such as the sorting results received from the sorting plan on the interactive terminal, or supplement the input of sorting requirements based on the feedback and supplementary prompts when the artificial intelligence module asks questions.

[0143] The AI ​​module uses complete sorting requirements input by the user through the interactive interface, combined with data obtained from the data platform—namely, sorting data. Based on a large-scale AI model, the module analyzes the acquired sorting data, requirements, and constraints, and, using an operations research and optimization algorithm library, formulates an operations research and optimization process.

[0144] Then, the artificial intelligence module calls the optimization algorithm module and related function of the corresponding operations optimization process in the operations optimization algorithm library to optimize the sorting plan, output the decision, that is, output the initial sorting plan, and initiate simulation based on the initial sorting plan. By calling the digital twin model, the initial sorting plan is executed in the digital twin model to obtain the sorting result.

[0145] The evaluation module receives the sorting results from the simulation, evaluates these results, obtains an evaluation result, and sends the evaluation result to the artificial intelligence module. The artificial intelligence module receives the evaluation result and, based on the evaluation result and the optimized initial sorting plan, determines the target sorting plan. Subsequently, the artificial intelligence module sends the sorting results and evaluation results, etc., to the data platform. The data platform receives and stores these results to provide a reference for users to input sorting requirements later.

[0146] Exemplary device

[0147] like Figure 4 As shown in the figure, this application embodiment also provides a sorting plan formulation device, including a receiving module 401, a determining module 402, and a simulation optimization module 403.

[0148] in,

[0149] The receiving module 401 is used to receive the sorting requirement information of the target transfer site input by the user;

[0150] The determination module 402 is used to formulate an operation optimization process based on the sorting demand information and the target sorting data of the target transfer site, wherein the optimization direction of the operation optimization process is consistent with the demand direction represented by the sorting demand information.

[0151] The simulation optimization module 403 is used to perform simulation optimization on the sorting plan of the target transfer station based on the target sorting data and the operation optimization process, so as to obtain the target sorting plan.

[0152] The sorting plan formulation device provided in this embodiment belongs to the same concept as the sorting plan formulation method provided in the above embodiments of this application. It can execute the method provided in any of the above embodiments of this application and has the corresponding functional modules and beneficial effects of the method. Technical details not described in detail in this embodiment can be found in the specific processing content of the sorting plan formulation method provided in the above embodiments of this application, and will not be repeated here.

[0153] The functions implemented by the receiving module 401, determining module 402 and simulation optimization module 403 can be implemented by the same or different processors calling software, and this application embodiment does not limit this.

[0154] Exemplary electronic devices

[0155] Another embodiment of this application also provides an electronic device, see [link to relevant documentation] Figure 5 As shown, the electronic device includes a memory 500 and a processor 510.

[0156] The memory 500 is connected to the processor 510 and is used to store programs;

[0157] The processor 510 is configured to implement the sorting plan formulation method disclosed in any of the above embodiments by running the program stored in the memory 500.

[0158] Specifically, the electronic device may also include: a bus, a communication interface 520, an input device 530, and an output device 540.

[0159] The processor 510, memory 500, communication interface 520, input device 530, and output device 540 are interconnected via a bus. Among them:

[0160] A bus can include a pathway for transmitting information between various components of a computer system.

[0161] The processor 510 can be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, etc., or an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of the program of the present application. It can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0162] The processor 510 may include a main processor, as well as a baseband chip, modem, etc.

[0163] The memory 500 stores a program for executing the technical solution of this application, and may also store an operating system and other critical business functions. Specifically, the program may include program code, which includes computer operation instructions. More specifically, the memory 500 may include read-only memory (ROM), other types of static storage devices capable of storing static information and instructions, random access memory (RAM), other types of dynamic storage devices capable of storing information and instructions, disk storage, flash memory, etc.

[0164] Input device 530 may include a device for receiving user input data and information, such as a keyboard, mouse, camera, scanner, light pen, voice input device, touch screen, pedometer, or gravity sensor.

[0165] Output device 540 may include devices that allow information to be output to a user, such as a display screen, printer, speaker, etc.

[0166] The communication interface 520 may include a device that uses any transceiver to communicate with other devices or communication networks, such as Ethernet, Radio Access Network (RAN), Wireless Local Area Network (WLAN), etc.

[0167] The processor 510 executes the program stored in the memory 500 and calls other devices, and can be used to implement any of the steps of the sorting plan formulation method provided in the above embodiments of this application.

[0168] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0169] This application also proposes a chip including a processor and a data interface. The processor reads and runs a program stored in a memory through the data interface to execute the sorting plan formulation method described in any of the above embodiments. For the specific processing procedure and its beneficial effects, please refer to the embodiments of the sorting plan formulation method described above.

[0170] In addition to the methods and apparatus described above, embodiments of this application provide a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the sorting plan formulation methods according to various embodiments of this application as described in the "Exemplary Methods" section of this specification.

[0171] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0172] Furthermore, embodiments of this application also propose a storage medium storing a computer program that is executed by a processor in the sorting plan formulation method according to various embodiments of this application as described in the "Exemplary Methods" section above.

[0173] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.

[0174] The block diagrams of devices, apparatuses, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0175] It should also be noted that in the apparatus, device, and method of the present invention, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of the present invention.

[0176] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the invention. Therefore, the invention is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0177] It should be understood that the qualifying terms "first", "second", "third", "fourth", "fifth" and "sixth" used in the description of the embodiments of the present invention are only used to more clearly illustrate the technical solutions and are not intended to limit the scope of protection of the present invention.

[0178] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A sorting plan formulation method, characterized in that, The sorting plan formulation method includes: Receive sorting requirements information for the target transit area input by the user; Based on the sorting demand information and the target sorting data of the target transit site, an operations optimization process is formulated, wherein the optimization direction of the operations optimization process is consistent with the demand direction represented by the sorting demand information. Based on the target sorting data and the operations optimization process, the sorting plan of the target transfer station is simulated and optimized to obtain the target sorting plan.

2. The sorting plan formulation method according to claim 1, characterized in that, Before formulating the operations optimization process based on the sorting demand information and the target sorting data of the target transit site, the method further includes: Determine whether any necessary requirements are missing from the sorting requirement information; If so, then generate supplementary prompt information and send the supplementary prompt information back to the user; If not, then the steps of formulating an operations optimization process based on the sorting demand information and the target sorting data of the target transit site are executed.

3. The sorting plan formulation method according to claim 1, characterized in that, The target sorting data of the target transit center includes historical sorting data, real-time sorting data, and / or predicted sorting data; before formulating the operations optimization process based on the sorting demand information and the target sorting data of the target transit center, the method further includes: If the degree of change in sorting demand represented by the sorting demand information exceeds a preset degree of change, the predicted sorting data is obtained as the target sorting data; If the change in sorting demand does not exceed a preset change level, the historical sorting data and the real-time sorting data are obtained as the target sorting data.

4. The sorting plan formulation method according to claim 1, characterized in that, The process of optimizing operations based on the sorting demand information and the target sorting data of the target transit point includes: The sorting demand information is analyzed to generate demand constraints; Based on the basic constraints of the target transfer center, the demand constraints, and the target sorting data, a target optimization algorithm and a target function are selected. Based on the target optimization algorithm and the target function, the operations research optimization process is formulated.

5. The sorting plan formulation method according to claim 1, characterized in that, The step of simulating and optimizing the sorting plan of the target transit area based on the target sorting data and the operations optimization process to obtain the target sorting plan includes: Based on the target sorting data and the operations optimization process, the sorting plan of the target transit site is optimized to obtain an initial sorting plan; Based on the digital twin model of the target transit site, sorting is carried out according to the initial sorting plan to obtain sorting results; Based on the sorting results and the initial sorting plan, the target sorting plan is determined.

6. The sorting plan formulation method according to claim 5, characterized in that, The step of determining the target sorting plan based on the sorting results and the initial sorting plan includes: The sorting results are evaluated to obtain evaluation results; Based on the evaluation results, it is determined whether the initial sorting plan meets the sorting requirements in the sorting requirements information; If so, the initial sorting plan shall be determined as the target sorting plan; If not, the initial sorting plan is optimized until a sorting plan that meets the sorting requirements is obtained, and the sorting plan that meets the sorting requirements is determined as the target sorting plan.

7. The sorting plan formulation method according to claim 5, characterized in that, After determining the target sorting plan, the method further includes: The sorting results and evaluation results of the target sorting plan are displayed.

8. A sorting planning system, characterized in that, The sorting plan formulation system includes an artificial intelligence module; wherein... The artificial intelligence module is used to receive sorting demand information of the target transfer site input by the user, and formulate an operation optimization process based on the sorting demand information and the target sorting data of the target transfer site. The optimization direction of the operation optimization process is consistent with the demand direction represented by the sorting demand information. The artificial intelligence module is used to simulate and optimize the sorting plan of the target transfer station based on the target sorting data and the operation optimization process, so as to obtain the target sorting plan.

9. An electronic device, characterized in that, Including memory and processor; The memory is connected to the processor and is used to store programs; The processor is used to implement the sorting plan formulation method as described in any one of claims 1 to 7 by running a program in the memory.

10. A storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the sorting plan formulation method as described in any one of claims 1 to 7.

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