Warehouse management method and device based on digital twinning and electronic equipment

Abstract

The invention relates to the technical field of warehousing, in particular to a digital twinning-based warehousing management method and device and electronic equipment, which are used for simulating the operation condition of a warehouse management system in real time based on a digital twinning technology, acquiring real-time operation information of the warehouse management system through long connection and synchronizing the real-time operation information to a digital twinning model. Performing configuration parameter adjustment on the digital twin model through a virtual interface on the basis of the obtained actual operation efficiency evaluation result, and comparing the generated predicted operation efficiency evaluation result with the actual operation efficiency evaluation result to determine a target operation parameter; in addition, the virtual interface focuses on configuration parameter adjustment and does not intervene in real-time operation information synchronization, the independence and reliability of configuration parameter adjustment are ensured, and the configuration parameter adjustment is based on an actual operation efficiency evaluation result and has reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of warehousing, and in particular, to a warehousing management method, device and electronic device based on digital twin. Background Art

[0002] Currently, automated and unmanned operation of stereoscopic warehouses is being gradually realized, but it is difficult for decision-makers and managers to achieve real-time remote monitoring of warehouse operations.

[0003] To address this technical problem, the prior art improves efficiency through specific algorithms for inbound task allocation, but does not obtain warehouse operation data in real time, and cannot truly simulate the inbound and outbound operation conditions in a real warehouse and fully reflect the actual operation status.

[0004] In reality, warehouse scheduling and warehouse operations are diverse and complex. Scheduling algorithms may be based on specific assumptions and models, which may limit their adaptability in diverse and dynamically changing warehouse environments, resulting in the simulation scheduling not being the real actual scheduling situation. Scheduling through specific algorithms cannot meet the conditions of all warehouses; in addition, there may be a lack of effective anomaly detection and handling mechanisms in the existing simulation scheduling process, resulting in slow response when problems occur due to abnormal states and abnormal situations of equipment not being considered in the scheduling algorithm. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a warehousing management method, device and electronic device based on digital twin.

[0006] In a first aspect, an embodiment of the present invention provides a warehousing management method based on digital twin. This method is applied to a warehouse management system, which includes a digital twin platform and a warehouse monitoring and management platform. The digital twin platform is communicatively connected to the warehouse monitoring and management platform. The method includes:

[0007] Construct a digital twin model corresponding to the warehouse on the digital twin platform;

[0008] Obtain the actual operation efficiency evaluation result generated by the digital twin model based on real-time operation information;

[0009] Based on the target improvement index, adjust the configuration parameters of the digital twin model through a virtual interface;

[0010] Obtain the predicted operation efficiency evaluation result of the digital twin model running based on the adjusted configuration parameters;

[0011] Determine the target operation parameters based on the comparison relationship between the actual operation efficiency evaluation result and the predicted operation efficiency evaluation result;

[0012] Send the target operating parameters to the warehouse monitoring and management platform to adjust the operating parameters of the warehouse system.

[0013] Combined with the first aspect, the steps of constructing and initializing the digital twin model corresponding to the warehouse include:

[0014] Load the initial digital twin model on the digital twin modeling platform;

[0015] Obtain the vertical warehouse information in the warehouse monitoring and management platform through a preset API interface;

[0016] Take the vertical warehouse information as the initial configuration parameters to initialize the initial digital twin model;

[0017] Obtain the real-time operating information of the warehouse based on a long connection to update the initial digital twin model in real time and obtain the digital twin model corresponding to the warehouse.

[0018] Combined with the first aspect, the steps of mapping the real-time operating information of the warehouse to the digital twin-based warehousing management platform based on the digital twin model and the obtained real-time operating information include:

[0019] Establish a communication connection between the digital twin model and the warehouse management system;

[0020] Obtain the real-time operating information through a long connection and synchronously update the digital twin model with the real-time operating information; the real-time operating information at least includes: stacker task information, AGV action information, RGV action information, conveyor belt action information.

[0021] Combined with the first aspect, the operation efficiency evaluation result includes the comprehensive efficiency of the conveyor belt;

[0022] The steps of obtaining the actual operation efficiency evaluation result generated by the digital twin model based on the real-time operating information include:

[0023] Obtain the first task order number in the outbound area, the second task order number in the inbound area, the inbound conveyor speed, the working time period, and the working quantity;

[0024] Calculate the product of the number of goods units processed per hour by the conveyor belt and the working duration to obtain the processing capacity of a single conveyor belt;

[0025] Calculate the ratio of the actual number of completed tasks to the number of transfer tasks to obtain the task completion rate;

[0026] Calculate the comprehensive efficiency of the conveyor belt based on the task completion rate, the processing capacity of a single conveyor belt, and the number of conveyor belts.

[0027] Combined with the first aspect, the operation efficiency evaluation result also includes the comprehensive utilization rate of the RGV;

[0028] The steps of obtaining the actual operation efficiency evaluation result generated by the digital twin model based on real-time operation information further include:

[0029] Obtain the speed value of the RGV, the track perimeter of the RGV track area, and the number of RGVs;

[0030] Calculate the ratio of the track perimeter to the speed value, and calculate the single-cycle time of the RGV;

[0031] Based on the single-cycle time, calculate the number of cycles within a preset duration;

[0032] Calculate the ratio of the number of cycles to the number of RGVs, and calculate the total number of transports within a preset duration;

[0033] Calculate the ratio of the actual number of transports to the total number of transports to obtain the utilization rate of the RGV;

[0034] Calculate the ratio of the actual number of tasks completed to the total number of tasks to obtain the task completion rate;

[0035] Based on the task completion rate and the utilization rate of the RGV, calculate the comprehensive utilization rate of the RGV.

[0036] Combined with the first aspect, the operation efficiency evaluation result further includes the working efficiency of the stacker crane;

[0037] The steps of obtaining the actual operation efficiency evaluation result generated by the digital twin model based on real-time operation information further include:

[0038] Obtain the running speed of the stacker crane in the warehouse area, the shelf numbers, layer numbers, storage location numbers of the inbound and outbound goods, and the shelf dimensions;

[0039] Obtain the running speeds of the stacker crane in the horizontal and vertical directions;

[0040] Based on the layer number, height, storage location number, and the width of each shelf layer and the picking time, calculate the time taken for the stacker crane to complete one inbound and outbound operation;

[0041] Calculate the ratio of the working duration to the time taken for the stacker crane to complete one inbound and outbound operation, and calculate the number of operations and the total operation distance of the stacker crane within a preset duration;

[0042] Based on the number of operations and the working duration, calculate the operation efficiency in terms of the number of operations, and based on the total operation distance and the operation duration, calculate the operation distance efficiency.

[0043] Combined with the first aspect, the method further includes:

[0044] In response to a target device operation information query instruction, obtain the unique identifier of the target device;

[0045] Based on the unique identifier, retrieve the current operation information corresponding to the unique identifier in the real-time operation information;

[0046] Feed the current running information back to the warehouse monitoring and management platform through the API interface.

[0047] In a second aspect, the present application provides a warehousing management device based on digital twin. The device is applied to a warehouse management system, which includes a digital twin platform and a warehouse monitoring and management platform. The digital twin platform is communicatively connected to the warehouse monitoring and management platform. The device includes:

[0048] A construction module for constructing a digital twin model corresponding to the warehouse on the digital twin platform;

[0049] A first acquisition module for acquiring an actual operation efficiency evaluation result generated by the digital twin model based on real-time operation information;

[0050] An adjustment module for adjusting the configuration parameters of the digital twin model through a virtual interface based on the target improvement index;

[0051] A second acquisition module for acquiring a predicted operation efficiency evaluation result of the digital twin model running based on the adjusted configuration parameters;

[0052] A determination module for determining the target operation parameters based on the comparison relationship between the actual operation efficiency evaluation result and the predicted operation efficiency evaluation result;

[0053] A feedback module for sending the target operation parameters to the warehouse monitoring and management platform.

[0054] In a third aspect, the present application provides an electronic device, which includes a memory and a processor. The memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above method.

[0055] In a fourth aspect, the present application provides a readable storage medium, in which computer program instructions are stored. When the computer program instructions are read and run by a processor, the above method is executed.

[0056] The embodiments of the present invention bring the following beneficial effects: A warehouse management method based on digital twin provided by the present application is applied to a warehouse management system, which includes a digital twin platform and a warehouse monitoring and management platform, and the digital twin platform is communicatively connected to the warehouse monitoring and management platform; the method includes: constructing a digital twin model corresponding to the warehouse on the digital twin platform; obtaining an actual operation efficiency evaluation result generated by the digital twin model based on real-time operation information; adjusting the configuration parameters of the digital twin model through a virtual interface based on a target improvement index; obtaining a predicted operation efficiency evaluation result of the digital twin model running based on the adjusted configuration parameters; determining target operation parameters based on the comparison relationship between the actual operation efficiency evaluation result and the predicted operation efficiency evaluation result; and sending the target operation parameters to the warehouse monitoring and management platform.

[0057] The warehouse management method based on digital twin provided by the present application realistically simulates the operation of the warehouse management system based on digital twin technology, obtains the real-time operation information of the warehouse management system through a long connection and synchronizes it to the digital twin model to generate an actual operation efficiency evaluation result. Then, based on the obtained actual operation efficiency evaluation result, the configuration parameters of the digital twin model are adjusted through a virtual interface, and the generated predicted operation efficiency evaluation result is compared with the actual operation efficiency evaluation result to determine the target operation parameters, so as to optimize the operation parameters of the warehouse management system, which is beneficial to improving the operation efficiency. In addition, the virtual interface focuses on adjusting the configuration parameters and does not interfere with the synchronization of real-time operation information, ensuring the independence and reliability of the configuration parameter adjustment. Moreover, the configuration parameter adjustment is based on the actual operation efficiency evaluation result, which has reliability.

[0058] Other features and advantages of the present invention will be described in the following specification, and part of them will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the specification, claims and drawings.

[0059] In order to make the above objectives, features and advantages of the present invention more obvious and understandable, the following specific preferred embodiments are given, and detailed descriptions are made in conjunction with the accompanying drawings as follows. Description of the Drawings

[0060] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative efforts.

[0061] Figure 1This is a schematic flowchart of a warehouse management method based on digital twin provided by an embodiment of the present invention;

[0062] Figure 2 This is a schematic information flow diagram of a warehouse management method based on digital twin provided by an embodiment of the present invention;

[0063] Figure 3 This is a schematic structural diagram of an electronic device provided by an embodiment of the present invention.

[0064] Reference numerals:

[0065] 10 - Construction module, 20 - First acquisition module, 30 - Adjustment module, 40 - Second acquisition module, 50 - Determination module, 60 - Feedback module;

[0066] 130 - Processor, 131 - Memory, 132 - Bus, 133 - Communication interface. Detailed implementation manners

[0067] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0068] To facilitate the understanding of this embodiment, the technical terms designed in this application will be briefly introduced below.

[0069] WMS (Warehouse Management System) is a software system used to manage and optimize warehouse operations. It can help enterprises manage inventory, order processing, goods sorting, transportation scheduling, and other logistics activities.

[0070] RGV is an automated transportation device that can move on a fixed track and is usually used in high-density storage environments. AGV is a vehicle that can autonomously navigate without direct human intervention and is suitable for various types of material handling tasks. In some advanced logistics centers or warehouses, RGV and AGV will cooperate together to improve the overall operation efficiency. RGV is responsible for storing and retrieving goods on high-level shelves, while AGV undertakes the task of transporting goods between the ground and RGV.

[0071] After introducing the technical terms involved in this application, next, the application scenarios and design concepts of the embodiments of this application will be briefly introduced.

[0072] The prior art is a scheduling simulation and optimization method based on a digital twin stereoscopic warehouse: obtaining an initial scheduling task coding cluster according to the number of workshop scheduling tasks and warehouse location information; determining the fitness values of the initial scheduling task coding sets in the initial scheduling task coding cluster; selecting a target scheduling task coding set from the initial scheduling task coding cluster based on the fitness values; inputting the target scheduling task coding set into a preset task flow model, and scheduling the workshop tasks through the preset task flow model.

[0073] This technology obtains information such as all scheduling tasks and location sets in the workshop through specific scheduling algorithms and preset task flow models to simulate the operation of the workshop. However, the scheduling algorithms may be based on specific assumptions and models, which may limit their adaptability in diverse and dynamically changing warehouse environments, resulting in the simulation scheduling not being the actual real scheduling situation. In addition, there may be a lack of effective anomaly detection and handling mechanisms, leading to a slow response when problems occur as the scheduling algorithms do not consider abnormal states and abnormal situations of equipment, etc.

[0074] Based on this, the embodiments of the present application provide a warehousing management method, device, and electronic device based on digital twins.

[0075] Embodiment 1

[0076] The present application provides a warehousing management method based on digital twins. The method is applied to a warehouse management system, which includes a digital twin platform and a warehouse monitoring and management platform. The digital twin platform is communicatively connected to the warehouse monitoring and management platform.

[0077] Combined with Figure 1 As shown, the method includes:

[0078] S110, constructing a digital twin model corresponding to the warehouse on the digital twin platform.

[0079] S120, obtaining an actual operation efficiency evaluation result generated by the digital twin model based on real-time operation information.

[0080] S130, adjusting the configuration parameters of the digital twin model through a virtual interface based on preset improvement indicators.

[0081] S140, obtaining a predicted operation efficiency evaluation result of the digital twin model running based on the adjusted configuration parameters.

[0082] S150, determining target operation parameters based on the comparison relationship between the actual operation efficiency evaluation result and the predicted operation efficiency evaluation result.

[0083] S160, sending the target operation parameters to the warehouse monitoring and management platform to adjust the operation parameters of the warehouse.

[0084] This application provides a warehouse management method based on digital twin. Based on digital twin technology, it uses a digital twin model to simulate the operation situation in the warehouse in real time and evaluate the operation efficiency, generating the actual operation efficiency evaluation result under the current configuration. Then, through a virtual interface, the configuration parameters of the digital twin model are adjusted to simulate the impact of different configurations on the warehouse operation efficiency, generating the predicted operation efficiency evaluation result. After that, by comparing the actual operation efficiency evaluation result with the predicted operation efficiency evaluation result, the optimal target operation parameters are determined, and then the operation status in the warehouse is adjusted based on the target parameters to improve the warehouse operation efficiency. This method utilizes digital twin technology, synchronizes real-time data acquisition through a long connection to the digital twin model, adjusts configuration parameters through a virtual interface, with independent data transmission, and the configuration parameter adjustment is based on the actual operation efficiency evaluation result, having reliability and being beneficial to improving the operation efficiency of the warehouse.

[0085] Step S110 includes:

[0086] S111, loading an initial digital twin model on a digital twin modeling platform.

[0087] S112, obtaining the information of the automated storage and retrieval system in the warehouse monitoring and management platform through a preset API interface.

[0088] S113, using the information of the automated storage and retrieval system as initial configuration parameters to initialize the initial digital twin model, obtaining the digital twin model.

[0089] S114, obtaining the real-time operation information of the warehouse based on a long connection to update the initial digital twin model in real time, obtaining the digital twin model corresponding to the warehouse.

[0090] In step S110, a digital twin model corresponding to the warehouse is constructed on the digital twin platform. This model includes, but is not limited to, an automated storage and retrieval system model, a conveyor belt model, an RGV (Rail Guided Vehicle) model, and a stacker crane model. In this way, by simulating each component of the actual warehouse, a virtual environment is created for evaluating and optimizing the operation efficiency of the warehouse.

[0091] Among them, in step S111, a pre-constructed initial digital twin model is loaded in the digital twin platform to provide a basic virtual environment for subsequent operations.

[0092] In step S112, the API interface is used to implement data interaction between the digital twin platform and the warehouse monitoring and management platform to ensure that the digital twin model can obtain the vertical warehouse information of the warehouse. Among them, the vertical warehouse information at least includes the physical layout of the warehouse (such as shelf size, shelf position coordinates, bin number, bin position coordinates, roadway position coordinates, layout of the inbound and outbound areas, conveyor belt information in the picking area), storage capacity (such as the quantity of goods stored, whether there are goods in each bin number), conveyor belt information in the inbound and outbound areas, operation information in the picking area, inbound and outbound information, etc.

[0093] After that, step S113 initializes the digital twin warehouse model based on the vertical warehouse information obtained in step S112 to establish the association between the digital twin model and the vertical warehouse data of the actual warehouse. In this way, it is ensured that the digital twin model has actual data support during initialization, improving the accuracy of the simulation results.

[0094] In this application, through long connection (such as WebSocket) technology, a persistent communication channel is established, enabling the digital twin platform to continuously receive real-time data from the warehouse management system. In this way, in step S114, the warehouse monitoring and management platform continuously pushes the latest operation data, that is, real-time operation information, to the digital twin platform based on WebSocket or other long connection technologies. After receiving the above real-time operation information, the digital twin platform immediately parses and extracts key information, and based on the parsed data, it updates the relevant component status in the digital twin model in real time, thereby realizing the synchronization of real-time operation information to the initialized initial digital twin model and obtaining a digital twin model that can reflect the real-time operation status of the warehouse.

[0095] Among them, the real-time operation information at least includes: stacker information, AGV movement information, RGV movement information, conveyor belt movement information. For example, stacker information includes: task information of the stacker, running speed of the stacker, etc.; AGV movement information includes: operation information such as AGV position, task, speed, and equipment status information (working status, idle status, maintenance status); RGV movement information at least includes: operation information such as RGV position, task, speed, arrival position identifier (arriving at the specified inbound and outbound conveyor belt), and equipment status information; conveyor belt movement information at least includes: conveyor belt running direction, speed, quantity and position of the cargo boxes on the conveyor belt, and simulates the warehouse operation through data update.

[0096] It can be understood that in the digital twin model obtained by real-time simulation of the warehouse operation based on the above data, there are at least multiple sub-digital twin models. For example, the cargo box digital twin model in the picking area. Specifically, by comprehensively capturing the key operation data in the picking area (including the inbound and outbound conveyor belt channels, speed and relative coordinates of the rollers, length and width dimensions of the conveyor belt, as well as the quantity, status, and position coordinates of the cargo boxes), the precise construction of the sub-digital twin model of the cargo boxes in the picking area is realized.

[0097] Combined with the first aspect, the operation efficiency evaluation result includes the comprehensive efficiency of the conveyor belt. Step S130 includes:

[0098] S1301, obtain the number of first task orders in the outbound area, the number of second task orders in the inbound area, the conveying speed in the inbound area, the working time period, and the quantity of work.

[0099] It can be understood that in step S110, a digital twin model corresponding to the warehouse has been constructed. At this time, the digital twin model can simulate the actual operation situation in the warehouse and extract the number of first task orders in the outbound area, the number of second task orders in the inbound area, the conveying speed in the inbound area, the working time period, and the quantity of work from the actual operation information.

[0100] S1302, calculate the product of the number of goods units processed per hour by the conveyor belt and the working duration to obtain the processing capacity of a single conveyor belt.

[0101] Specifically, C = v×T; where C is the theoretical processing capacity of a single conveyor belt, v is the number of goods units processed per hour by the conveyor belt based on preset or actual operation statistics, and T is the working duration.

[0102] S1303, calculate the ratio of the number of tasks actually completed to the number of conveying tasks to obtain the task completion rate.

[0103] Specifically, R = Q÷N; where R is the task completion rate, Q is the number of tasks actually completed, and N is the total number of tasks of this conveyor belt.

[0104] S1304, calculate the comprehensive efficiency of the conveyor belt based on the task completion rate, the processing capacity of a single conveyor belt, and the number of conveyor belts.

[0105] Specifically, where E is the comprehensive efficiency of the conveyor belt, R is the task completion rate, n is the number of conveyor belts, C is the theoretical processing capacity of a single conveyor belt, and Q is the number of tasks actually completed.

[0106] Combined with the first aspect, the operation efficiency evaluation result also includes the utilization rate of the RGV. Step S130 also includes:

[0107] S130a, obtain the speed value of the RGV, the track circumference of the RGV track area, and the number of RGVs.

[0108] Similarly, extract the above information from the actual operation information.

[0109] S130b, calculate the ratio of the track circumference to the speed value to calculate the single-cycle time of the RGV.

[0110] Specifically, Among them, L is the track perimeter, v 0 is the speed value, and T is the single-cycle time.

[0111] S130c, based on the single-cycle time, calculate the number of cycles within a preset duration.

[0112] It can be understood that usually a natural day is selected for evaluating the operation efficiency. Specifically:

[0113] Among them, M is the number of cycles in a day, t is the working duration, and T is the single-cycle time.

[0114] S130d, calculate the number of cycles and the number of RGVs, and calculate the total transportation times within a preset duration.

[0115] Specifically, C ′ = n ′ × M; among them, C ′ is the total transportation times in a day, n ′ is the number of RGVs, and M is the number of cycles in a day.

[0116] S130e, calculate the ratio of the actual transportation times to the total transportation times to obtain the utilization rate of the RGV.

[0117] Specifically, Among them, U ′ is the utilization rate of the RGV, Q ′ is the actual transportation times, and C ′ is the total transportation times in a day.

[0118] S130f, calculate the ratio of the actual completed task quantity to the total task quantity to obtain the task completion rate.

[0119] Specifically, Among them, R ′ is the task completion rate, Q ′ is the actual transportation times, and N ′ is the total task quantity.

[0120] S130g, based on the task completion rate and the utilization rate of the RGV, calculate the comprehensive utilization rate of the RGV.

[0121] Specifically, Among them, E ′ is the comprehensive utilization rate of the RGV, R ′ is the task completion rate, and U ′ is the utilization rate of the RGV.

[0122] Combined with the first aspect, the operation efficiency evaluation result also includes the working efficiency of the stacker crane; step S130 also includes:

[0123] S130A. Obtain the running speed of the stacker in the warehouse area, the rack numbers, layer numbers, storage location numbers of the incoming and outgoing goods, and the rack dimensions.

[0124] Similarly, extract the above information from the actual operation information.

[0125] S130B. Obtain the running speeds of the stacker in the horizontal and vertical directions.

[0126] After obtaining the running speed of the stacker in S130A, decompose the speed to obtain the running speeds in the horizontal and vertical directions.

[0127] S130C. Calculate the time taken for the stacker to complete one incoming and outgoing operation based on the layer number, height, storage location number, width of each rack layer, and picking time.

[0128] Specifically, where T″ is the time taken for the stacker to complete one incoming and outgoing operation, L″ is the layer number, H is the height value of the layer number, P″ is the storage location number, W is the width of the rack layer, V″ is the running speed of the stacker, and t″ is the picking time.

[0129] S130D. Calculate the ratio of the working duration to the time taken for the stacker to complete one incoming and outgoing operation, and calculate the number of operations and the total operation distance of the stacker within a preset duration.

[0130] Specifically, N″ is the number of operations of the stacker in a day, D″ is the working duration of the stacker, and T″ is the duration of one incoming and outgoing operation.

[0131] where R″ is the total operation distance, L″ is the layer number, H is the height value of the layer number, P″ is the storage location number, and W is the width of the rack layer.

[0132] S130E. Calculate the operation number efficiency based on the number of operations and the working duration, and calculate the operation distance efficiency based on the total operation distance and the operation duration.

[0133] Specifically,

[0134]

[0135] where E 1 is the operation number efficiency, E 2 is the operation distance efficiency, N″ is the number of operations of the stacker in a day, D″ is the working duration of the stacker, and R″ is the total operation distance.

[0136] It can be understood that after calculating the comprehensive efficiency of the conveyor belt, the utilization rate of the RGV, and the working efficiency of the stacker based on the above method, the operation efficiency evaluation result at this time is obtained. If the actual operation information is used for calculation at this time, then the actual operation efficiency evaluation result is obtained. It can be understood that if other operation information is used for calculation at this time, then the operation efficiency evaluation result corresponding to the other operation information is obtained. It can be understood that the comprehensive efficiency of the conveyor belt, the utilization rate of the RGV, and the working efficiency of the stacker are important indicators for evaluating the operation of the warehouse system. Therefore, by comparing the operation efficiency evaluation results under different operation information, the target operation parameters that help improve the operation efficiency of the warehouse system can be selected. Moreover, by comparing the operation efficiency evaluation results under different configuration parameters, the working efficiency of the equipment in each storage area can be compared. These equipment can be the stacker in the vertical storage area, the conveyor belt in the inbound area, the RGV in the RGV track area, the conveyor belt in the picking area, etc. In this way, the operation strategy of the warehouse can be further optimized to improve the operation efficiency. When the operation efficiency of the storage area of the virtual interface is higher than that of the actual storage area, some configurations of the actual warehouse operation can refer to the configuration of the virtual interface to improve the comprehensive efficiency of the warehouse.

[0137] It can be understood that because the above operation efficiency evaluation result is closely combined with actual data, it has higher reference value and practicality, providing strong data support for warehouse management decision-making.

[0138] Therefore, in this application, after the operation efficiency is evaluated based on the actual operation information in step S130, the manager observes or compares each index with a preset index threshold to determine whether the above index needs to be adjusted. When an index needs to be adjusted, the index to be improved is used as the target improvement index. Or, the index to be improved set by a pre-program can be used as the target improvement index.

[0139] After determining the target improvement metrics, the configuration parameters related to the target improvement metrics are adjusted through the virtual interface. For example, if the target improvement metric is the overall utilization rate of the RGV, the relevant configuration parameters such as the speed value of the RGV and the number of RGVs can be adjusted at this time. It should be noted that the parameter adjustment is performed through the virtual interface at this time, and the virtual interface is independent of the long connection, that is, the configuration parameter channel is relatively independent of the transmission of real-time operation information, thus ensuring the independence and security of the data. After that, a simulation run is performed based on the adjusted configuration parameters, and step S130 is executed to calculate the operation efficiency under the simulation run situation, and a predicted operation efficiency evaluation result is obtained. Subsequently, step S150 compares the actual operation efficiency evaluation result with the predicted operation efficiency evaluation result under the simulation run situation at this time to determine whether each index has been improved and whether the operation efficiency of the warehouse system can be improved, and then takes the operation parameters under the situation of higher operation efficiency as the target operation parameters. Step S160 sends the target operation parameters to the warehouse monitoring and management platform to control the warehouse system to operate with the target operation parameters that can improve the warehouse operation efficiency.

[0140] In this technical solution, the digital twin model automatically generates a detailed electronic report every day based on the real-time operation information and the operation efficiency evaluation result, so as to comprehensively analyze the operation status and efficiency indicators of the warehouse. First, the current operation efficiency of the warehouse is calculated by using the actually collected parameter data, such as the goods flow and the equipment performance. Then, based on these actual efficiency data, the initial parameters are finely adjusted through the virtual interface, and a simulation is carried out through the digital twin model. By comparing the operation efficiency of the warehouse obtained from the simulation with the actual operation data, the performance of the warehouse operation can be deeply analyzed and evaluated. It should be noted that our virtual data interface focuses on setting and adjusting the initial parameters and does not involve real-time data synchronization, ensuring the independence and controllability of the simulation. This method allows us to test and verify potential improvement measures without disturbing the actual warehouse operation, so that the simulation results are more valuable for reference and have practical application potential.

[0141] When the AGV places the incoming storage box on the incoming conveyor belt, the digital twin model sends precise scheduling instructions to the RGV cluster and the stacker crane cluster. The RGV cluster then activates the intelligent instruction processing mechanism, giving priority to allocating tasks to the nearest RGV without carrying goods to ensure operational efficiency. Facing instructions sent from multiple conveyor belts simultaneously, the system follows the principle of giving priority to the one closest to the storage area for task allocation, optimizing resource utilization. In addition, our digital twin model equips each RGV model with an advanced automatic collision detection function. This function can monitor and predict potential collision risks in real time. Once it detects an impending collision, the system will automatically adjust the speed of the relevant RGV to ensure synchronization with the potentially colliding RGV, thus skillfully avoiding collisions and ensuring the smoothness and safety of warehouse operations.

[0142] The RGV sub-digital twin model is included in this digital twin model, and the running speed of the RGV sub-digital twin model is calculated as follows: Among them, V x is the running speed of the RGV sub-digital twin model, L x is the perimeter of the track model, L 0 is the perimeter of the actual warehouse track; v x is the actual running speed of the RGV.

[0143] If the stacker crane cluster receives an instruction for an incoming storage box, the system responds quickly. For instructions containing specific storage location information, such as the designated lane, layer number, and storage location number, the stacker crane cluster will intelligently allocate the task directly to the stacker crane in the corresponding lane and accurately plan the running trajectory of the stacker crane model based on these detailed storage parameters to ensure that the storage box can be stored efficiently and accurately. However, if the specific storage location of the storage box is not specified in the incoming instruction, the system will activate the 'furthest principle' and automatically allocate the task to the furthest available stacker crane at present. At the same time, when the stacker crane stores the storage box at the storage location number, it will synchronize the goods information, lane, layer number, and storage location number to the system. This strategy not only optimizes resource allocation but also effectively avoids subsequent RGV incoming delays caused by waiting, thus maintaining the smoothness and efficiency of the entire warehouse operation. Through this flexible and intelligent task scheduling mechanism, our system can ensure the efficient operation of the warehouse while reducing potential congestion and delays.

[0144] Specifically, the method for planning the running trajectory of the stacker crane is as follows:

[0145] Determine the horizontal route trajectory and calculate the horizontal route length based on the starting point of the stacker crane, lane, storage location number, and the model width of the storage rack.

[0146] Then determine the vertical route trajectory and calculate the vertical route length based on the lane, storage location number, and layer number.

[0147] Connect the horizontal and vertical routes in series to form a complete running trajectory, and the total length of the trajectory route is L y It is equal to the sum of the horizontal trajectory length and the vertical trajectory length.

[0148] Calculate the running speed of the stacker according to the preset actual height and length of the storage lane in the automated storage and retrieval system (AS / RS) and the speed of the stacker.

[0149] Among them, V y is the running speed of the stacker in the digital twin model of the stacker, L y is the total length of the trajectory route, w is the length of the actual storage lane shelf in the AS / RS, h is the height of the actual shelf layer, and v y is the actual speed of the stacker.

[0151] If the stacker cluster receives an outbound order, the system will intelligently allocate the task to the stacker in the corresponding lane according to the specific information of the outbound task, such as the lane, layer number, and storage location number. It can be understood that digital twin technology can accurately plan the running trajectory and speed of the stacker model to ensure that every operation is accurate. When the stacker successfully retrieves the outbound container and is ready for the next operation, it will issue an outbound completion instruction. At this time, the RGV cluster responds quickly. Through an intelligent scheduling algorithm, it gives priority to allocating the task to the nearest idle RGV at the current location. The RGV obtains the coordinates of the corresponding outbound conveyor belt according to the corresponding outbound order and transports the container to the corresponding location for outbound.

[0152] If the container is put back on the inbound roller belt after manual sorting, and the container follows the planned path, the system will send an inbound order to the RGV cluster at this time. After receiving the order, the RGV cluster will quickly perform intelligent scheduling to ensure that the container can be transported to the designated storage location in a timely and accurate manner.

[0153] Combined with the first aspect, the method further includes:

[0154] S160. In response to a target device operation information query instruction, obtain the unique identifier of the target device.

[0155] S170. Based on the unique identifier, retrieve the current operation information corresponding to the unique identifier in the real-time operation information.

[0156] S180. Feed back the current operation information to the warehouse monitoring and management platform through the API interface.

[0157] It can be understood that the operation information of each device can also be retrieved through the digital twin platform. Specifically, based on the unique identifier of the device as an index to distinguish each device, the unique identifier corresponding to each device is pre-stored, and the operation information of the device, such as the current position coordinates, the current operation status, the operation direction, etc., is associated based on the unique identifier. In this way, after receiving the query instruction for the operation information of the target device, the relevant current operation information is retrieved based on the unique identifier of the target device and returned to the warehouse monitoring and management platform through the API interface for communication connection, so that the manager can view it from the warehouse management platform.

[0158] In a second aspect, the present application provides a warehousing management device based on digital twins. The device is applied to a warehouse management system, and the warehouse management system includes a digital twin platform and a warehouse monitoring and management platform. The digital twin platform is communicatively connected to the warehouse monitoring and management platform; combined with Figure 2 As shown, the device includes: a construction module 10, a first acquisition module 20, an adjustment module 30, a second acquisition module 40, a determination module 50, and a feedback module 60.

[0159] The construction module 10 is used to construct a digital twin model corresponding to the warehouse on the digital twin platform.

[0160] The first acquisition module 20 is used to acquire the actual operation efficiency evaluation result generated by the digital twin model based on the real-time operation information.

[0161] The adjustment module 30 is used to adjust the configuration parameters of the digital twin model through a virtual interface based on the target improvement index.

[0162] The second acquisition module 40 is used to acquire the predicted operation efficiency evaluation result of the digital twin model running based on the adjusted configuration parameters.

[0163] The determination module 50 is used to determine the target operation parameters based on the comparison relationship between the actual operation efficiency evaluation result and the predicted operation efficiency evaluation result.

[0164] The feedback module 60 is used to send the target operation parameters to the warehouse monitoring and management platform to adjust the operation parameters of the warehouse.

[0165] In a third aspect, an embodiment of the present application provides an electronic device. Combined with Figure 3 As shown, the electronic device includes a memory 131 and a processor 130. The memory 131 is used to store a computer program, and the processor 130 runs the computer program to enable the electronic device to execute the above method.

[0166] Furthermore, combined with Figure 3The electronic device shown also includes a bus 132 and a communication interface 133. The processor 130, the communication interface 133, and the memory 131 are connected through the bus 132.

[0167] Among them, the memory 131 may include high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk memory. Through at least one communication interface 133 (which can be wired or wireless), a communication connection is realized between this system network element and at least one other network element. The Internet, wide area network, local area network, metropolitan area network, etc. can be used. The bus 132 can be an ISA bus, a PCI bus, an EISA bus, etc. The said bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of easy representation, Figure 3 only a single bidirectional arrow is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0168] The processor 130 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit in the hardware of the processor 130 or the instructions in software form. The above-mentioned processor 130 can be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it can also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field-programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present invention can be directly embodied as being executed and completed by the hardware decoding processor, or executed and completed by the combination of the hardware and software modules in the decoding processor. The software module can be located in the random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, registers, and other mature storage media in the art. This storage media is located in the memory 131, and the processor 130 reads the information in the memory 131 and combines its hardware to complete the steps of the method in the foregoing embodiments.

[0169] Fourthly, an embodiment of the present application provides a readable storage medium. When computer program instructions stored in the readable storage medium are read and run by a processor, the above-mentioned method is executed.

[0170] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems and devices described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0171] In addition, in the description of the embodiments of the present invention, unless otherwise clearly defined and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0172] If the above-mentioned functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0173] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, the terms "first", "second", and "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0174] Finally, it should be noted that the above embodiments are only specific implementation manners of the present invention, used to illustrate the technical solutions of the present invention, rather than limiting it. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art within the technical scope disclosed by the present invention can still modify the technical solutions recorded in the foregoing embodiments, or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes 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 invention, and should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A warehouse management method based on digital twins, characterized in that: The method is applied to a warehouse management system, which includes a digital twin platform and a warehouse monitoring and management platform, and the digital twin platform is communicatively connected with the warehouse monitoring and management platform; The method comprises: Constructing a digital twin model corresponding to the warehouse on the digital twin platform; Obtaining actual operation efficiency evaluation results generated by the digital twin model based on real-time operation information; Based on the target improvement index, adjusting the configuration parameters of the digital twin model through the virtual interface; Obtaining a predicted operating efficiency evaluation result of the digital twin model based on the adjusted configuration parameters; Determining target operating parameters based on a comparison between the actual operating efficiency evaluation result and the predicted operating efficiency evaluation result; The target operating parameters are sent to the warehouse monitoring and management platform to adjust the warehouse operating parameters.

2. The method according to claim 1, characterized in that The steps to build and initialize the digital twin model corresponding to the warehouse include: Loading an initial digital twin model on the digital twin modeling platform; Obtaining the warehouse information in the warehouse monitoring management platform through a preset API interface; Using the library information as initial configuration parameters to initialize the initial digital twin model; Based on the long connection, the real-time operation information of the warehouse is obtained to update the initial digital twin model in real time and obtain the digital twin model corresponding to the warehouse.

3. The method according to claim 1, characterized in that Based on the digital twin model and the acquired real-time operation information, the step of mapping the real-time operation information of the warehouse to the warehouse management platform based on the digital twin in real time includes: Establishing a communication connection between the digital twin model and the warehouse management system; Real-time operation information is obtained through a long connection, and the real-time operation information is synchronously updated with the digital twin model; the real-time operation information includes at least: stacker task information, AGV action information, RGV action information, and conveyor belt action information.

4. The method according to claim 1, characterized in that: The operation efficiency evaluation result includes the overall efficiency of the conveyor belt; The step of obtaining an actual operation efficiency evaluation result generated by the digital twin model based on the real-time operation information comprises: Obtain the number of first task orders in the outbound area, the number of second task orders in the inbound area, the conveying speed, working time period and work quantity of the inbound area; Calculate the product of the number of cargo units processed by the conveyor belt per hour and the working time to obtain the processing capacity of a single conveyor belt; Calculate the ratio of the number of tasks actually completed to the number of tasks transmitted to obtain the task completion rate; The comprehensive efficiency of the conveyor belts is calculated based on the task completion rate, the processing capacity of the single conveyor belt, and the number of the conveyor belts.

5. The method according to claim 1, characterized in that The operation efficiency evaluation results also include the comprehensive utilization rate of RGV; The step of obtaining an actual operation efficiency evaluation result generated by the digital twin model based on the real-time operation information further includes: Get the speed value of RGV, the track circumference of RGV track area, and the number of RGVs; Calculate the ratio of the track circumference to the speed value, and calculate the single cycle time of the RGV; Based on the single cycle time, calculating the number of cycles within a preset time length; Calculate the number of cycles and the number of RGVs, and calculate the total number of transports within the preset time period; Calculate the ratio of the actual transport times to the total transport times to obtain the utilization rate of the RGV; Calculate the ratio of the number of tasks actually completed to the total number of tasks to get the task completion rate; Based on the task completion rate and the utilization rate of the RGV, a comprehensive utilization rate of the RGV is calculated.

6. The method according to claim 5, characterized in that The operating efficiency evaluation result also includes the working efficiency of the stacker; The step of obtaining an actual operation efficiency evaluation result generated by the digital twin model based on the real-time operation information further includes: Obtain the operating speed of the stacker in the warehouse area, the shelf number, layer number, cargo location number and shelf size of the goods entering and leaving the warehouse; Obtaining the running speed of the stacker in the horizontal direction and the vertical direction; Based on the layer number, height, cargo location number, width of each shelf layer and pickup time, calculate the time it takes for the stacker to complete one storage entry and exit; Calculate the ratio of the working time to the time it takes for the stacker to complete one storage and retrieval operation, and calculate the number of operations and the total operation distance of the stacker within the preset time; The operation number efficiency is calculated based on the operation number and the working time, and the operation distance efficiency is calculated based on the total operation distance and the operation time.

7. The method according to claim 1, characterized in that The method further comprises: In response to a target device operation information query instruction, obtaining a unique identifier of the target device; Based on the unique identifier, retrieve the current operation information corresponding to the unique identifier in the real-time operation information; The current operation information is fed back to the warehouse monitoring management platform through the API interface.

8. A warehouse management device based on digital twins, characterized in that: The device is applied to a warehouse management system, which includes a digital twin platform and a warehouse monitoring and management platform, and the digital twin platform is communicatively connected with the warehouse monitoring and management platform; the device includes: A construction module, used to construct a digital twin model corresponding to the warehouse on the digital twin platform; A first acquisition module is used to obtain an actual operation efficiency evaluation result generated by the digital twin model based on the real-time operation information; An adjustment module, used to adjust the configuration parameters of the digital twin model through a virtual interface based on the target improvement index; A second acquisition module is used to obtain a predicted operation efficiency evaluation result of the digital twin model based on the adjusted configuration parameters; A determination module, configured to determine a target operating parameter based on a comparison between the actual operating efficiency evaluation result and the predicted operating efficiency evaluation result; The feedback module is used to send the target operating parameters to the warehouse monitoring and management platform to adjust the operating parameters of the warehouse.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores computer program instructions, and when the computer program instructions are read and executed by a processor, the method according to any one of claims 1 to 7 is executed.

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