Warehouse intelligent scheduling method based on digital twinning and related device

Abstract

Embodiments of the present application relate to the field of intelligent scheduling, and provide a warehouse intelligent scheduling method based on digital twinning and related devices, the method comprising: acquiring basic attribute information of goods to be stored in a warehouse; performing storage matching information construction according to the basic attribute information to obtain reference storage matching information; determining target storage location information from a digital twinning model of the warehouse by using the reference storage matching information; generating storage path information of the goods to be stored in the warehouse by using the target storage location information; and performing storage processing of the goods to be stored in the warehouse in the warehouse by using the storage path information and the target storage location information, which can determine a storage location in combination with the basic attribute information of the goods to be stored in the warehouse and the digital twinning model of the warehouse, generate a storage path based on the storage location, and finally perform storage, thereby improving the accuracy of storage scheduling of the goods.

Description

Technical Field

[0001] This application relates to the fields of intelligent scheduling and warehouse logistics optimization technology, specifically to a warehouse intelligent scheduling method and related devices based on digital twins. Background Technology

[0002] In existing warehouses, storage location allocation relies on manual experience and does not consider the dynamic matching of multi-dimensional attributes of goods (weight, volume, storage period, value) with storage location characteristics. Fixed handling routes lead to low inbound efficiency, and fragile / high-value goods lack dedicated storage strategies. As a result, in existing warehouse management, it is easy for goods to be stored to fail to be stored in the optimal location, resulting in low accuracy in goods storage scheduling. Summary of the Invention

[0003] This application provides a warehouse intelligent scheduling method and related apparatus based on digital twins, which can determine the storage location by combining the basic attribute information of the goods to be stored with the digital twin model of the warehouse, generate a storage path based on the storage location, and finally store the goods, thereby improving the accuracy of the storage scheduling of goods.

[0004] The first aspect of this application provides a warehouse intelligent scheduling method based on digital twins, the method comprising:

[0005] Obtain the basic attribute information of goods to be received into the warehouse;

[0006] Based on the aforementioned basic attribute information, storage matching information is constructed to obtain reference storage matching information;

[0007] The target storage location information is determined from the digital twin model of the warehouse using the reference storage matching information;

[0008] The storage path information of the goods to be stored is generated using the target storage location information;

[0009] The storage path information and the target storage location information are used to process the goods to be stored in the warehouse.

[0010] In one possible implementation, the step of constructing storage matching information based on the basic attribute information to obtain reference storage matching information includes:

[0011] Extract and store key information from the basic attribute information;

[0012] The key storage information is standardized to obtain standard key storage information;

[0013] Extract emergency tag information from the standard stored key information;

[0014] Determine emergency tag matching information based on the emergency tag information;

[0015] Based on the standard storage key information and the emergency tag matching information, a matching vector is constructed to obtain reference storage matching information.

[0016] In one possible implementation, determining the target storage location information from the digital twin model of the warehouse using the reference storage matching information includes:

[0017] Determine storage adaptation information based on the reference storage matching information;

[0018] Based on the storage adaptation information, location filtering is performed from the digital twin model to obtain k reference storage location information;

[0019] The storage matching degree is calculated for k reference storage location information to obtain k storage matching degrees;

[0020] Extract n first storage location information from k reference storage location information, where the storage matching degree of the first storage location information is higher than the preset storage matching degree threshold;

[0021] The optimal storage environment information is determined based on the reference storage matching information;

[0022] The target storage location information is determined from n first storage location information based on the optimal storage environment information.

[0023] In one possible implementation, the storage path information of the goods to be stored is generated using the target storage location information, including:

[0024] Based on the target storage location information and the initial location of the goods to be stored, determine m reference storage path information;

[0025] Extract the key node information from m reference storage path information to obtain m sets of key node information;

[0026] Based on the information set of m key nodes, segmented driving scores are calculated to obtain m segmented driving score sets.

[0027] The m segmented driving score sets are respectively subjected to score fusion processing to obtain m target driving score values;

[0028] Based on the driving scores of m targets, the storage path information of the goods to be stored is determined from the m reference storage path information.

[0029] In one possible implementation, the method further includes:

[0030] If the target storage location information is empty, then extract the size information from the basic attribute information;

[0031] If the size indicated by the size information exceeds a preset size threshold, an alarm message is generated;

[0032] Display the alarm information.

[0033] A second aspect of this application provides a warehouse intelligent scheduling device based on digital twins, the device comprising:

[0034] The acquisition unit is used to acquire the basic attribute information of goods to be put into storage.

[0035] A construction unit is used to construct storage matching information based on the basic attribute information to obtain reference storage matching information;

[0036] The determining unit is used to determine the target storage location information from the digital twin model of the warehouse using the reference storage matching information;

[0037] The generation unit is used to generate storage path information for the goods to be stored using the target storage location information;

[0038] The receiving unit is used to process the goods to be received in the warehouse using the storage path information and the target storage location information.

[0039] In one possible implementation, the building unit is specifically used for:

[0040] Extract and store key information from the basic attribute information;

[0041] The key storage information is standardized to obtain standard key storage information;

[0042] Extract emergency tag information from the standard stored key information;

[0043] Determine emergency tag matching information based on the emergency tag information;

[0044] Based on the standard storage key information and the emergency tag matching information, a matching vector is constructed to obtain reference storage matching information.

[0045] In one possible implementation, the determining unit is specifically used for:

[0046] Determine storage adaptation information based on the reference storage matching information;

[0047] Based on the storage adaptation information, location filtering is performed from the digital twin model to obtain k reference storage location information;

[0048] The storage matching degree is calculated for k reference storage location information to obtain k storage matching degrees;

[0049] Extract n first storage location information from k reference storage location information, where the storage matching degree of the first storage location information is higher than the preset storage matching degree threshold;

[0050] The optimal storage environment information is determined based on the reference storage matching information;

[0051] The target storage location information is determined from n first storage location information based on the optimal storage environment information.

[0052] In one possible implementation, the generation unit is specifically used for:

[0053] Based on the target storage location information and the initial location of the goods to be stored, determine m reference storage path information;

[0054] Extract the key node information from m reference storage path information to obtain m sets of key node information;

[0055] Based on the information set of m key nodes, segmented driving scores are calculated to obtain m segmented driving score sets.

[0056] The m segmented driving score sets are respectively subjected to score fusion processing to obtain m target driving score values;

[0057] Based on the driving scores of m targets, the storage path information of the goods to be stored is determined from the m reference storage path information.

[0058] In one possible implementation, the device is further used for:

[0059] If the target storage location information is empty, then extract the size information from the basic attribute information;

[0060] If the size indicated by the size information exceeds a preset size threshold, an alarm message is generated;

[0061] Display the alarm information.

[0062] A third aspect of this application provides a terminal including a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to invoke the program instructions to execute the step instructions as described in the first aspect of this application.

[0063] A fourth aspect of this application provides a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the first aspect of this application.

[0064] A fifth aspect of this application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the first aspect of this application. The computer program product may be a software installation package.

[0065] Implementing the embodiments of this application has the following beneficial effects:

[0066] By acquiring the basic attribute information of the goods to be received, constructing storage matching information based on the basic attribute information, obtaining reference storage matching information, determining the target storage location information from the digital twin model of the warehouse using the reference storage matching information, generating storage path information for the goods to be received using the target storage location information, and performing the receiving process for the goods in the warehouse using the storage path information and the target storage location information, the storage location can be determined by combining the basic attribute information of the goods to be received and the digital twin model of the warehouse, and a storage path can be generated based on the storage location before storage is performed, thus improving the accuracy of goods storage scheduling. Attached Figure Description

[0067] 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 some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0068] Figure 1 This application provides a flowchart illustrating a warehouse intelligent scheduling method based on digital twins.

[0069] Figure 2 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application;

[0070] Figure 3 This application provides a schematic diagram of the structure of a warehouse intelligent scheduling device based on digital twins. Detailed Implementation

[0071] 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.

[0072] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0073] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0074] To better understand the intelligent warehouse scheduling method based on digital twins provided in this application, a brief introduction and analysis of existing warehouse storage scheduling solutions is given below. In existing warehouse storage management, manual methods are typically relied upon. While some warehouse storage management systems based on conventional digital twin models have emerged with technological advancements, these models are usually used for visual management. However, location selection and determination still require significant human intervention for confirmation. Furthermore, path planning relies on simple data and human experience to arrive at results, leading to low accuracy in storage management.

[0075] To address the aforementioned issues, this application provides a warehouse intelligent scheduling method and related apparatus based on digital twins. This method can determine the storage location by combining the basic attribute information of the goods to be stored with the digital twin model of the warehouse, generate a storage path based on the storage location, and finally perform storage, thereby improving the accuracy of goods storage scheduling.

[0076] Please see Figure 1 , Figure 1 This application provides a flowchart illustrating a warehouse intelligent scheduling method based on digital twins. Figure 1 As shown, the method includes:

[0077] 101. Obtain the basic attribute information of the goods to be put into storage.

[0078] The goods to be received can be any items that can be stored in the warehouse, such as packaged logistics goods. Basic attribute information includes weight, volume, dimensions, estimated departure date, whether it is fragile, whether it is high-value, whether it has an expedited label, and some descriptive information. Specifically, this basic attribute information can be obtained by scanning the barcode on the goods to be received.

[0079] 102. Based on the basic attribute information, construct the storage matching information to obtain the reference storage matching information.

[0080] When constructing reference storage matching information, key storage information can be extracted from basic attribute information, and the reference storage matching information can be determined based on this key information. Key storage information may include weight, volume, dimensions, estimated departure date, whether it is fragile, whether it is high-value goods, and whether it has an expedited label. Reference storage matching information can be a vector containing key storage information and expedited label matching information, specifically used as an input for subsequent location information matching in the digital twin model.

[0081] 103. The target storage location information is determined from the digital twin model of the warehouse using the reference storage matching information.

[0082] After determining the reference storage matching information, a digital twin model of the warehouse can be used to determine the target storage location information. Specifically, this can be done by filtering locations based on the reference storage matching information to obtain multiple reference storage location information, and then determining the target storage location information from these multiple reference storage location information based on the corresponding storage matching degree and optimal storage environment information.

[0083] The digital twin model of the warehouse can be created by collecting 3D point cloud data of the warehouse using a laser scanner / RTK (Real-Time Dynamic Carrier Phase Differential) positioning device, importing it into CAD (Computer-Aided Design) software to build a scaled virtual model; and marking the coordinates of fixed facilities (entrances and exits, elevators, load-bearing columns, fire lanes) in the virtual model.

[0084] The digital twin model of a warehouse includes location attributes, functional attributes, and real-time status mapping.

[0085] Location attributes include: near-exit area (≤10 meters from exit); high-bay storage area (shelf height ≥5 meters);

[0086] Deep storage areas at the edges and corners (at the end of aisles or in dead corners); ground storage areas (flat areas without shelving).

[0087] Functional attributes:

[0088] Load-bearing capacity classification (e.g., Class A ≥ 2 tons, Class B ≥ 1 ton); safety level labeling (monitoring coverage, distance to fire-fighting equipment); environmental sensitivity labeling (e.g., earthquake-resistant zone, constant temperature zone).

[0089] Real-time state mapping:

[0090] Establish a warehouse location status dashboard in the twin system and synchronize the real-time occupancy rate (idle / occupied / awaiting replenishment) of the physical warehouse through RFID (Radio Frequency Identification) / infrared sensors.

[0091] Of course, preliminary screening can also be performed. If the target storage location information is empty, then the size information is extracted from the basic attribute information. If the size indicated by the size information exceeds a preset size threshold, an alarm message is generated and displayed. The alarm message can be a general alarm message used to indicate that the size of the goods to be received exceeds the standard and requires manual verification.

[0092] 104. Use the target storage location information to generate storage path information for the goods to be stored.

[0093] Storage path information can be understood as the transportation route that takes goods to be stored in the warehouse to the target storage location indicated by the target storage location information. Multiple unfiltered reference storage paths can be determined based on the target storage location information and the initial location of the goods to be stored, using a digital twin model. Then, each path is segmented and scored, resulting in a driving score. Finally, the driving score is used to filter the storage path information of the goods to be stored from the multiple unfiltered reference storage paths.

[0094] 105. The storage path information and the target storage location information are used to process the goods to be stored in the warehouse.

[0095] After determining the storage path information and the target storage location information, the goods to be stored can be transported to the corresponding location by a transport vehicle based on the above two types of information, and then processed for storage.

[0096] In this example, by obtaining the basic attribute information of the goods to be stored, storage matching information is constructed based on the basic attribute information to obtain reference storage matching information. The target storage location information is determined from the digital twin model of the warehouse using the reference storage matching information. The storage path information of the goods to be stored is generated using the target storage location information. The goods to be stored are then processed for storage in the warehouse using the storage path information and the target storage location information. Therefore, by combining the basic attribute information of the goods to be stored and the digital twin model of the warehouse, the storage location can be determined, and the storage path can be generated based on the storage location before storage is performed, thus improving the accuracy of goods storage scheduling.

[0097] In one possible implementation, a method for constructing storage matching information based on the basic attribute information to obtain reference storage matching information includes:

[0098] A1. Extract and store key information from the basic attribute information;

[0099] A2. Standardize the stored key information to obtain standard stored key information;

[0100] A3. Extract emergency tag information from the standard stored key information;

[0101] A4. Determine emergency tag matching information based on the emergency tag information;

[0102] A5. Based on the standard storage key information and the emergency tag matching information, a matching vector is constructed to obtain reference storage matching information.

[0103] The key information stored may include weight, volume, dimensions, estimated shipping date, whether it is a fragile item, whether it is a high-value item, and whether it has an expedited label.

[0104] A general standardized processing method can be adopted. For example, it can be standardized and mapped to map key information into corresponding numerical codes, and perform general standardized processing on numerical data.

[0105] Emergency label information includes fragile items, high-value goods, and expedited markings. These labels are emergency labels and have a higher priority than ordinary stored critical information.

[0106] Emergency tag matching information can be location matching conditions corresponding to the tags. Because these conditions need to be met in limited quantities, their priority is higher than that of ordinary stored critical information. Furthermore, these conditions may reside in special storage areas, so retrieving emergency tag matching information in a limited way can improve processing efficiency.

[0107] A general vector construction method is used to construct vectors for both the standard stored key information and the emergency tag matching information. These two vectors are then concatenated to obtain the target vector, which is used as the reference stored matching information. By extracting and processing the key information and emergency tag information to obtain the target vector, the accuracy of subsequent location determination and route planning can be improved.

[0108] In one possible implementation, a method for determining target storage location information from a digital twin model of a warehouse using the reference storage matching information includes:

[0109] B1. Determine storage adaptation information based on the reference storage matching information;

[0110] B2. Based on the storage adaptation information, perform location filtering from the digital twin model to obtain k reference storage location information;

[0111] B3. Calculate the storage matching degree for the k reference storage location information to obtain the k storage matching degrees;

[0112] B4. Extract n first storage location information from k reference storage location information, where the storage matching degree of the first storage location information is higher than the preset storage matching degree threshold.

[0113] B5. Determine the optimal storage environment information based on the reference storage matching information;

[0114] B6. Determine the target storage location information from n first storage location information based on the optimal storage environment information.

[0115] Storage adaptation information can be understood as the storage adaptation interval corresponding to each vector parameter in the reference storage matching information. For example, taking size as an example, the storage size of the goods to be stored is a size interval that includes its actual size, so as to find the location within the storage interval to avoid excessive waste of resources.

[0116] After extracting the storage adaptation information, location filtering is performed from the digital twin model to obtain k reference storage location information. Since the k reference storage location information is obtained through preliminary filtering, it may be optimal under certain conditions, but may not be optimal overall. Therefore, the target storage location information can be selected from these by combining the matching degree and the optimal storage environment information.

[0117] When filtering, you can use the filtering strategies in the following table:

[0118]

[0119] When calculating the matching degree, the matching degree can be calculated separately for each piece of basic information to obtain the matching degree corresponding to each piece of basic information. Then, the matching degrees are weighted to obtain k stored matching degrees. Specifically, a general matching degree calculation method can be used for the matching degree calculation. When performing the weighting calculation, pre-set weights corresponding to the basic information can be used. The weights can be obtained from empirical values ​​or historical data.

[0120] After calculating the matching degree, the reference storage location information corresponding to the n storage matching degrees from high to low can be determined as the first storage location information.

[0121] Each type of goods has its corresponding optimal storage environment information. For example, for fragile items, the optimal storage environment information may include storing them in a low position that is easy to move and not easily bumped. For another example, if the goods need a dry environment, the optimal storage environment information may include storing them in a higher position.

[0122] Therefore, the target storage location information can be determined from n first storage location information based on the optimal storage environment information. Specifically, the first storage location information with the highest matching pair between the environment information of the n first storage location information and the optimal storage environment information can be determined as the target storage location information. This allows for comprehensive and accurate acquisition of the target storage location information, improving the accuracy of target storage location information determination.

[0123] In one possible implementation, a method for generating storage path information for goods to be stored using the target storage location information includes:

[0124] C1. Based on the target storage location information and the initial location of the goods to be stored, determine m reference storage path information;

[0125] C2. Extract the key node information from the m reference storage path information to obtain a set of m key node information;

[0126] C3. Calculate segmented driving scores based on the information set of m key nodes to obtain m segmented driving score sets;

[0127] C4. Perform score fusion processing on the m segmented driving score sets respectively to obtain m target driving score values;

[0128] C5. Based on the driving scores of m targets, determine the storage path information of the goods to be stored from the m reference storage path information.

[0129] Among them, the m reference storage path information consists of unfiltered paths, which can be understood as all reachable paths, but not all of them are preferred paths. The key node information can be understood as the information of the points where the path turns.

[0130] When conducting segmented scoring, the scoring can be based on the traffic flow information within each segment. Higher traffic flow results in a lower score, and lower traffic flow results in a higher score. For example, a traffic flow greater than 5 vehicles per minute indicates a highly congested lane and a lower score. Additionally, the angle of the turning points between segments can be considered. Taking the inner angle as an example, once the inner angle is greater than 0 degrees, the larger the angle, the higher the score; 180 degrees indicates no turning point. Therefore, combining traffic flow and turning angles improves accuracy.

[0131] During the fusion process, the more segments there are, the lower the final score. This is because more segments require more turns, increasing the difficulty of transportation. The fusion process can involve obtaining a total score, then adjusting it based on the number of segments. The score also needs to consider the mean squared error; a larger mean squared error results in a lower final score, and vice versa.

[0132] Once the route information is determined, voice prompts can be set at each key point. Simultaneously, the deviation rate between the stored route information and the actual driving route can be compared to automatically correct map weight parameters; cases of incorrect warehouse allocation (such as allocating fragile items to high-vibration areas) can be statistically analyzed, manually marked, and then used to reverse-engineer decision rules; weekly warehouse turnover efficiency reports can be generated, and storage strategy thresholds can be dynamically adjusted (such as shortening the threshold for the "short storage period" definition).

[0133] For examples consistent with the above embodiments, please refer to... Figure 2 , Figure 2 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application, such as... Figure 2 As shown, it includes a processor, an input device, an output device, and a memory, which are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to call the program instructions. The program includes instructions for performing the following steps.

[0134] Obtain the basic attribute information of goods to be received into the warehouse;

[0135] Based on the aforementioned basic attribute information, storage matching information is constructed to obtain reference storage matching information;

[0136] The target storage location information is determined from the digital twin model of the warehouse using the reference storage matching information;

[0137] The storage path information of the goods to be stored is generated using the target storage location information;

[0138] The storage path information and the target storage location information are used to process the goods to be stored in the warehouse.

[0139] In this example, by obtaining the basic attribute information of the goods to be stored, storage matching information is constructed based on the basic attribute information to obtain reference storage matching information. The target storage location information is determined from the digital twin model of the warehouse using the reference storage matching information. The storage path information of the goods to be stored is generated using the target storage location information. The goods to be stored are then processed for storage in the warehouse using the storage path information and the target storage location information. Therefore, by combining the basic attribute information of the goods to be stored and the digital twin model of the warehouse, the storage location can be determined, and the storage path can be generated based on the storage location before storage is performed, thus improving the accuracy of goods storage scheduling.

[0140] The above mainly describes the solutions of the embodiments of this application from the perspective of the method execution process. It is understood that, in order to achieve the above functions, the terminal includes the corresponding hardware structure and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments provided herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0141] This application embodiment can divide the terminal into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0142] For those consistent with the above, please refer to Figure 3 , Figure 3 This application provides a schematic diagram of the structure of a warehouse intelligent scheduling device based on digital twins. For example... Figure 3 As shown, the device includes:

[0143] Acquisition unit 301 is used to acquire basic attribute information of goods to be put into storage;

[0144] Construction unit 302 is used to construct storage matching information based on the basic attribute information to obtain reference storage matching information;

[0145] The determining unit 303 is used to determine the target storage location information from the digital twin model of the warehouse using the reference storage matching information;

[0146] The generation unit 304 is used to generate storage path information for the goods to be stored using the target storage location information;

[0147] The warehousing unit 305 is used to process the goods to be warehoused in the warehouse using the storage path information and the target storage location information.

[0148] In this example, by obtaining the basic attribute information of the goods to be stored, storage matching information is constructed based on the basic attribute information to obtain reference storage matching information. The target storage location information is determined from the digital twin model of the warehouse using the reference storage matching information. The storage path information of the goods to be stored is generated using the target storage location information. The goods to be stored are then processed for storage in the warehouse using the storage path information and the target storage location information. Therefore, by combining the basic attribute information of the goods to be stored and the digital twin model of the warehouse, the storage location can be determined, and the storage path can be generated based on the storage location before storage is performed, thus improving the accuracy of goods storage scheduling.

[0149] In one possible implementation, the building unit 302 is specifically used for:

[0150] Extract and store key information from the basic attribute information;

[0151] The key storage information is standardized to obtain standard key storage information;

[0152] Extract emergency tag information from the standard stored key information;

[0153] Determine emergency tag matching information based on the emergency tag information;

[0154] Based on the standard storage key information and the emergency tag matching information, a matching vector is constructed to obtain reference storage matching information.

[0155] In one possible implementation, the determining unit 303 is specifically used for:

[0156] Determine storage adaptation information based on the reference storage matching information;

[0157] Based on the storage adaptation information, location filtering is performed from the digital twin model to obtain k reference storage location information;

[0158] The storage matching degree is calculated for k reference storage location information to obtain k storage matching degrees;

[0159] Extract n first storage location information from k reference storage location information, where the storage matching degree of the first storage location information is higher than the preset storage matching degree threshold;

[0160] The optimal storage environment information is determined based on the reference storage matching information;

[0161] The target storage location information is determined from n first storage location information based on the optimal storage environment information.

[0162] In one possible implementation, the generating unit 304 is specifically used for:

[0163] Based on the target storage location information and the initial location of the goods to be stored, determine m reference storage path information;

[0164] Extract the key node information from m reference storage path information to obtain m sets of key node information;

[0165] Based on the information set of m key nodes, segmented driving scores are calculated to obtain m segmented driving score sets.

[0166] The m segmented driving score sets are respectively subjected to score fusion processing to obtain m target driving score values;

[0167] Based on the driving scores of m targets, the storage path information of the goods to be stored is determined from the m reference storage path information.

[0168] In one possible implementation, the device is further used for:

[0169] If the target storage location information is empty, then extract the size information from the basic attribute information;

[0170] If the size indicated by the size information exceeds a preset size threshold, an alarm message is generated;

[0171] Display the alarm information.

[0172] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the digital twin-based intelligent warehouse scheduling methods described in the above method embodiments.

[0173] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program that causes a computer to perform some or all of the steps of any of the digital twin-based intelligent warehouse scheduling methods described in the above method embodiments.

[0174] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0175] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0176] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0177] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0178] Furthermore, the functional units in the various embodiments of the application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software program module.

[0179] If the integrated unit is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0180] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk, or an optical disk, etc.

[0181] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A warehouse intelligent scheduling method based on digital twins, characterized in that, The method includes: Obtain the basic attribute information of goods to be received into the warehouse; Based on the aforementioned basic attribute information, storage matching information is constructed to obtain reference storage matching information; The target storage location information is determined from the digital twin model of the warehouse using the reference storage matching information; The storage path information of the goods to be stored is generated using the target storage location information; The storage path information and the target storage location information are used to process the goods to be put into the warehouse for warehousing. The step of constructing reference storage matching information based on the basic attribute information includes: Extract and store key information from the basic attribute information; The key storage information is standardized to obtain standard key storage information; Extract emergency tag information from the standard stored key information; Determine emergency tag matching information based on the emergency tag information; Based on the standard storage key information and the emergency tag matching information, a matching vector is constructed to obtain reference storage matching information; The step of determining the target storage location information from the digital twin model of the warehouse using the reference storage matching information includes: Storage adaptation information is determined based on the reference storage matching information, wherein the storage adaptation information is the storage adaptation interval corresponding to each vector parameter in the reference storage matching information; Based on the storage adaptation information, location filtering is performed from the digital twin model to obtain k reference storage location information; The storage matching degree is calculated for k reference storage location information to obtain k storage matching degrees; Extract n first storage location information from k reference storage location information, where the storage matching degree of the first storage location information is higher than the preset storage matching degree threshold; The optimal storage environment information is determined based on the reference storage matching information; The target storage location information is determined from n first storage location information based on the optimal storage environment information.

2. The warehouse intelligent scheduling method based on digital twins according to claim 1, characterized in that, The storage path information of the goods to be stored is generated using the target storage location information, including: Based on the target storage location information and the initial location of the goods to be stored, determine m reference storage path information; Extract the key node information from m reference storage path information to obtain m sets of key node information; Based on the information set of m key nodes, segmented driving scores are calculated to obtain m segmented driving score sets. The m segmented driving score sets are respectively subjected to score fusion processing to obtain m target driving score values; Based on the driving scores of m targets, the storage path information of the goods to be stored is determined from the m reference storage path information.

3. The warehouse intelligent scheduling method based on digital twins according to claim 2, characterized in that, The method further includes: If the target storage location information is empty, then extract the size information from the basic attribute information; If the size indicated by the size information exceeds a preset size threshold, an alarm message is generated; Display the alarm information.

4. A warehouse intelligent scheduling device based on digital twins, characterized in that, The device includes: The acquisition unit is used to acquire the basic attribute information of goods to be put into storage. A construction unit is used to construct storage matching information based on the basic attribute information to obtain reference storage matching information; The determining unit is used to determine the target storage location information from the digital twin model of the warehouse using the reference storage matching information; The generation unit is used to generate storage path information for the goods to be stored using the target storage location information; The warehousing unit is used to process the goods to be warehoused in the warehouse using the storage path information and the target storage location information. The building unit is specifically used for: Extract and store key information from the basic attribute information; The key storage information is standardized to obtain standard key storage information; Extract emergency tag information from the standard stored key information; Determine emergency tag matching information based on the emergency tag information; Based on the standard storage key information and the emergency tag matching information, a matching vector is constructed to obtain reference storage matching information; The determining unit is specifically used for: Storage adaptation information is determined based on the reference storage matching information, wherein the storage adaptation information is the storage adaptation interval corresponding to each vector parameter in the reference storage matching information; Based on the storage adaptation information, location filtering is performed from the digital twin model to obtain k reference storage location information; The storage matching degree is calculated for k reference storage location information to obtain k storage matching degrees; Extract n first storage location information from k reference storage location information, where the storage matching degree of the first storage location information is higher than the preset storage matching degree threshold; The optimal storage environment information is determined based on the reference storage matching information; The target storage location information is determined from n first storage location information based on the optimal storage environment information.

5. A terminal, characterized in that, The system includes a processor, an input device, an output device, and a memory, which are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to invoke the program instructions to execute the warehouse intelligent scheduling method based on digital twins as described in any one of claims 1-3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to perform the warehouse intelligent scheduling method based on digital twins as described in any one of claims 1-3.

Images