Material stacking storage location allocation method and system

Abstract

The application provides a material stacking warehouse position allocation method and system, wherein the method comprises: acquiring warehouse area state data; synchronizing the warehouse area state data to a digital twin base to construct a virtual deduction environment for warehouse position allocation simulation; in the virtual deduction environment, a plurality of candidate warehouse positions are determined, and for each candidate warehouse position, the conflict risk of the out-of-warehouse timing between the to-be-stored material and the current surface layer material is evaluated when the to-be-stored material is stacked as an upper layer material; and a target warehouse position is determined from the plurality of candidate warehouse positions according to the conflict risk corresponding to each candidate warehouse position. By constructing the digital twin base and the virtual deduction environment of the warehouse area, the actual situation of the warehouse area can be reflected in real time, and in the virtual deduction environment, the stacking situation of different candidate warehouse positions can be preformed, the forward-looking preforming capability based on the conflict risk of the out-of-warehouse timing is possessed, the material burying situation can be effectively avoided, and unnecessary pile turning operations are reduced.

Description

Technical Field

[0001] This application relates to the field of warehouse management technology, and in particular to a method and system for allocating material stacking locations. Background Technology

[0002] In industrial manufacturing, automated warehousing and stacking decisions are a core component of intelligent logistics, aiming to achieve efficient storage and turnover of materials while meeting complex constraints such as load-bearing capacity, layer height, and material compatibility. With the continuous increase in modern industrial capacity and the significant acceleration of production logistics, warehousing scenarios are exhibiting complex characteristics of high-density storage and highly dynamic operations, thus placing more stringent demands on the real-time performance, robustness, and foresight of stacking decisions.

[0003] In related technologies, the allocation of storage locations for material stacking often relies on heuristic algorithms such as prioritizing empty spaces based on static snapshots of current inventory. However, this approach still has significant limitations. When allocating storage locations, it not only becomes disconnected from the actual current state of the storage area but also lacks consideration for the lifespan of materials within the storage area. In complex operating conditions and long-cycle operations, this can easily lead to material burial and frequent repacking, severely hindering the smooth execution of warehousing operations. Summary of the Invention

[0004] This application discloses a material stacking location allocation method and system to solve the technical problems of material being buried and frequent repacking in material stacking.

[0005] This application provides a method for allocating material stacking storage locations. The method includes: acquiring storage area status data; constructing a digital twin base for the storage area and synchronizing the storage area status data to the digital twin base to construct a virtual simulation environment for simulating storage location allocation; in the virtual simulation environment, determining multiple candidate storage locations, and for each candidate storage location, assessing the risk of conflict between the outbound timing of the material to be stored and the current surface layer material when the material to be stored is stacked as the upper layer material; and determining the target storage location for stacking the material to be stored from the multiple candidate storage locations based on the conflict risk corresponding to each candidate storage location.

[0006] In one embodiment of this application, the method for determining the candidate storage location includes: obtaining the locking status of each storage location in the storage area and the first material information of the material to be stored; determining whether the locking status of each storage location is unlocked, and determining whether each storage location has space for stacking the material to be stored based on the first material information; if the locking status of any storage location is unlocked and it has the space, then the any storage location is determined as the candidate storage location.

[0007] In one embodiment of this application, before determining the target storage location for stacking the material to be stored from the plurality of candidate storage locations, the method further includes: obtaining second material information of the current surface layer material in each candidate storage location; for each candidate storage location, determining whether the material to be stored and the current surface layer material violate stacking constraints based on the second material information and the first material information of the material to be stored, wherein the stacking constraints include material constraints, material pressure constraints, and stacking shape constraints; if the determination result for each candidate storage location is a violation of the stacking constraints, then calculating the risk entropy increment corresponding to each candidate storage location, and combining the risk entropy increment to determine the target storage location from the plurality of candidate storage locations.

[0008] In one embodiment of this application, calculating the risk entropy increment corresponding to each candidate storage location includes: determining the target constraints violated between the material to be stored and the current surface layer material in the candidate storage location, wherein the target constraints include at least one of the material constraints, the material pressure constraints, and the stacking shape constraints; determining the target penalty strategy corresponding to the target constraints from a preset penalty strategy, wherein the penalty strategy includes a penalty weight and a penalty function corresponding to each constraint; determining a penalty value according to the target penalty strategy for each target constraint; and summing the penalty values ​​corresponding to all target constraints to obtain the risk entropy increment.

[0009] In one embodiment of this application, assessing the conflict risk of the outbound timing between the material to be received and the current surface layer material includes: acquiring inbound / outbound reservation data and historical inbound / outbound data; determining, based on the inbound / outbound reservation data and the historical inbound / outbound data, a first distribution expectation value and a first distribution variance of the expected outbound time of the material to be received, and a second distribution expectation value and a second distribution variance of the expected outbound time of the current surface layer material; generating, based on a preset probability distribution model and combining the first distribution expectation value and the first distribution variance, a first probability distribution model of the expected outbound time of the material to be received, and generating, based on the second distribution expectation value and the second distribution variance, a second probability distribution model of the expected outbound time of the current surface layer material; and calculating the conflict risk based on the first probability distribution model and the second probability distribution model.

[0010] In one embodiment of this application, the determination of the first distribution expectation value and the first distribution variance includes: determining the first entry time and the first exit time of the material to be entered based on the entry and exit reservation data; determining the first average storage time of similar materials to be entered and the first lateness rate and first modification rate of the scheduled exit time of similar materials to be entered based on the historical entry and exit data; determining the second exit time of the material to be entered based on the first entry time and the first average storage time; determining the first distribution expectation value based on the first exit time and / or the second exit time; and determining the first distribution variance based on the first lateness rate and / or the first modification rate.

[0011] In one embodiment of this application, the determination of the second distribution expectation value and the second distribution variance includes: determining the third outbound time of the current surface layer material based on the inbound / outbound reservation data; and determining the second inbound time of the current surface layer material, the second average inbound time of similar materials of the current surface layer material, and the second late arrival rate and the second modification rate of the reserved outbound time of similar materials of the current surface layer material based on the historical inbound / outbound data; determining the fourth outbound time of the current surface layer material based on the second inbound time and the second average inbound time; determining the second distribution expectation value based on the third outbound time and / or the fourth outbound time; and determining the second distribution variance based on the second late arrival rate and / or the second modification rate.

[0012] In one embodiment of this application, calculating the conflict risk based on the first probability distribution model and the second probability distribution model includes: determining the retention probability of the material to be stored at each time point based on the first probability distribution model; determining the temporal conflict probability of the material to be stored remaining in the storage area when the current surface layer material is discharged based on the second probability distribution model and the retention probability of the material to be stored at each time point; determining the temporal conflict probability under each preset number of deduction steps, and applying a corresponding time decay weight to the temporal conflict probability under each deduction step, wherein the time decay weight decreases as the number of deduction steps increases; and summing the temporal conflict probabilities after weighting by the time decay weight under all deduction steps to obtain the conflict risk.

[0013] In one embodiment of this application, before determining the target storage location for stacking the materials to be stored from the plurality of candidate storage locations, the method further includes: in the virtual simulation environment, for each candidate storage location, determining the moving distance, lifting stroke, and operation time for the handling equipment to complete the stacking operation of the materials to be stored; and determining the physical cost corresponding to each candidate storage location based on the moving distance, the lifting stroke, and the operation time, so as to determine the target storage location from the plurality of candidate storage locations by combining the physical cost.

[0014] This application also provides a material stacking storage location allocation system, the system comprising: a data acquisition module for acquiring storage area status data; a construction module for constructing a digital twin base for the storage area and synchronizing the storage area status data to the digital twin base to construct a virtual simulation environment for storage location allocation simulation; a simulation module for determining multiple candidate storage locations in the virtual simulation environment, and for each candidate storage location, assessing the risk of conflict between the outbound timing of the material to be stored and the current surface layer material when the material to be stored is stacked as the upper layer material; and a screening module for determining the target storage location for stacking the material to be stored from the multiple candidate storage locations based on the conflict risk corresponding to each candidate storage location.

[0015] The beneficial effects of this application are as follows: The material stacking location allocation method and system provided by this application first acquires the warehouse area status data, then constructs a digital twin base for the warehouse area and synchronizes the warehouse area status data to the digital twin base, constructs a virtual simulation environment for location allocation simulation, then determines multiple candidate locations in the virtual simulation environment, and for each candidate location, assesses the conflict risk of the outbound timing between the material to be stored and the current surface layer material when the material to be stored is stacked as the upper layer material, and finally determines the target location for stacking the material to be stored from multiple candidate locations based on the conflict risk corresponding to each candidate location. By constructing a digital twin base and virtual simulation environment for the warehouse area, the actual situation of the warehouse area can be reflected in real time, and the stacking situation of different candidate locations can be simulated in the virtual simulation environment, the conflict risk of the outbound timing between the material to be stored and the existing surface layer material can be assessed in advance, and the target location can be selected based on the conflict risk, which can effectively avoid material burial and reduce unnecessary stacking operations, thereby improving the overall turnover efficiency of warehousing. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0017] In the attached diagram: Figure 1 This is a schematic diagram illustrating the implementation environment of a material stacking location allocation system, as shown in an exemplary embodiment of this application. Figure 2 This is a schematic flowchart illustrating a material stacking location allocation method according to an exemplary embodiment of this application; Figure 3 This is a schematic flowchart illustrating a specific material stacking location allocation method according to an exemplary embodiment of this application; Figure 4 This is a block diagram illustrating a material stacking location allocation system as shown in an exemplary embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0018] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0019] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. The drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the shape, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0020] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the present application. However, it will be apparent to those skilled in the art that embodiments of the present application may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the present application.

[0021] In industrial manufacturing, automated warehousing and stacking decision-making is a core component of intelligent logistics, aiming to achieve efficient material storage and turnover while meeting complex constraints such as load-bearing capacity, layer height, and material compatibility. With the continuous increase in modern industrial capacity and the significant acceleration of production logistics, warehousing scenarios are exhibiting complex characteristics of high-density storage and highly dynamic operations, thus placing stricter demands on the real-time performance, robustness, and foresight of stacking decisions. Currently, material stacking location allocation largely relies on heuristic algorithms such as prioritizing empty spaces based on static snapshots of current inventory. However, the inventors of this application have found that this allocation not only deviates from the current actual state of the warehouse area but also lacks consideration for the material's lifespan within the warehouse. In complex operating conditions and long-cycle operations, this easily leads to material burial and frequent repackaging, severely hindering the smooth execution of warehousing operations.

[0022] Therefore, please see Figure 1 , Figure 1 This is a schematic diagram illustrating an implementation environment of a material stacking location allocation system, as shown in an exemplary embodiment of this application. Figure 1 As shown, the implementation environment may include a material stacking location allocation system 110 and a computer device 120. The material stacking location allocation system 110 can be set up within the computer device 120 to realize material stacking location allocation. This material stacking location allocation system 110, by constructing a digital twin base and virtual simulation environment for the storage area, can reflect the actual situation of the storage area in real time. In the virtual simulation environment, it can simulate the stacking situation of different candidate storage locations, assess in advance the risk of time sequence conflicts between materials to be stored and existing surface materials, and then select target storage locations based on the conflict risk. This can effectively avoid material burial and reduce unnecessary repacking operations, thereby improving the overall turnover efficiency of warehousing.

[0023] Please see Figure 2 , Figure 2 This is a schematic flowchart illustrating a material stacking location allocation method according to an exemplary embodiment of this application. This method can be applied to... Figure 1 The implementation environment shown is specifically executed by the material stacking location allocation system 110 within that implementation environment. It should be understood that this method can also be applied to other exemplary implementation environments and executed by devices in other implementation environments; this embodiment does not limit the implementation environment to which the method is applicable.

[0024] like Figure 2 As shown, in an exemplary embodiment, the material stacking location allocation method includes at least steps S210 to S240, which are described in detail below: Step S210: Obtain warehouse status data.

[0025] Among them, the warehouse status data refers to various information reflecting the current actual status of the warehouse, including but not limited to the locking status of each warehouse location, the three-dimensional spatial coordinates of each warehouse location, the three-dimensional spatial coordinates of the stacked materials in each warehouse location, the secondary material information of each stacked material in the warehouse, the layout of the warehouse, and the location, horizontal speed, lifting speed, and virtual collision and spatiotemporal occupancy of the handling equipment.

[0026] It should be noted that the locked status refers to whether the storage location is currently occupied, reserved, under maintenance, or temporarily unavailable due to other management policies. It includes locked and unlocked. Locked means unavailable, and unlocked means available.

[0027] Step S220: Construct a digital twin base for the storage area and synchronize the storage area status data to the digital twin base to build a virtual simulation environment for storage location allocation simulation.

[0028] Among them, the digital twin base refers to the virtual mapping of the warehouse area in the physical space to the digital space, which can reflect the layout, equipment and materials of the physical warehouse area in real time or near real time; the virtual simulation environment refers to a simulation platform built on the basis of the digital twin base for simulating and predicting the future state of the warehouse area. In this environment, different warehouse location allocation strategies can be virtually tested and their impact evaluated without having to operate in the actual physical warehouse area.

[0029] In one possible embodiment, the digital twin base is an incrementally isolated simulation base. It collects the state of the warehouse area in the physical space in real time through an industrial fieldbus and maps it to the baseline layer of the in-memory database. The data in the baseline layer serves as a mirror of the physical world and remains in a globally read-only state within a single simulation. The baseline layer is updated at the end of each simulation to ensure the consistency between the physical state and the virtual simulation state. When a simulation is started, a unique simulation branch identifier is assigned to each heuristic strategy individual, and an incremental view logically isolated from the baseline layer is constructed in the in-memory database based on key prefix indexing technology.

[0030] In one possible embodiment, a hierarchical data access mechanism is established for the incremental isolation simulation base. When the simulation kernel reads the state, it first queries the private state space with the current branch identifier prefix. If it does not exist, it queries the base layer. When the simulation kernel writes the state, it executes a copy-on-write strategy, recording state changes only in the private space. It supports large-scale parallel simulation based on a single physical memory, breaks through the memory resource bottleneck, and can meet the real-time decision-making requirements.

[0031] Step S230: In the virtual simulation environment, multiple candidate storage locations are determined, and for each candidate storage location, when the material to be stored is stacked as the upper layer material, the risk of conflict between the outbound timing of the material to be stored and the current surface layer material is assessed.

[0032] Among them, candidate storage locations refer to a group of storage locations selected from all available storage locations based on preliminary screening criteria during the allocation of material storage locations; current surface layer material refers to the material currently located on the top layer of the stack in a candidate storage location and that can be directly retrieved by handling equipment; the risk of conflict in outbound timing refers to the possible sequence conflict between the expected outbound time of the material to be stored and the expected outbound time of the current surface layer material in that candidate storage location when the material to be stored is stacked on a candidate storage location. For example, if the expected outbound time of the current surface layer material is earlier than the expected outbound time of the material to be stored, a timing conflict will occur, and a stacking operation may be required.

[0033] It should be noted that a multi-branch parallel simulation strategy is used when performing simulations for each candidate storage location. Furthermore, in the virtual simulation environment, if there is only one candidate storage location, that single candidate storage location is directly designated as the target storage location.

[0034] In one possible embodiment, the filtering rules and cost algorithms in warehouse allocation are encapsulated into an independent, standardized decision-making kernel. This kernel is configured to be invoked by both the physical control system and the digital twin system, ensuring that the simulation and production use completely consistent computational logic.

[0035] In one embodiment, the method for determining candidate storage locations includes: obtaining the locking status of each storage location in the storage area and the first material information of the material to be stored; determining whether the locking status of each storage location is unlocked, and determining whether each storage location has space for stacking the material to be stored based on the first material information; if the locking status of any storage location is unlocked and it has space, then any storage location is determined as a candidate storage location.

[0036] The first material information includes the quantity and size of the material, with the size including length, width and height.

[0037] In this embodiment, when determining candidate storage locations, the availability of the locations is first pre-judged. This efficiently eliminates currently unavailable storage locations in the storage area, avoiding subsequent complex risk assessments of outbound timing conflicts for these invalid locations, thus significantly reducing computational load and resource consumption. Simultaneously, it ensures that all determined candidate storage locations possess basic stacking feasibility, improving the efficiency and accuracy of storage location allocation.

[0038] For example, determining whether each storage location has space for stacking materials to be stored based on the first material information includes: obtaining the remaining available space of each storage location; determining the space occupied by the materials to be stored based on the quantity and size of the materials in the first material information; and determining whether each storage location has space for stacking materials to be stored based on the occupied space and the remaining available space of each storage location.

[0039] The remaining available space can be determined based on the three-dimensional spatial coordinates of the storage location and the three-dimensional spatial coordinates of the materials already stacked in that storage location.

[0040] In one possible embodiment, the method for determining the materials to be put into storage includes: acquiring production scheduling data and inbound / outbound reservation data; determining a first material sequence to be put into storage within a preset time period based on the production scheduling data, wherein the first material sequence is marked with a first estimated storage time for each material; and determining a second material sequence to be put into storage within a preset time period based on the inbound / outbound reservation data, wherein the second material sequence is marked with a second estimated storage time for each material; and determining the materials to be put into storage based on the first estimated storage time and the second estimated storage time.

[0041] Among them, production scheduling data represents the material production plan, which can be obtained from the upstream production scheduling system, and the first material information can be obtained based on the production scheduling data; inbound and outbound reservation data represents the material reservation inbound and outbound situation in the future period, which can be obtained from the downstream logistics reservation system; the preset time can be a preset future prediction time domain, such as the next 4 hours, which can be set according to specific situations and needs.

[0042] As one possible implementation, by integrating production scheduling data and inbound / outbound reservation data, two material sequences with expected inbound times are generated respectively. By combining the time information of both, the materials to be inbound are accurately determined, realizing multi-source information collaborative prediction of inbound tasks and improving the accuracy of the determined materials to be inbound.

[0043] In one embodiment, assessing the conflict risk of outbound timing between materials to be received and current surface layer materials includes: acquiring inbound / outbound reservation data and historical inbound / outbound data; determining, based on the inbound / outbound reservation data and historical inbound / outbound data, a first distribution expectation value and a first distribution variance of the expected outbound time of the materials to be received, and a second distribution expectation value and a second distribution variance of the expected outbound time of the current surface layer materials; generating a first probability distribution model of the expected outbound time of the materials to be received based on a preset probability distribution model, combining the first distribution expectation value and the first distribution variance, and generating a second probability distribution model of the expected outbound time of the current surface layer materials, combining the second distribution expectation value and the second distribution variance; and calculating the conflict risk based on the first probability distribution model and the second probability distribution model.

[0044] Among them, historical inbound and outbound data represent the actual inbound and outbound situation of materials over a period of time, which can be obtained from the warehouse management system; the expected value and variance of the first distribution represent the central trend and fluctuation of the expected outbound time of the materials to be inbound, respectively; the expected value and variance of the second distribution represent the central trend and fluctuation of the expected outbound time of the current surface layer materials, respectively.

[0045] In this embodiment, based on a preset probability distribution model, a probability distribution model for the expected outbound time is generated by combining the expected value and the variance of the distribution. A parameter estimation method (such as maximum likelihood estimation) can be used, that is, the expected value and the variance of the distribution are dynamically fitted into the probability distribution model, thereby generating a probability distribution of outbound time that evolves dynamically over time.

[0046] In this embodiment, by acquiring inbound / outbound reservation data and historical inbound / outbound data, and based on this data determining the expected distribution value and variance of the material's estimated outbound time, a probability distribution model is constructed. This model can accurately quantify the uncertainty of the outbound timing between materials awaiting entry and the current surface layer materials. This timing conflict risk assessment mechanism based on the probability distribution of outbound time, unlike the traditional fixed deadline, provides a more objective and accurate assessment of potential outbound conflicts. Consequently, when allocating storage locations, it can select target storage locations that not only meet stacking requirements but also minimize future outbound operation interference, significantly improving the intelligence level of storage location allocation.

[0047] For example, the inbound / outbound reservation data includes the inbound and outbound times of each reserved material, and the historical inbound / outbound data includes the average inbound time of each material, the lateness rate of the reserved outbound time of each material, the modification rate of the reserved outbound time of each material, and the actual outbound time of each material.

[0048] For example, the preset probability distribution model can be a normal distribution model or a skewed distribution model, which can be determined according to the actual distribution characteristics of the material outbound time. For example, for normal reservation scenarios with strong planning and relatively concentrated outbound times, a normal distribution model can be used, while for temporary outbound scenarios with suddenness and right-skewed outbound times, a skewed distribution model is more suitable.

[0049] Specifically, the determination of the expected value and variance of the first distribution includes: determining the first entry time and the first exit time of the materials to be entered based on the entry and exit reservation data; determining the first average storage time of similar materials to be entered and the first late rate and first modification rate of the scheduled exit time of similar materials to be entered based on historical entry and exit data; determining the second exit time of the materials to be entered based on the first entry time and the first average storage time; determining the expected value of the first distribution based on the first exit time and / or the second exit time; and determining the variance of the first distribution based on the first late rate and / or the first modification rate.

[0050] Among these, "same type of materials" refers to materials of the same type, which can be identified based on material identifiers; "first average in-stock time" refers to the average in-stock time of similar materials awaiting warehousing over a past period; "first late delivery rate" refers to the frequency with which similar materials awaiting warehousing failed to be dispatched at the scheduled time over a past period; and "first modification rate" refers to the frequency with which the scheduled dispatch time of similar materials awaiting warehousing was changed over a past period. Furthermore, the historical data used to calculate the first average in-stock time, first late delivery rate, and first modification rate all come from the same fixed time window.

[0051] It should be noted that the difference between the first outbound time and the second outbound time is that the first outbound time is the planned outbound time of the currently reserved materials to be put into storage, while the second outbound time is the estimated outbound time of the materials to be put into storage calculated based on historical data.

[0052] In this embodiment, for materials awaiting warehousing, combining the scheduled first warehousing time, first outbound time, and the historical average inbound duration of similar materials makes the determination of the expected value of the first distribution more reliable. Simultaneously, by introducing the late arrival rate and modification rate of scheduled outbound times for similar materials in historical operations, the uncertainty and volatility of outbound times can be effectively quantified, thereby accurately determining the variance of the first distribution. It should be understood that higher late arrival rates and modification rates mean greater uncertainty in the expected outbound time of the materials awaiting warehousing, and consequently, a larger variance in the first distribution. This method, combining reservation information and historical statistical data, allows the first probability distribution model of the expected outbound time of materials awaiting warehousing to more realistically reflect the actual situation, thus ensuring the accuracy of subsequent assessments of the risk of outbound timing conflicts between materials awaiting warehousing and current surface layer materials, and effectively reducing the risk of material overturning.

[0053] For example, determining the first distribution expectation value of the expected outbound time of the materials to be received based on the first outbound time and / or the second outbound time includes the following cases: determining the first distribution expectation value of the expected outbound time of the materials to be received based on the first outbound time; or, determining the first distribution expectation value of the expected outbound time of the materials to be received based on the second outbound time; or, determining the first distribution expectation value of the expected outbound time of the materials to be received based on the first outbound time and the second outbound time.

[0054] Specifically, the expected value of the first distribution of the estimated outbound time of the materials to be put into storage is determined based on the first outbound time and the second outbound time. This can be done by using the weighted average of the first outbound time and the second outbound time to determine the expected value of the first distribution, thus making the determination of the expected value of the first distribution more reliable and avoiding the deviation that may be caused by relying solely on a single scheduled outbound time.

[0055] For example, determining the first distribution variance of the expected outbound time of the materials to be received based on the first late arrival rate and / or the first modification rate includes the following cases: determining the first distribution variance of the expected outbound time of the materials to be received based on the first late arrival rate; or, determining the first distribution variance of the expected outbound time of the materials to be received based on the first modification rate; or, determining the first distribution variance of the expected outbound time of the materials to be received based on the first late arrival rate and the first modification rate.

[0056] Specifically, the first distribution variance of the expected outbound time of the materials to be put into storage is determined based on the first late arrival rate and the first modification rate. The expected value of the first distribution of the expected outbound time of the materials to be put into storage can be determined based on the weighted average of the first late arrival rate and the first modification rate, thereby making the determination of the first distribution variance more reliable and avoiding the deviation that may be caused by relying on a single data.

[0057] For example, in the first distribution variance of determining the expected outbound time of materials to be received based on the first late arrival rate and / or the first modification rate, these ratios can be converted into variance values ​​through a preset mapping function.

[0058] Specifically, the determination of the expected value of the second distribution and the variance of the second distribution includes: determining the third outbound time of the current surface layer material based on inbound / outbound reservation data; and determining the second inbound time of the current surface layer material, the second average inbound time of similar materials of the current surface layer material, and the second late rate and second modification rate of the scheduled outbound time of similar materials of the current surface layer material based on historical inbound / outbound data; determining the fourth outbound time of the current surface layer material based on the second inbound time and the second average inbound time; determining the expected value of the second distribution based on the third outbound time and / or the fourth outbound time; and determining the variance of the second distribution based on the second late rate and / or the second modification rate.

[0059] The second average inventory duration refers to the average inventory duration of similar materials on the current surface layer over a past period; the second late delivery rate refers to the frequency with which similar materials on the current surface layer failed to be dispatched at the scheduled time over a past period; and the second modification rate refers to the frequency with which the scheduled dispatch time of similar materials on the current surface layer was changed over a past period. Furthermore, the historical data used to calculate the second average inventory duration, the second late delivery rate, and the second modification rate all come from the same fixed time window, and this fixed time window is the same as the fixed time window used to calculate the first average inventory duration, the first late delivery rate, and the first modification rate.

[0060] It should be noted that the difference between the third and fourth outbound times is that the third outbound time is the planned outbound time for the currently reserved surface layer material, while the fourth outbound time is the estimated outbound time for the current surface layer material calculated based on historical data.

[0061] In this embodiment, when determining the expected value of the second distribution and the variance of the estimated outbound time of the current surface layer material, not only is the scheduled third outbound time considered, but historical inbound and outbound data are also fully utilized. Specifically, this includes the second inbound time of the current surface layer material, the second average inbound time of similar materials, and the second late arrival rate and second modification rate of scheduled outbound times for similar materials. This approach more comprehensively and accurately reflects the distribution characteristics and uncertainties of the actual outbound time of the current surface layer material. It should be understood that a higher second late arrival rate and second modification rate indicate greater uncertainty in the estimated outbound time of the current surface layer material, and consequently, a larger variance in the second distribution. This method, combining reservation information and historical statistical data, allows the second probability distribution model of the estimated outbound time of the current surface layer material to more realistically reflect the actual situation. This ensures the accuracy of subsequent assessments of the risk of outbound timing conflicts between materials awaiting entry and the current surface layer material, effectively reducing the risk of material overturning.

[0062] For example, determining the second distribution expectation value of the current surface layer material's expected outbound time based on the third outbound time and / or the fourth outbound time includes the following cases: determining the second distribution expectation value of the current surface layer material's expected outbound time based on the third outbound time; or, determining the second distribution expectation value of the current surface layer material's expected outbound time based on the fourth outbound time; or, determining the second distribution expectation value of the current surface layer material's expected outbound time based on the third outbound time and the fourth outbound time.

[0063] Specifically, the second distribution expectation value of the current surface layer material's estimated outbound time is determined based on the third and fourth outbound times. This can be done by using the weighted average of the third and fourth outbound times, making the determination of the second distribution expectation value more reliable and avoiding the deviation that may be caused by relying solely on a single scheduled outbound time.

[0064] For example, determining the second distribution variance of the expected outbound time of the current surface layer material based on the second late arrival rate and / or the second modification rate includes the following cases: determining the second distribution variance of the expected outbound time of the current surface layer material based on the second late arrival rate; or, determining the second distribution variance of the expected outbound time of the current surface layer material based on the second modification rate; or, determining the second distribution variance of the expected outbound time of the current surface layer material based on the second late arrival rate and the second modification rate.

[0065] Specifically, the second distribution variance of the expected outbound time of the current surface layer material is determined based on the second late arrival rate and the second modification rate. This can be done by using the weighted average of the second late arrival rate and the second modification rate to determine the second distribution variance, thus making the determination of the second distribution variance more reliable and avoiding the bias that may be caused by relying on a single data point.

[0066] For example, in the second distribution variance of determining the expected outbound time of the current surface layer material based on the second late arrival rate and / or the second modification rate, these ratios can be converted into variance values ​​through a preset mapping function.

[0067] In one possible embodiment, the late delivery rate can be replaced by the late delivery rate range over a period of time. For example, if the monthly late delivery rate of a certain type of material is between 5% and 30% over the past 12 months, then the corresponding late delivery rate range is 25%.

[0068] In one embodiment, the conflict risk is calculated based on a first probability distribution model and a second probability distribution model, including: determining the retention probability of the material to be stored at each time point according to the first probability distribution model; determining the temporal conflict probability of the material to be stored remaining in the storage area when the current surface layer material is discharged, based on the second probability distribution model and the retention probability of the material to be stored at each time point; determining the temporal conflict probability under each preset number of simulation steps, and applying a corresponding time decay weight to the temporal conflict probability under each simulation step, wherein the time decay weight decreases as the number of simulation steps increases; and summing the temporal conflict probabilities after time decay weighting under all simulation steps to obtain the conflict risk.

[0069] In this embodiment, determining the retention probability of materials to be received at each time point based on the first probability distribution model means using the first probability distribution model of the expected outbound time of the materials to be received, and exhaustively calculating the probability that the materials to be received will still remain in the storage area at each future time point by exhaustively listing all time points. Based on the second probability distribution model and the retention probability of materials to be received at each time point, determining the temporal conflict probability of materials to be received remaining in the storage area when the current surface layer material is outbound aims to quantify the possibility that the materials to be received, as upper layer materials, will still not be outbound when the current surface layer material needs to be outbound. To more accurately quantify the potential impact of conflict risk in the future, multiple extrapolation steps are set. By determining the temporal conflict probability under each extrapolation step, and applying a time decay weight that decreases with the increase of extrapolation steps to the temporal conflict probability under each extrapolation step, the temporal conflict probabilities after time decay weighting under all extrapolation steps are finally accumulated to obtain the conflict risk. This conflict risk not only considers the possibility of conflict occurring but also incorporates the time factor of conflict occurrence, representing the total cost of future risk, making the risk assessment results more reliable.

[0070] In this embodiment, the probability of material retention at each time point is first determined, and the probability of the expected outbound time of the current surface layer material is combined to accurately calculate the probability of potential time-series conflicts under different simulation steps. Then, a time decay weight is introduced to give higher weight to conflicts that may occur in the near future, while the weight of conflicts that occur in the long term is reduced accordingly. This makes the final conflict risk value more realistic and dynamic in reflecting the potential impact of time-series conflicts in the warehouse, thereby effectively reducing the risk of material overturning.

[0071] For example, both the first probability distribution model and the second probability distribution model are probability density functions.

[0072] For example, the formula for calculating the probability of timing conflicts is: Equation (1) in, Indicates the probability of timing conflicts; Representing the future time; Indicates the current surface layer material at The probability density function for items leaving the warehouse at any given time; Indicates that the materials to be received into the warehouse are The cumulative distribution function of outbound shipments at any given time is derived from the probability density function of the estimated outbound time of the materials to be received. The result is obtained by integrating over the interval [0, t]. Indicates in The probability that materials awaiting entry into the warehouse have not yet been dispatched, i.e., the probability of being held up, is the time it takes for materials to be dispatched. After that moment.

[0073] For example, the formula for calculating conflict risk is: Equation (2) in, Indicates the risk of conflict; Indicates the preset number of deduction steps; Indicates the first One deduction step; This represents the time decay coefficient, with a value ranging from 0 to 1, for example, 0.8; Indicates the first Time decay weights for timing conflict probabilities in each simulation step; Indicates the first The probability of timing conflicts under each deduction step.

[0074] Step S240: Based on the conflict risk corresponding to each candidate storage location, determine the target storage location for stacking materials to be stored from multiple candidate storage locations.

[0075] Among them, the target storage location refers to the storage location that is finally determined after evaluation and screening for stacking materials to be stored.

[0076] For example, based on the conflict risk corresponding to each candidate storage location, the target storage location for stacking materials to be stored is determined from multiple candidate storage locations, including: determining the candidate storage location corresponding to the minimum conflict risk as the target storage location.

[0077] It should be noted that if multiple candidate storage locations have the same and minimal conflict risk, one of the candidate storage locations can be selected as the target storage location.

[0078] In one embodiment, before determining the target storage location for the stacked material to be stored from multiple candidate storage locations, the method further includes: obtaining second material information of the current surface layer material in each candidate storage location; for each candidate storage location, determining whether the material to be stored and the current surface layer material violate stacking constraints based on the second material information and the first material information of the material to be stored, wherein stacking constraints include material constraints, material pressure constraints, and stacking shape constraints; if the determination result for each candidate storage location is a violation of stacking constraints, then calculating the risk entropy increment corresponding to each candidate storage location, and combining the risk entropy increment to determine the target storage location from multiple candidate storage locations.

[0079] The first material information includes the material type, weight data, and shape characteristics of the material to be received; the second material information includes the material type, load-bearing data, and shape characteristics of the current surface layer material; stacking constraints refer to a series of rules that ensure materials can be stacked safely and stably; material constraints are used to avoid potential risks caused by material incompatibility, such as chemical reactions, corrosion, or contamination; material pressure constraints are used to ensure that the current surface layer material can withstand the weight of the material to be received, avoiding damage to the current surface layer material or instability of the stacking structure due to overweight; stacking shape constraints are used to ensure that the geometric structure formed when two materials are stacked meets the stacking compatibility requirements to ensure the stability of the stack, such as avoiding violation of the trapezoidal stacking structure that leads to an inverted center of gravity; risk entropy increment refers to an indicator for quantitatively assessing the additional risks introduced by violating stacking constraints.

[0080] It should be noted that if there are other materials below the current surface layer material in a candidate storage location, the load-bearing capacity of the current surface layer material and the materials below it should be taken into account.

[0081] In this embodiment, considering that in some cases all evaluated candidate storage locations may violate stacking constraints to varying degrees, the risk entropy increment for each candidate storage location is determined by accurately judging whether there are problems such as material incompatibility, insufficient pressure bearing capacity, or unstable stacking shape. This risk entropy increment is then used to determine the target storage location from multiple candidate locations. Thus, in addition to considering the risk of material outbound sequence conflicts, a rigorous verification of physical stacking constraints is introduced, preventing materials to be stored from being placed in unsuitable locations. Especially in the extreme case where all candidate storage locations violate stacking constraints, intelligent dimensionality reduction generates an effective strategy, avoiding storage capacity deadlock and preventing situations where candidate storage locations cannot be selected, thus ensuring operational continuity.

[0082] For example, when determining the target storage location from multiple candidate storage locations by incorporating risk entropy increment, the objective function is: Equation (3) in, Indicates the overall cost; Indicates the risk of conflict; This indicates an increase in risk entropy; , These represent the weights for conflict risk and risk entropy increment, respectively, and can be set according to specific circumstances and needs.

[0083] In this exemplary embodiment, the candidate storage location corresponding to the minimum overall cost can be determined as the target storage location. If multiple candidate storage locations have the same and minimum overall cost, one of them can be selected as the target storage location.

[0084] In one possible embodiment, if there is a single candidate storage location whose judgment result is that the stacking constraint is not violated, then the single candidate storage location is determined as the target storage location; if there are multiple candidate storage locations whose judgment result is that the stacking constraint is not violated, then the target storage location is determined from the multiple candidate storage locations.

[0085] Specifically, calculating the risk entropy increment for each candidate storage location includes: determining the target constraints violated between the material to be stored and the current surface layer material in the candidate storage location, where the target constraints include at least one of material constraints, material pressure constraints, and stacking shape constraints; determining the target penalty strategy corresponding to the target constraints from a preset penalty strategy, wherein the penalty strategy includes the penalty weight and penalty function corresponding to each constraint; determining the penalty value for each target constraint according to the target penalty strategy; and summing the penalty values ​​corresponding to all target constraints to obtain the risk entropy increment.

[0086] Among them, the penalty weight is used to characterize the importance of the constraint in risk assessment. For example, the material constraint is given the largest penalty weight of 0.5, the material pressure constraint is given a relatively high penalty weight of 0.3, and the stacking shape constraint is given the largest penalty weight of 0.2. The penalty function is used to map the specific degree of violation to a numerical penalty value.

[0087] It should be noted that the penalty weights for each constraint can be the same or different, and can be set according to specific circumstances and needs. The larger the final risk entropy increment, the higher the risk of the corresponding candidate storage location violating stacking constraints, and the less suitable it is as a target storage location.

[0088] In this embodiment, to determine whether the material constraint is violated, the material properties of the two materials are checked for compatibility. If they are incompatible, the material constraint is violated. To determine whether the material pressure constraint is violated, the weight of the material to be stored is compared with the load-bearing capacity of the current surface layer material. If the weight of the material to be stored is greater, the material pressure constraint is violated. To determine whether the stacking shape constraint is violated, the shape matching degree and stacking stability of the two materials are evaluated. If they are mismatched or unstable, the stacking shape constraint is violated.

[0089] In this embodiment, by clearly identifying the specific target constraints that are violated and setting differentiated penalty weights and penalty functions for each constraint, the risk entropy increment corresponding to each candidate storage location can be calculated more precisely. When all candidate storage locations violate stacking constraints, each candidate storage location can be distinguished and sorted according to the severity and importance of different constraints, so as to select the target storage location with the lowest relative risk and most suitable for stacking materials to be stored from multiple candidate storage locations that violate stacking constraints.

[0090] For example, the penalty function for material constraints can be binary or a continuous function based on the degree of incompatibility; the penalty function for material bearing capacity constraints can be a linear or nonlinear function based on the overweight ratio / weight exceeding the safe bearing capacity threshold; and the penalty function for stacking shape constraints can be a linear or nonlinear function based on the area or size of the overhanging upper edge.

[0091] In one possible embodiment, stacking constraints include not only physical constraints between the material to be stored and the current surface layer material, i.e., material constraints, material pressure constraints, and stacking shape constraints, but also physical constraints between the material to be stored and the candidate storage location, such as prohibiting forced stacking without aligning the special pads / supports in the candidate storage location, and may also include physical constraints between the material to be stored and the same type of material or batch of material stacked in other storage locations when the material to be stored is stacked in any candidate storage location, such as the same type of material or batch of material should be forcibly truncated and stored separately.

[0092] Among them, the penalty function for special pads / supports in misaligned candidate storage locations can be a linear or nonlinear function based on the geometric error of form and position mismatch. The penalty function for the forced truncation and discrete storage of materials of the same type or batch can be a linear or nonlinear function based on the number of non-continuous stacking locations / span distance.

[0093] As one possible implementation, by comprehensively considering the multi-dimensional physical constraints between materials, between materials and storage locations, and between similar materials or materials of the same batch across storage locations, it is possible to further select the target storage location with the lowest relative risk and most suitable for stacking materials to be stored from multiple candidate storage locations that violate stacking constraints.

[0094] It should be noted that the multi-layered materials in the same storage location are formed through multiple warehousing and stacking processes. In the virtual simulation environment, each time the material to be stored is placed into a candidate storage location, the topmost uncovered material in that candidate location is used as the current surface layer material. The stacking constraints and outbound timing conflicts between the material to be stored and the current surface layer material are assessed. In the actual physical space, after the material to be stored is stacked, it is updated to the new current surface layer material of the storage location and synchronized to the digital twin base for evaluating subsequent new materials to be stored, thus completing the storage location allocation. Furthermore, since the lower layer material has already participated in the corresponding evaluation as the current surface layer material before being covered by the upper layer material, the storage location allocation calculation does not need to repeatedly calculate all historical layers of the same storage location. Instead, it only calculates the material to be stored and the current surface layer material, thereby reducing computational complexity and ensuring real-time storage location allocation efficiency.

[0095] In one embodiment, before determining the target storage location for stacking materials to be stored from multiple candidate storage locations, the method further includes: in a virtual simulation environment, determining the moving distance, lifting stroke, and operation time for the handling equipment to complete the stacking operation of the materials to be stored for each candidate storage location; and determining the physical cost corresponding to each candidate storage location based on the moving distance, lifting stroke, and operation time, so as to determine the target storage location from multiple candidate storage locations by combining the physical cost.

[0096] The moving distance refers to the path length traversed by the handling equipment in the horizontal direction from its starting position to the candidate storage location, which can be determined based on the storage area layout; the lifting stroke refers to the distance required for the handling equipment to lift or lower the material to be stored to the specified stacking height of the candidate storage location in the vertical direction, which can be determined based on the three-dimensional spatial coordinates of the material currently stacked in the candidate storage location; the operation time refers to the total time required for the handling equipment to complete the stacking operation of the material to be stored, including the moving time, lifting time, material grabbing time, material placement time, and necessary waiting or adjustment time (such as avoidance waiting time), which can be determined based on the moving distance, lifting stroke, horizontal speed, lifting speed, and virtual collision and spatiotemporal occupancy of the handling equipment.

[0097] In this embodiment, when evaluating the storage location allocation scheme, not only are the conflict risks of material outbound sequence and stacking constraints considered, but also the physical costs generated by the handling equipment during the stacking operation are introduced. This allows for the selection of a target storage location with better overall performance from multiple candidate storage locations, effectively reducing the cost of stacking operations.

[0098] For example, the physical cost corresponding to each candidate storage location can be determined by first normalizing the travel distance, lifting stroke, and operation time, and then performing a weighted summation; or by substituting the travel distance, lifting stroke, and operation time into a predefined cost function for calculation.

[0099] For example, when determining the target storage location from multiple candidate storage locations by incorporating physical costs, the objective function can be: Equation (4) in, Indicates the overall cost; Indicates physical cost; Indicates the risk of conflict; , These represent the weights for physical costs and conflict risks, which can be set according to specific circumstances and needs.

[0100] In this exemplary embodiment, the candidate storage location corresponding to the minimum overall cost can be determined as the target storage location. If multiple candidate storage locations have the same and minimum overall cost, one of them can be selected as the target storage location.

[0101] For example, when determining the target storage location from multiple candidate storage locations by incorporating physical costs, the objective function can also be: Equation (5) in, Indicates the overall cost; Indicates physical cost; Indicates the risk of conflict; This indicates an increase in risk entropy; , , These represent the weights of physical cost, conflict risk, and risk entropy increment, respectively, and can be set according to specific circumstances and needs.

[0102] In this exemplary embodiment, the candidate storage location corresponding to the minimum overall cost can be determined as the target storage location. If multiple candidate storage locations have the same and minimum overall cost, one of them can be selected as the target storage location.

[0103] In one possible embodiment, after obtaining the target storage location, the method further includes: generating an inbound control instruction for the materials to be stored based on the storage location information of the target storage location; and sending the inbound control instruction to the equipment control layer to drive the handling equipment to complete the stacking of the materials to be stored.

[0104] In one possible embodiment, during the simulation, the standardized decision-making kernel is placed in a virtual simulation mode, in which the determined target storage location is directly mapped to the incremental virtual state space to drive the state change of virtual materials without triggering electrical signals of physical equipment.

[0105] In one possible embodiment, after determining the first material sequence and the second material sequence, the sequence of materials to be stored can be determined based on the first estimated storage time of each material marked in the first material sequence and the second estimated storage time of each material marked in the second material sequence. In the virtual simulation environment, after completing the storage location allocation of the previous material to be stored, the storage area status data needs to be updated before the storage location allocation of the next material to be stored is carried out.

[0106] In one possible embodiment, after the handling equipment completes the stacking of materials to be stored in the physical space according to the storage control command, the method further includes: collecting the residual between the actual pose of the handling equipment and the pose in the virtual simulation state and / or the residual between the actual pose of the material and the pose in the virtual simulation state; when the residual exceeds the preset tolerance, the storage area state data in the digital twin base is dynamically corrected using the residual to eliminate the cumulative impact of physical execution error on simulation accuracy, realize virtual-real closed loop, and ensure high confidence of simulation.

[0107] The aforementioned material stacking location allocation method first acquires warehouse area status data, then constructs a digital twin base for the warehouse area and synchronizes the warehouse area status data to the digital twin base. A virtual simulation environment is then built for location allocation simulation. Within this virtual simulation environment, multiple candidate locations are identified. For each candidate location, when the material to be received is stacked as the upper layer material, the risk of conflict between the material to be received and the current surface layer material is assessed. Finally, based on the conflict risk corresponding to each candidate location, the target location for stacking the material to be received is determined from the multiple candidate locations. By constructing a digital twin base and a virtual simulation environment for the warehouse area, the actual situation of the warehouse area can be reflected in real time. In the virtual simulation environment, the stacking situation of different candidate locations can be simulated, and the risk of conflict between the material to be received and the existing surface layer material's outbound timing can be assessed in advance. Based on the conflict risk, the target location is then selected, effectively avoiding material burial and reducing unnecessary repacking operations, thereby improving the overall turnover efficiency of warehousing.

[0108] Please see Figure 3 , Figure 3 This is a schematic flowchart illustrating a specific material stacking location allocation method as shown in an exemplary embodiment of this application, such as... Figure 3 As shown, the specific method for allocating material stacking locations is detailed below: Obtain warehouse status data and construct an incremental isolation simulation base for the warehouse area; synchronize the warehouse status data to the incremental isolation simulation base to construct a virtual simulation environment; in the virtual simulation environment, determine multiple candidate locations; calculate the conflict risk corresponding to each candidate location; determine whether each candidate location violates stacking constraints; if not, directly calculate the physical cost corresponding to each candidate location; if so, first calculate the risk entropy increment corresponding to each candidate location, then calculate the physical cost corresponding to each candidate location; determine the target location based on conflict risk and physical cost, or, determine the target location based on conflict risk, risk entropy increment, and physical cost; issue an inbound control command to drive the handling equipment to complete the stacking of materials to be inbound; after stacking is completed, perform residual calibration.

[0109] In this way, by constructing an incremental isolation simulation base based on key prefix indexing technology, the resource bottleneck problem of high-frequency simulation and inference is solved. Simultaneously, based on conflict risk prediction according to outbound time sequence, and by performing hard constraint dynamic dimensionality reduction through risk entropy increment under extreme operating conditions, a fully closed-loop system is constructed, from probabilistic prediction to virtual pre-simulation to physical execution. This achieves a leap in warehouse location allocation from being based on the current state to being based on the future situation, significantly reducing the turnover rate and deadlock probability in long-cycle operations. This material stacking warehouse location allocation method, with its forward-looking pre-simulation capabilities, isomorphic virtual and physical logic, and flexible ability to cope with extreme constraints, is particularly suitable for high-density, multi-constraint complex warehousing scenarios such as steel, ports, and heavy manufacturing industries, to meet the efficient and robust operational needs of modern industrial warehousing.

[0110] Please see Figure 4 , Figure 4 This is a block diagram illustrating a material stacking location allocation system, as shown in an exemplary embodiment of this application. The system can be applied to... Figure 1 The implementation environment shown is intended to illustrate the system, but it should be understood that the system can also be applied to other exemplary implementation environments. This embodiment does not limit the implementation environment to which the system is applicable.

[0111] like Figure 4 As shown, in an exemplary embodiment, the material stacking location allocation system 400 includes at least a data acquisition module 410, a data construction module 420, a data deduction module 430, and a data filtering module 440, which are described in detail below: The data acquisition module 410 is used to acquire warehouse status data; Module 420 is used to build a digital twin base for the storage area and synchronize the storage area status data to the digital twin base to build a virtual simulation environment for storage location allocation simulation; The simulation module 430 is used to determine multiple candidate storage locations in a virtual simulation environment, and for each candidate storage location, when the material to be stored is stacked as the upper layer material, to assess the risk of conflict between the outbound timing of the material to be stored and the current surface layer material. The screening module 440 is used to determine the target storage location for stacking materials to be stored from multiple candidate storage locations based on the conflict risk corresponding to each candidate storage location.

[0112] It should be noted that the material stacking location allocation system provided in the above embodiments and the material stacking location allocation method provided in the above embodiments belong to the same concept. The content of the operation of each module has been described in detail in the method embodiments, and will not be repeated here.

[0113] This application also provides an electronic device, including: a processor; and a storage device for storing a program, which, when executed by the processor, causes the electronic device to implement the material stacking location allocation method described above.

[0114] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Figure 5 A schematic diagram of a computer system suitable for implementing the embodiments of this application is shown. It should be noted that... Figure 5 The computer system 500 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0115] like Figure 5 As shown, the computer system 500 includes a Central Processing Unit (CPU) 501, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, based on programs stored in Read-Only Memory (ROM) 502 or programs loaded from storage portion 508 into Random Access Memory (RAM) 503. The RAM 503 also stores various programs and data required for system operation. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0116] The following components are connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. Removable media 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 510 as needed so that computer programs read from them can be installed into storage section 508 as needed.

[0117] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by central processing unit (CPU) 501, it performs various functions defined in the system of this application.

[0118] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a computer's processor, causes the computer to perform the material stacking location allocation method described above. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not deployed within that electronic device.

[0119] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0120] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for allocating material stacking locations, characterized in that, The method includes: Obtain warehouse status data; Construct a digital twin base for the storage area and synchronize the storage area status data to the digital twin base to build a virtual simulation environment for storage location allocation simulation; In the virtual simulation environment, multiple candidate storage locations are identified, and for each candidate storage location, when the material to be stored is stacked as the upper layer material, the risk of conflict between the outbound timing of the material to be stored and the current surface layer material is assessed. Based on the conflict risk corresponding to each of the candidate storage locations, a target storage location for stacking the materials to be stored is determined from the plurality of candidate storage locations.

2. The material stacking location allocation method according to claim 1, characterized in that, The method for determining the candidate storage location includes: Obtain the locking status of each storage location in the storage area and the first material information of the material to be stored; Determine whether the locking status of each storage location is unlocked, and based on the first material information, determine whether each storage location has space for stacking the materials to be stored; If any storage location is unlocked and has the required space, then that storage location is identified as a candidate storage location.

3. The material stacking location allocation method according to claim 1, characterized in that, Before determining the target storage location for stacking the materials to be stored from the plurality of candidate storage locations, the method further includes: Obtain the second material information of the current surface layer material in each of the candidate storage locations; For each candidate storage location, based on the second material information and the first material information of the material to be stored, it is determined whether the material to be stored violates the stacking constraints between the material to be stored and the current surface layer material. The stacking constraints include material constraints, material pressure constraints, and stacking shape constraints. If the judgment result for each candidate storage location is a violation of the stacking constraint, then the risk entropy increment for each candidate storage location is calculated, and the target storage location is determined from the multiple candidate storage locations by combining the risk entropy increment.

4. The material stacking location allocation method according to claim 3, characterized in that, The calculation of the risk entropy increment for each candidate storage location includes: Determine the target constraints violated between the material to be stored in the candidate storage location and the current surface layer material, wherein the target constraints include at least one of the material constraints, the material pressure constraints, and the stacking shape constraints; The target penalty strategy corresponding to the target constraint is determined from the preset penalty strategies, wherein the penalty strategy includes the penalty weight and penalty function corresponding to each constraint; For each of the aforementioned target constraints, a penalty value is determined according to the target penalty strategy; The risk entropy increment is obtained by summing the penalty values ​​corresponding to all the target constraints.

5. The material stacking location allocation method according to claim 1, characterized in that, The assessment of the risk of conflict in the outbound timing between the materials to be received and the current surface layer materials includes: Retrieve inbound / outbound reservation data and historical inbound / outbound data; Based on the inbound / outbound reservation data and the historical inbound / outbound data, determine the first distribution expectation value and the first distribution variance of the expected outbound time of the materials to be put into storage, and the second distribution expectation value and the second distribution variance of the expected outbound time of the current surface layer materials; Based on a preset probability distribution model, a first probability distribution model for the expected outbound time of the material to be put into storage is generated by combining the expected value of the first distribution and the variance of the first distribution. A second probability distribution model for the expected outbound time of the current surface layer material is generated by combining the expected value of the second distribution and the variance of the second distribution. The conflict risk is calculated based on the first probability distribution model and the second probability distribution model.

6. The material stacking location allocation method according to claim 5, characterized in that, The methods for determining the expected value and variance of the first distribution include: Based on the inbound and outbound reservation data, the first inbound time and the first outbound time of the material to be inbound are determined. Based on the historical inbound and outbound data, the first average inbound time of similar materials to be inbound and the first late rate and the first modification rate of the scheduled outbound time of similar materials to be inbound are determined. The second outbound time of the material to be received is determined based on the first inbound time and the first average inbound time. Based on the first outbound time and / or the second outbound time, determine the expected value of the first distribution, and based on the first late delivery rate and / or the first modification rate, determine the variance of the first distribution.

7. The material stacking location allocation method according to claim 5, characterized in that, The methods for determining the expected value and variance of the second distribution include: Based on the inbound and outbound reservation data, the third outbound time of the current surface layer material is determined, and based on the historical inbound and outbound data, the second inbound time of the current surface layer material, the second average inbound time of similar materials of the current surface layer material, and the second late rate and second modification rate of the reserved outbound time of similar materials of the current surface layer material are determined. The fourth outbound time of the current surface layer material is determined based on the second inbound time and the second average inbound time. The expected value of the second distribution is determined based on the third outbound time and / or the fourth outbound time, and the variance of the second distribution is determined based on the second late delivery rate and / or the second modification rate.

8. The material stacking location allocation method according to claim 5, characterized in that, The step of calculating the conflict risk based on the first probability distribution model and the second probability distribution model includes: Based on the first probability distribution model, the retention probability of the materials to be put into storage at each time point is determined; Based on the second probability distribution model and the retention probability of the material to be put into storage at each time point, determine the temporal conflict probability of the material to be put into storage remaining in the storage area when the current surface layer material is released from the storage area; Within a preset number of simulation steps, the probability of timing conflict under each simulation step is determined, and a corresponding time decay weight is applied to the probability of timing conflict under each simulation step. The time decay weight decreases as the number of simulation steps increases. The conflict risk is obtained by summing the time-decayed probability after weighting all simulation steps.

9. The material stacking location allocation method according to any one of claims 1 to 8, characterized in that, Before determining the target storage location for stacking the materials to be stored from the plurality of candidate storage locations, the method further includes: In the virtual simulation environment, for each candidate storage location, the moving distance, lifting stroke, and operation time of the handling equipment to complete the stacking operation of the materials to be stored are determined; Based on the travel distance, the lifting stroke, and the operation time, the physical cost corresponding to each candidate storage location is determined, and the target storage location is determined from the plurality of candidate storage locations by combining the physical cost.

10. A material stacking location allocation system, characterized in that, The system includes: The data acquisition module is used to obtain the status data of the storage area; The construction module is used to build a digital twin base for the storage area and synchronize the status data of the storage area to the digital twin base to build a virtual simulation environment for storage location allocation simulation; The simulation module is used to determine multiple candidate storage locations in the virtual simulation environment, and for each candidate storage location, when the material to be stored is stacked as the upper layer material, to assess the risk of conflict between the outbound timing of the material to be stored and the current surface layer material. The screening module is used to determine the target storage location for stacking the materials to be stored from the plurality of candidate storage locations based on the conflict risk corresponding to each candidate storage location.

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