Intelligent stereoscopic warehouse management method and device and computer program product

Abstract

The invention discloses an intelligent stereoscopic warehouse management method and device and a computer program product, and the method comprises the steps: collecting the state data of each space unit of a warehouse in real time, and building and updating a digital twinborn model based on the state data; a space-time state incidence matrix is calculated according to the distance between the space units and the state change rate difference of the space units; dynamically updating the state of each space unit based on the space-time state incidence matrix and the current state of each space unit; calculating the resource load of the space unit based on the updated space unit state and task demand; when it is detected that the resource load exceeds a preset threshold value, it is judged that resource conflicts occur, and a task scheduling strategy is dynamically adjusted. According to the method, the problems of low operation efficiency and poor adaptability caused by lack of space-time relevance in space unit state modeling, inaccurate task resource demand calculation, scheduling mechanism staticization and insufficient resource conflict coping of a traditional stereoscopic warehouse are solved, and dynamic optimization management and efficient resource utilization of the stereoscopic warehouse are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent warehousing and logistics automation, in particular to an intelligent stereoscopic warehouse management method, device and computer program product. BACKGROUND

[0002] With the rapid development of the logistics industry, manufacturing industry and e-commerce warehousing, the requirements for warehousing efficiency and space utilization are increasing. As a core facility for efficient warehousing, the stereoscopic warehouse has been widely used in various automated warehousing systems due to its significant advantages in space utilization and operation efficiency. However, traditional stereoscopic warehouse management and scheduling mainly rely on preset rules and manual intervention, which has obvious limitations in dealing with dynamic changes in warehouse environment and multi-task concurrent execution scenarios.

[0003] In terms of technical implementation, the existing stereoscopic warehouse system has many shortcomings: first, the warehouse space unit state modeling method is too single, only considering a single space unit in isolation, and failing to deeply explore the spatio-temporal correlation characteristics between units, resulting in difficulty in realizing dynamic optimization of warehouse space resources; second, the task resource demand calculation lacks refinement, ignoring the precise matching of tasks and space units in time and space dimensions, making resource allocation unreasonable and easily causing resource waste and operation conflicts; third, the scheduling mechanism mainly adopts static or rule-driven mode, which cannot real-time feedback and flexibly adjust resource allocation according to the actual running state of the system, resulting in insufficient applicability of the scheduling scheme under complex working conditions; fourth, the resource conflict identification and processing means are not perfect, lacking effective coping strategies when facing sudden situations such as device overload and task delay, which seriously affects the overall operation efficiency and system stability of the warehouse.

[0004] In recent years, digital twin technology, as a leading technology that integrates physical systems and virtual models, can holographically map and real-time synchronize the entity warehouse, and has strong simulation prediction, data fusion and dynamic control capabilities. Applying digital twin technology to intelligent stereoscopic warehouse management can realize accurate perception and rapid response to resource status in the warehouse, and build an intelligent, efficient and self-adaptive operation system for the stereoscopic warehouse system, which has become an important direction and inevitable trend to promote the development of intelligent warehousing technology. SUMMARY

[0005] The technical problem to be solved by the embodiments of the present application is to provide an intelligent stereoscopic warehouse management method, device and computer program product to realize dynamic optimization management and efficient resource utilization of the stereoscopic warehouse.

[0006] To solve the above technical problems, the present application provides an intelligent stereoscopic warehouse management method, comprising:

[0007] Step S1: collect status data of each space unit in the warehouse in real time, and build and update the digital twin model based on the status data;

[0008] Step S2, calculating the spatiotemporal state correlation matrix based on the distances between the spatial units and the differences in their state change rates;

[0009] Step S3, dynamically updating the state of each spatial unit based on the spatiotemporal state association matrix and the current state of each spatial unit;

[0010] Step S4, based on the updated spatial unit status and task requirements, calculate the resource load of the spatial unit; when it is detected that the resource load exceeds the preset threshold, determine that a resource conflict occurs, and dynamically adjust the task scheduling strategy.

[0011] Preferably, in step S1, the status data includes the availability of shelf locations, equipment operating status, and temperature and humidity.

[0012] Preferably, in step S2, the elements of the spatiotemporal state association matrix are calculated using the following formula:

[0013]

[0014] Among them, A ij (t) represents the space unit x in the warehouse at time t i For spatial unit x j The influence of x i and x j They are the space units x in the warehouse i and x j The position or entity of the space unit; λ is the proportional coefficient, which is used to control the influence between the space units; D max is the maximum distance scale of the warehouse; |x i -x j | is the spatial unit x i and x j The spatial distance between them; α is the parameter for adjusting the spatiotemporal attenuation; v i (t) and v j (t) are spatial units x i and x j The rate of state change at time t.

[0015] Preferably, in step S3, the status update of each spatial unit is calculated by the following formula:

[0016]

[0017] Among them, s i (t+1) represents the state of the i-th space unit in the warehouse at time t+1; si (t) represents the state of the i-th spatial unit in the warehouse at time t; β is a feedback coefficient for controlling the degree of influence of the spatio-temporal feedback on the state update; s j (t) is the state of the j-th spatial unit at time t.

[0018] Preferably, in the step S4, the resource demand Φ ki (t) is calculated by the following formula:

[0019]

[0020] wherein Φ ki (t) represents the resource demand of the task T k at time t for the spatial unit x i ; δ ki is an indicator variable representing whether the task T k occupies the spatial unit x i ; if the task T k occupies the spatial unit x i , then δ ki = 1, otherwise δ ki = 0; μ ki is the resource contribution degree of the spatial unit x i to the task T k ; γ is a time decay factor representing the influence of the task execution duration on the resource demand; t k is the execution time of the task T k .

[0021] Preferably, in the step S4, the resource load of the spatial unit is calculated by the following formula:

[0022]

[0023] wherein ρ i (t) is the total resource load of the spatial unit x i at time t, representing the total amount of resources occupied by all tasks at time t in the spatial unit x i ; m is the total number of tasks; ω k is the weight of the task T k , representing the importance or priority of the task.

[0024] Preferably, in the step S4, the resource conflict is determined by a resource conflict determination function, which is:

[0025]

[0026] wherein Ω ij (t) is the resource conflict of the spatial unit x i and xj a resource conflict determination function of the space unit x i and x j whether a resource conflict occurs, Ω ij (t) = 1 indicates that there is a resource conflict, Ω ij (t) = 0 indicates that there is no resource conflict; ρ j (t) is the total resource load of the space unit x j at time t, indicating the total amount of resources occupied by all tasks in the space unit x j at time t; C max is the maximum load of the space unit, indicating the maximum amount of resource usage that a space unit x i or x j can carry.

[0027] Preferably, the dynamic adjustment of the task scheduling strategy includes at least one of the following ways:

[0028] reassigning conflicting tasks to idle space units;

[0029] adjusting the execution order of the conflicting tasks;

[0030] delaying the execution time of part of the conflicting tasks.

[0031] The present application also provides an intelligent three-dimensional warehouse management device, comprising:

[0032] one or more processors;

[0033] a memory;

[0034] one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the one or more processors, and the one or more application programs are configured to execute the intelligent three-dimensional warehouse management method.

[0035] The present application also provides a computer program product comprising computer instructions instructing a computer device to perform operations corresponding to the method.

[0036] The present application has the following beneficial effects:

[0037] By introducing a spatiotemporal state association mechanism, considering the position distance and state change rate of the space unit, a dynamic evolution state model is constructed, which can accurately capture the dependency relationship between different space units and realize accurate description and prediction of the overall operation situation of the warehouse. At the same time, relying on the spatiotemporal feedback mechanism, each space unit can adjust based on its current state and respond to the associated influence of other units during state updating, forming an intelligent collaborative space resource network, which significantly improves the flexibility and adaptability of resource utilization.

[0038] Based on the task-based spatio-temporal features, execution timing and real-time state of the spatial unit, the resource demand of the task for each spatial unit is dynamically calculated, the task allocation is more targeted, the resource waste and scheduling delay are effectively avoided, and the overall scheduling quality is improved. At the same time, by constructing a resource load calculation mechanism and a conflict judgment mechanism, the load state of the spatial unit can be monitored in real time, conflicts are automatically identified when the load exceeds the maximum carrying capacity, and real-time intervention is performed through task redistribution, execution order adjustment or delayed execution, etc. The risk of system bottleneck or collapse caused by resource competition is greatly reduced.

[0039] By dynamically adjusting the feedback coefficient and the scheduling strategy, the system can flexibly respond to complex task combinations and sudden demands according to the resource utilization during the operation of the warehouse, enhance the adaptability to variable logistics operation scenarios, and improve the stability and resilience of the system. In addition, with accurate state modeling, efficient task scheduling and flexible resource adjustment mechanism, the operation efficiency of the intelligent three-dimensional warehouse is significantly improved, the task execution period is shortened, the waiting and idle time of equipment and space is reduced, and the utilization rate of warehouse space and equipment is further improved. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0041] Figure 1 is a flowchart of an intelligent three-dimensional warehouse management method according to an embodiment of the present application. DETAILED DESCRIPTION

[0042] The following description of the embodiments is with reference to the drawings, which are used to illustrate specific embodiments in which the present application can be implemented.

[0043] Please refer to Figure 1 The embodiment of the present application provides an intelligent three-dimensional warehouse management method, which comprises:

[0044] Step S1, real-time collection of state data of each spatial unit of the warehouse, and construction and update of a digital twin model based on the state data;

[0045] Step S2, calculation of a spatio-temporal state correlation matrix according to the distance between spatial units and the difference in state change rate thereof;

[0046] Step S3, dynamic update of the state of each spatial unit based on the spatio-temporal state correlation matrix and the current state of each spatial unit;

[0047] Step S4, based on the updated spatial unit status and task requirements, calculate the resource load of the spatial unit; when it is detected that the resource load exceeds the preset threshold, determine that a resource conflict occurs, and dynamically adjust the task scheduling strategy.

[0048] Specifically, in step S1, the status data of each spatial unit in the warehouse is collected in real time through sensors, RFID tags, temperature and humidity sensors, cameras and other equipment deployed in the warehouse. The status data of each spatial unit in the warehouse will be used as the input of the digital twin model for subsequent spatiotemporal feedback mechanism and resource scheduling decisions.

[0049] The status data obtained includes the vacancy of shelf positions, whether the equipment is operating normally, whether the temperature and humidity meet storage requirements, etc. The status data is used to construct the spatiotemporal state model of the spatial unit and synchronize it with the virtual warehouse model in real time, thereby achieving accurate management and efficient scheduling of internal warehouse resources and ensuring optimal utilization of resources.

[0050] In intelligent warehouse management, each spatial unit (such as shelves, storage locations, and equipment) has a specific state. A spatiotemporal state model is constructed to represent the state of each spatial unit at different points in time based on real-time sensor data and historical data. To dynamically adjust resource usage, embodiments of the present invention introduce a spatiotemporal feedback mechanism to ensure that the state of each spatial unit can be effectively adjusted in real time.

[0051] Specifically, in step S2, the state of the warehouse space unit is set to s i (t), represents the state of the i-th space unit in the warehouse at time t (such as the storage status of goods, the operating status of equipment, etc.); the total space state of the warehouse can be expressed as a state vector s(t) = [s1(t), s2(t), ..., s n (t)] T , where n is the number of spatial units in the warehouse and T represents transposition.

[0052] When calculating the spatiotemporal state update of each spatial unit, the key is to consider the spatiotemporal correlation between spatial units. It is necessary to construct a spatiotemporal state association matrix to describe the spatiotemporal dependency between different spatial units in the warehouse. The element A of the spatiotemporal state association matrix is ij (t) represents the space unit x in the warehouse at time t i For spatial unit x j The influence of the spatial distance is not only affected by the spatial distance, but also by the time difference between the two. Therefore, the spatiotemporal state association considers the impact of spatial distance and time delay on resource scheduling. The spatiotemporal state association matrix is ​​calculated using the following formula:

[0053]

[0054] wherein A ij (t) represents the influence degree of space unit x i on space unit x j at time t, x i and x j are the positions or entities of space unit x i and x j respectively; λ is a proportional coefficient for controlling the influence degree between space units, which is set according to the operation requirements of the actual warehouse, and the value range is [0, 1]; D max is the maximum distance scale of the warehouse, which is the maximum distance between all space units in the warehouse; |x i -x j | is the spatial distance between space unit x i and x j ; α is a parameter for adjusting the spatio-temporal attenuation, which represents the influence of time difference on state association, and the value range is [0, 1]; v i (t) and v j (t) are the state change rates of space unit x i and x j at time t respectively.

[0055] When the distance between space units is close (i.e., |x i -x j | is less than a preset threshold), or the state changes of the two are close (i.e., |v i (t)-v j (t)| is less than a preset threshold), the greater the association degree between the space units, the more attention should be paid to their mutual influence.

[0056] In order to realize the spatio-temporal state update of space units, the current state of each space unit and the state of other space units associated with it need to be considered; under the framework of digital twinning technology, each space unit in the warehouse can interact with other space units through real-time data, therefore, the state change of a space unit is not only affected by its own state, but also affected by the feedback of other unit states.

[0057] Specifically, in step S3, when calculating the state of each space unit, the following factors are considered: the state of the space unit will affect its state at the next time step, and the influence is controlled by the feedback coefficient; the state of the space unit will also be affected by the states of other space units, which is quantified by the spatio-temporal association degree matrix. Therefore, the state update formula is:

[0058]

[0059] wherein s i (t+1) represents the state of the i-th space unit in the warehouse at t+1 time, i.e. the updated state; s i (t) represents the state of the i-th space unit in the warehouse at t time (i.e. the state at the current time); β is a feedback coefficient, which controls the degree of influence of the spatio-temporal feedback on the state update, and is obtained through experiments, with a value range of [0, 1]. Through state updating, each space unit of the warehouse will adjust its state according to its own state and the spatio-temporal association with other units after each time step t, so as to realize dynamic optimization of the intelligent three-dimensional warehouse.

[0060] Step S4 further calculates the resource load of each space unit and performs conflict judgment based on the updated space unit state, so as to avoid overload and dynamically adjust the task scheduling strategy, thereby ensuring optimal utilization of resources.

[0061] Each task, when executed, will generate resource demand for the space units in the warehouse, and the resource demand describes the amount of resources required by the space unit when the task is executed; the resource demand of the task for the space unit will be affected by the spatio-temporal distance between the task and the space unit, the time window during task execution, and the current state of the space unit; by analyzing the demand for various resources (such as goods access, equipment use, etc.) in the warehouse, efficient scheduling of resources is realized. A resource demand formula is constructed, which comprehensively considers the spatio-temporal relationship of the task, the priority of the task, and the state of the space unit, reflects the specific demand of the task for the warehouse resources at a specific time, and also considers the spatial distribution and time constraints of the task for the space unit, as well as the load condition of the space unit. The specific formula is:

[0062]

[0063] wherein Φ k (t) represents the resource demand of task T k for space unit x i at t time; δ ki is an indicator variable, which indicates whether task T k occupies space unit x i or not, δ k = 1 if task T i occupies space unit x ki , otherwise δ ki = 0; μ ki is the resource contribution degree of space unit x i to task T k , which is set according to the physical resources in the warehouse and the type of the task; γ is a time decay factor, which represents the influence of the task execution time on the resource demand, and is obtained through experiments, with a value range of [0, 1]; t k is the execution time of task Tk The execution time of the task is as follows: k As the gap between the task and the current time t increases, the task's demand for resources will gradually decrease.

[0064] Based on the resource requirements of each task on the space unit, the resource load function of each space unit is calculated to measure the overall resource load of each space unit in the warehouse. The resource load function represents the total load of the space unit at time t. The calculation formula of the resource load function is as follows:

[0065]

[0066] Among them, ρ i (t) is the spatial unit x i The total resource load at time t is represented by the spatial unit x at time t. i The total amount of resources occupied by all tasks; m is the total number of tasks; ω k It is task T k The weight represents the importance or priority of the task, which is obtained through experiments and ranges from [0,1].

[0067] During resource scheduling, different tasks may conflict with each other in the warehouse's spatial unit. To prevent conflicts from impacting the warehouse's overall operational efficiency, a resource conflict determination function is introduced to determine whether two tasks are overloading the same resource at a given moment. Specifically, tasks may conflict over the same resource. If two tasks simultaneously request resources from the same spatial unit, and the resource load exceeds the maximum capacity of the spatial unit, a conflict is identified.

[0068] When calculating the resource conflict determination function, we first need to obtain the load of each space unit. When the total load of the space unit exceeds the maximum carrying capacity, we adjust the scheduling strategy according to the resource conflict determination function to avoid excessive use of resources and scheduling conflicts. The resource conflict determination function is:

[0069]

[0070] Among them, Ω ij (t) is the spatial unit x i and x j The resource conflict determination function is used to determine the spatial unit x i and x j Whether resource conflict occurs, Ω ij (t) = 1 means there is a resource conflict, Ω ij (t) = 0 means there is no resource conflict; C max is the maximum load of a space unit, representing a space unit x i or xj The maximum resource usage that can be carried.

[0071] When passing Ω ij When the (t) formula determines that there is a resource conflict, the task scheduling is dynamically adjusted according to the conflict situation, including: reallocating tasks to other idle space units, adjusting the execution order of tasks, or temporarily adjusting the load of space units (such as delaying the execution of certain tasks) to ensure the efficient use of resources and ensure that warehouse resources are optimally utilized, thereby improving the efficiency and flexibility of the entire intelligent three-dimensional warehouse management system.

[0072] Corresponding to the intelligent three-dimensional warehouse management method described in the first embodiment of the present invention, the second embodiment of the present invention further provides an intelligent three-dimensional warehouse management device, including:

[0073] one or more processors;

[0074] Memory;

[0075] One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, and the one or more applications are configured to execute the intelligent high-bay warehouse management method described in the aforementioned embodiment 1 of the present invention.

[0076] Corresponding to the intelligent three-dimensional warehouse management method described in the aforementioned embodiment 1 of the present invention, embodiment 3 of the present invention also provides a computer program product, including computer instructions, which instruct the computer device to perform operations corresponding to the intelligent three-dimensional warehouse management method described in the aforementioned embodiment 1 of the present invention.

[0077] Preferably, the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may be any conventional processor. The processor is the control center of the device, and various parts of the device are connected using various interfaces and lines.

[0078] The memory mainly includes a program storage area and a data storage area, wherein the program storage area can store an operating system, application programs required by at least one function, and the like, and the data storage area can store relevant data and the like. In addition, the memory can be a high-speed random access memory, and can also be a non-volatile memory such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, and the like, or the memory can also be other volatile solid-state storage devices.

[0079] It should be noted that the above device can include but is not limited to a processor and a memory.

[0080] As can be seen from the above description, compared with the prior art, the beneficial effects of the present application are that:

[0081] By introducing the spatio-temporal state association mechanism, the position distance and state change rate of the space unit are comprehensively considered, a dynamic evolution state model is constructed, the dependence relationship between different space units can be accurately captured, and the overall operation situation of the warehouse can be accurately described and predicted. At the same time, relying on the spatio-temporal feedback mechanism, each space unit can adjust based on its current state during the state updating process, and can also respond to the associated influence of other units, forming an intelligent collaborative space resource network, which significantly improves the flexibility and adaptability of resource utilization.

[0082] Based on the spatio-temporal characteristics of the task, the execution time, and the real-time state of the space unit, the resource demand of the task for each space unit is dynamically calculated, the task allocation is more targeted, the waste of resources and the delay of scheduling are effectively avoided, and the overall scheduling quality is improved. At the same time, by constructing a resource load calculation mechanism and a conflict judgment mechanism, the load state of the space unit can be monitored in real time, conflicts are automatically identified when the load exceeds the maximum carrying capacity, and real-time intervention is performed through task re-allocation, execution order adjustment or delayed execution, etc., which greatly reduces the risk of system bottleneck or collapse caused by resource competition.

[0083] By dynamically adjusting the feedback coefficient and the scheduling strategy, the system can flexibly respond to complex task combinations and sudden demands according to the resource utilization during the operation of the warehouse, enhance the adaptability to variable logistics operation scenarios, and improve the stability and resilience of the system. In addition, with the precise state modeling, efficient task scheduling and flexible resource adjustment mechanism, the operation efficiency of the intelligent three-dimensional warehouse is significantly improved, the task execution period is shortened, the waiting and idle time of equipment and space is reduced, and the utilization rate of warehouse space and equipment is further improved.

[0084] The above merely provides the preferred embodiment of the application, and cannot allude the protection scope of the application, therefore any equivalent changes made according to the claims of the application shall be within the scope of the application.

Claims

1. An intelligent three-dimensional warehouse management method, characterized in that: include: Step S1: collect status data of each space unit in the warehouse in real time, and build and update the digital twin model based on the status data; Step S2, calculating the spatiotemporal state correlation matrix based on the distances between the spatial units and the differences in their state change rates; Step S3, dynamically updating the state of each spatial unit based on the spatiotemporal state association matrix and the current state of each spatial unit; Step S4, based on the updated spatial unit status and task requirements, calculate the resource load of the spatial unit; when it is detected that the resource load exceeds the preset threshold, determine that a resource conflict occurs, and dynamically adjust the task scheduling strategy.

2. The method according to claim 1, characterized in that In step S1, the status data includes the availability of shelf locations, equipment operating status, and temperature and humidity.

3. The method according to claim 1, characterized in that In step S2, the elements of the spatiotemporal state association matrix are calculated using the following formula: Among them, A ij (t) represents the space unit x in the warehouse at time t i For spatial unit x j The influence of x i and x j They are the space units x in the warehouse i and x j The position or entity of the space unit; λ is the proportional coefficient, which is used to control the influence between the space units; D max is the maximum distance scale of the warehouse; |x i -x j | is the spatial unit x i and x j The spatial distance between them; α is the parameter for adjusting the spatiotemporal attenuation; v i (t) and v j (t) are spatial units x i and x j The rate of state change at time t.

4. The method according to claim 3, characterized in that In step S3, the status update of each spatial unit is calculated using the following formula: Among them, s i (t+1) represents the state of the i-th space unit in the warehouse at time t+1; s i (t) represents the state of the i-th spatial unit in the warehouse at time t; β is the feedback coefficient, which is used to control the influence of spatiotemporal feedback on state update; s j (t) is the state of the j-th spatial unit at time t.

5. The method according to claim 4, characterized in that In step S4, the resource requirement of the task for the space unit Φ ki (t) is calculated by the following formula: Among them, Φ ki (t) represents task T k At time t, the spatial unit x i Resource requirements; ki Is an indicator variable, indicating that task T k Whether to occupy space unit x i , if task T k Occupied space unit x i , then δ ki =1, otherwise δ ki =0;μ ki is the spatial unit x i For Task T k The resource contribution of the task; γ is the time decay factor, which indicates the impact of task execution time on resource demand; t k It is task T k execution time.

6. The method according to claim 5, characterized in that In step S4, the resource load of the space unit is calculated using the following formula: Among them, ρ i (t) is the spatial unit x i The total resource load at time t is represented by the spatial unit x at time t. i The total amount of resources occupied by all tasks; m is the total number of tasks; ω k It is task T k The weight indicates the importance or priority of the task.

7. The method according to claim 6, characterized in that In step S4, resource conflicts are determined by a resource conflict determination function, where the resource conflict determination function is: Among them, Ω ij (t) is the spatial unit x i and x j The resource conflict determination function is used to determine the spatial unit x i and x j Whether resource conflict occurs, Ω ij (t) = 1 means there is a resource conflict, Ω ij (t) = 0 means no resource conflict; ρ j (t) is the spatial unit x j The total resource load at time t is represented by the spatial unit x at time t. j The total amount of resources occupied by all tasks; C max is the maximum load of a space unit, representing a space unit x i or x j The maximum resource usage that can be carried.

8. The method according to claim 1, characterized in that The dynamic adjustment of the task scheduling strategy includes at least one of the following methods: Reassign conflicting tasks to free space units; Adjust the execution order of conflicting tasks; Delay the execution of some conflicting tasks.

9. An intelligent three-dimensional warehouse management device, characterized in that: include: one or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, and the one or more applications are configured to execute the intelligent high-bay warehouse management method according to any one of claims 1 to 8.

10. A computer program product, characterized in that The method comprises computer instructions, wherein the computer instructions instruct a computer device to perform operations corresponding to the method according to any one of claims 1 to 8.

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