AGV (Automatic Guided Vehicle) digital twin battery management system for dynamic storage environment

Abstract

The invention belongs to the field of storage AGV battery management, relates to a data analysis technology, is used for solving the problem that the charging interruption state of a storage AGV cannot be monitored in the prior art, and particularly relates to an AGV digital twin battery management system oriented to a dynamic storage environment. Comprising a storage area segmentation module, a task load analysis module and an interruption monitoring module which are connected in sequence; the warehouse area segmentation module is used for carrying out area segmentation and AGV distribution on the warehouse, wherein the warehouse is segmented into a plurality of warehouse areas, and AGV distribution is carried out in a uniform distribution mode after the warehouse areas are segmented; according to the invention, area segmentation and AGV distribution can be carried out on a warehouse, AGV distribution is carried out by adopting a uniform distribution mode after segmentation is completed, and the load response condition when the AGV executes a subsequent goods storage and taking task is analyzed and evaluated through a prediction analysis model; the overall stored electric quantity of the AGV is guaranteed, and meanwhile the goods storing and taking task can be normally completed.

Description

Technical Field

[0001] The present invention belongs to the field of battery management for warehousing AGVs, involves data analysis technology, and specifically is an AGV digital twin battery management system for a dynamic warehousing environment. Background Art

[0002] The battery management of warehousing AGVs is a key technology to ensure logistics efficiency and equipment reliability. Its core goal is to achieve the efficient utilization and safety control of the entire life cycle of the battery. The battery management of warehousing AGVs has realized the upgrade from passive maintenance to active prediction through the "perception - analysis - optimization" closed-loop, providing a highly reliable energy guarantee for intelligent logistics.

[0003] In a dynamic warehousing environment, the battery management of AGVs is the core issue to ensure logistics efficiency and equipment stability. However, the existing technologies can only manage the AGV batteries from the perspective of task completion rate, but cannot monitor the charging interruption status of warehouse AGVs, resulting in the inability to effectively plan the charging timing of the batteries, incomplete charging and discharging of the batteries, and thus affecting their service life.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the Invention

[0005] The purpose of the present invention is to provide an AGV digital twin battery management system for a dynamic warehousing environment, which is used to solve the problem that the existing technologies cannot monitor the charging interruption status of warehouse AGVs; The technical problem that the present invention needs to solve is: how to provide an AGV digital twin battery management system for a dynamic warehousing environment that can monitor the charging interruption status of warehouse AGVs.

[0006] The purpose of the present invention can be achieved through the following technical solutions: An AGV digital twin battery management system for a dynamic warehousing environment includes a warehousing area segmentation module, a task load analysis module, and an interruption monitoring module that are connected in sequence; The warehousing area segmentation module is used for area segmentation of the warehouse and AGV allocation: the warehouse is segmented into several warehousing areas, and after the warehousing area segmentation is completed, the AGVs are allocated in a uniform distribution manner; The task load analysis module is used to evaluate and analyze the load of the warehouse AGV when performing storage and retrieval tasks: when each storage and retrieval task is completed in the warehousing area, the data of the next two tasks to be executed are retrieved. The data of the tasks to be executed include the task start time, the task execution duration, and the quantity of stored and retrieved goods. The power data of the AGV at the start time of the prediction period is retrieved. The data of the tasks to be executed and the power data of the AGV are input into the prediction analysis model. The prediction analysis model predicts the charging time and charging status of the AGV and marks the first execution object, the charging object, the regression object, the second execution object, and the prediction interruption coefficient; The interruption monitoring module is used to monitor and analyze the charging interruption status of the AGV and obtain the actual interruption coefficient, and determine whether the task execution load rate meets the requirements through the actual interruption coefficient; the absolute value of the difference between the predicted interruption coefficient and the actual interruption coefficient is marked as the prediction deviation value, and it is determined whether the charging prediction result meets the requirements through the prediction deviation value.

[0007] Further, the specific process of AGV allocation by the uniform distribution method includes: rounding down the ratio of the total number of fault-free AGVs to the number of warehousing areas to obtain the average value, allocating the AGVs to each warehousing area according to the average value, and then allocating the remaining AGVs in descending order of the floor area values of the warehousing areas.

[0008] Further, the specific process of marking the first execution object includes: marking the data of the task to be executed with an earlier task start time as the first candidate task, marking the data of the task to be executed with a later task start time as the second candidate task, converting the power data of the AGV into the continuous operation duration according to full-load transportation, marking the AGV with a continuous operation duration not less than the task execution duration in the first candidate task as the first execution object, marking the remaining AGVs as the objects to be charged, marking the sum value of the quantity of stored and retrieved goods when the full-load transportation duration of all the first execution objects reaches the task execution duration as the first execution value, comparing the first execution value with the quantity of stored and retrieved goods in the first candidate task, and marking the charging object according to the comparison result.

[0009] Further, the specific process of marking the charging object includes: comparing the first execution value with the quantity of stored and retrieved goods in the first candidate task: if the execution value is less than the quantity of stored and retrieved goods, marking the object to be charged with the highest remaining power as the first execution object, and then recalculating the first execution value, and so on until the first execution value is greater than or equal to the quantity of stored and retrieved goods; if the first execution value is greater than or equal to the quantity of stored and retrieved goods, marking the object to be charged with the remaining power lower than the preset power threshold among the objects to be charged as the charging object.

[0010] Further, the specific process of marking the regression object includes: the first execution object executes the first candidate task to charge the charging object; then, based on the task execution duration, the remaining power of all first execution objects when completing the first candidate task is predicted, and the charging object whose predicted remaining power reaches the full charge state before the start time of the second candidate task is marked as the regression object.

[0011] Further, the specific process of marking the second execution object and the prediction interruption coefficient includes: converting the remaining power of the regression object and the first execution object into the continuous operation duration according to the full load transportation, marking the regression object and the first execution object whose continuous operation duration is not less than the task execution duration in the second candidate task as the second execution object, marking the sum value of the storage and retrieval quantities when the full load transportation duration of all second execution objects reaches the task execution duration as the second execution value, comparing the second execution value with the storage and retrieval quantity in the second candidate task, and marking the AGV with the highest remaining power among the remaining AGVs as the second execution object when the second execution value is less than the storage and retrieval quantity until the second execution value is not less than the storage and retrieval quantity; marking the ratio of the number of AGVs with charging interruption to the number of charging objects as the prediction interruption coefficient.

[0012] Further, the specific process of determining whether the task execution load rate meets the requirements includes: at the start time of the second candidate task, marking the ratio of the number of AGVs with actual charging terminals to the number of actual charging objects as the actual interruption coefficient, and comparing the actual interruption coefficient with the preset actual interruption threshold: if the actual interruption coefficient is less than the actual interruption threshold, it is determined that the task execution load rate meets the requirements; if the actual interruption coefficient is greater than or equal to the actual interruption threshold, it is determined that the task execution load rate does not meet the requirements.

[0013] Further, the specific process of determining whether the charging prediction result meets the requirements includes: comparing the prediction deviation value with the preset prediction deviation threshold: if the prediction deviation value is less than the prediction deviation threshold, it is determined that the charging prediction result meets the requirements; if the prediction deviation value is greater than or equal to the prediction deviation threshold, it is determined that the charging prediction result does not meet the requirements, generating a prediction optimization signal and sending the prediction optimization signal to the mobile terminal of the management personnel.

[0014] Furthermore, the interruption monitoring module is also communicatively connected to a necessary evaluation module, and the necessary evaluation module is also communicatively connected to a distribution optimization analysis module; the necessary evaluation module is used to evaluate the necessity of AGV distribution optimization in the storage area: generate an evaluation period, mark the number of times of receiving the distribution optimization signal within the evaluation period as the comprehensive necessity value, mark the number of times of generating the distribution optimization signal in a single storage area within the evaluation period as the single necessity value of the corresponding storage area, determine that the warehouse has the necessity of optimization when the comprehensive necessity value exceeds the preset comprehensive necessity threshold or the single necessity value of any storage area exceeds the preset single necessity threshold, generate a distribution optimization analysis signal and send the distribution optimization analysis signal to the distribution optimization analysis module; otherwise, determine that the warehouse does not have the necessity of optimization.

[0015] The distribution optimization analysis module is used to perform AGV distribution optimization analysis on the storage area: sum and average the actual interruption coefficients when the storage area performs storage and retrieval tasks within the evaluation period to obtain the resource inclination value of the storage area, mark the sum value of the resource inclination values of all storage areas as the resource comprehensive value, mark the ratio of the total number of fault-free AGVs to the resource comprehensive value as the resource average value, round down the product of the resource inclination value of the storage area and the resource average value to obtain the distribution optimization value of the storage area, allocate the fault-free AGVs to the storage areas according to the distribution optimization value, and then allocate the remaining AGVs in descending order of the floor area values of the storage areas.

[0016] The present invention has the following beneficial effects: 1. The warehouse can be regionally segmented and AGVs can be allocated through the storage area segmentation module. After the segmentation is completed, the AGVs are allocated in a uniform distribution manner, and then the load of the warehouse AGVs performing storage and retrieval tasks is evaluated and analyzed. The load response situation of the AGVs when performing subsequent storage and retrieval tasks is analyzed and evaluated through the prediction analysis model, and the task-executing AGVs and charging AGVs are respectively marked, which can ensure the overall reserve power of the AGVs while ensuring that the storage and retrieval tasks can be completed normally, and at the same time enable the charging AGVs to reach the full charge state as much as possible before being put into use, delaying the service life of the AGV batteries; 2. The charging interruption status of the AGVs can be monitored and analyzed through the interruption monitoring module, and at the same time, the charging prediction results can be evaluated. When the charging prediction results are abnormal, the AGV charging parameters in the dynamic storage environment are updated, improving the accuracy of the charging prediction analysis process; 3. The necessity of AGV distribution optimization in the storage area can be evaluated through the necessary evaluation module, the total number of generated distribution optimization signals in the warehouse and the number of generated distribution optimization signals in a single storage area are counted, and the distribution optimization analysis is triggered when the distribution optimization analysis standard is met; 4. The AGV allocation optimization analysis module can perform AGV allocation optimization analysis on the warehousing area. Different from the existing method of AGV allocation based on the total task volume, the present invention considers the impact of parameters such as the dynamic environment of different warehousing areas and the urgency of task execution on the normal charging of AGVs. Based on the monitoring results of the AGV charging interruption status, the AGV allocation for the warehousing area is carried out to ensure the full charge rate of AGVs in the charging state, and further protect the AGV batteries from the perspective of management planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 is the overall system block diagram of the present invention; Figure 2 is the system block diagram of Embodiment 1 of the present invention; Figure 3 is the system block diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0020] As Figure 1 shown, the AGV digital twin battery management system for a dynamic warehousing environment includes a management subsystem and an optimization subsystem. The management subsystem includes a warehousing area segmentation module, a task load analysis module, and an interruption monitoring module. The optimization subsystem includes a necessary evaluation module and an allocation optimization analysis module.

[0021] Embodiment 1: As Figure 2 shown, the warehousing area segmentation module, the task load analysis module, and the interruption monitoring module are sequentially communicatively connected.

[0022] The warehousing area segmentation module is used to segment the warehouse area and allocate AGVs: the warehouse is segmented into several warehousing areas, and after the warehousing area segmentation is completed, AGVs are allocated in an evenly distributed manner: the total number of fault-free AGVs is divided by the number of warehousing areas and rounded down to obtain the average value, and the AGVs are allocated to each warehousing area according to the average value, and then the remaining AGVs are allocated in descending order of the floor area values of the warehousing areas.

[0023] The task load analysis module is used to evaluate and analyze the load of the warehouse AGV performing storage and retrieval tasks: when each storage and retrieval task in the warehousing area is completed, the data of the next two tasks to be executed are retrieved. The data of the tasks to be executed include the task start time, the task execution duration, and the number of storage and retrieval items. The AGV power data at the start time of the prediction period is retrieved. The data of the tasks to be executed and the AGV power data are input into the prediction analysis model, and the charging time and charging status of the AGV are predicted through the prediction analysis model: the data of the task to be executed with an earlier task start time is marked as the first candidate task, and the data of the task to be executed with a later task start time is marked as the second candidate task. The AGV power data is converted into the continuous operation duration according to full-load transportation. The AGVs with a continuous operation duration not less than the task execution duration in the first candidate task are marked as the first execution objects, and the remaining AGVs are marked as the objects to be charged. The sum of the number of storage and retrieval items when the full-load transportation duration of all the first execution objects reaches the task execution duration is marked as the first execution value, and the first execution value is compared with the number of storage and retrieval items in the first candidate task: if the execution value is less than the number of storage and retrieval items, the object to be charged with the highest remaining power is marked as the first execution object (when the object to be charged is converted and marked as the first execution object, the number of storage and retrieval items accumulated on the first execution value is the number of storage and retrieval items when the full-load transportation duration of the object to be charged reaches the continuous operation duration), and then the first execution value is recalculated, and so on, until the first execution value is greater than or equal to the number of storage and retrieval items; if the first execution value is greater than or equal to the number of storage and retrieval items, the objects to be charged with the remaining power lower than the preset power threshold among the objects to be charged are marked as the charging objects; the first execution objects execute the first candidate task, and the charging objects are charged; then, according to the task execution duration, the remaining power of all the first execution objects when they complete the first candidate task is predicted, and the charging objects predicted to reach the fully charged state before the task start time of the second candidate task are marked as the regression objects; Convert the remaining power of the regression object and the first execution object into the continuous operation duration according to full-load operation. Mark the regression object and the first execution object whose continuous operation duration is not less than the task execution duration in the second alternative task as the second execution object. Mark the sum value of the storage and retrieval quantities when the full-load operation duration of all second execution objects reaches the task execution duration as the second execution value. Compare the second execution value with the storage and retrieval quantity in the second alternative task. When the second execution value is less than the storage and retrieval quantity, mark the AGV with the highest remaining power among the remaining AGVs (including the AGVs in the charging state) as the second execution object until the second execution value is not less than the storage and retrieval quantity. Mark the ratio of the number of AGVs with charging interruptions to the number of charging objects as the prediction interruption coefficient. Divide the warehouse into regions and allocate AGVs. After the division is completed, use the uniform allocation method for AGV allocation. Then, evaluate and analyze the load of the warehouse AGVs when performing storage and retrieval tasks. Analyze and evaluate the load response of the AGVs when performing subsequent storage and retrieval tasks through the prediction analysis model, and mark the task-executing AGVs and the charging AGVs respectively, ensuring the overall reserve power of the AGVs while ensuring that the storage and retrieval tasks can be completed normally, and at the same time enabling the charging AGVs to reach the full-charged state as much as possible before being put into use, delaying the service life of the AGV batteries.

[0024] The interruption monitoring module is used to monitor and analyze the charging interruption status of the AGVs: At the start time of the second alternative task, mark the ratio of the actual number of AGVs with charging terminals to the actual number of charging objects as the actual interruption coefficient, and compare the actual interruption coefficient with the preset actual interruption threshold: If the actual interruption coefficient is less than the actual interruption threshold, it is determined that the task execution load rate meets the requirements; If the actual interruption coefficient is greater than or equal to the actual interruption threshold, it is determined that the task execution load rate does not meet the requirements, generate an allocation optimization signal and send the allocation optimization signal to the optimization analysis subsystem.

[0025] Mark the absolute value of the difference between the prediction interruption coefficient and the actual interruption coefficient as the prediction deviation value, and compare the prediction deviation value with the preset prediction deviation threshold: If the prediction deviation value is less than the prediction deviation threshold, it is determined that the charging prediction result meets the requirements; If the prediction deviation value is greater than or equal to the prediction deviation threshold, it is determined that the charging prediction result does not meet the requirements, generate a prediction optimization signal and send the prediction optimization signal to the mobile terminal of the management personnel; Monitor and analyze the charging interruption status of the AGVs, and at the same time evaluate the charging prediction result. Update the charging parameters of the AGVs in the dynamic storage environment when the charging prediction result is abnormal to improve the accuracy of the charging prediction analysis process.

[0026] Example 2: As Figure 3As shown, the necessary evaluation module is communicatively connected to the allocation optimization analysis module. The necessary evaluation module is used to evaluate the necessity of AGV allocation optimization in the storage area: generate an evaluation period, mark the number of times of receiving the allocation optimization signal within the evaluation period as the comprehensive necessity value, and mark the number of times of generating the allocation optimization signal in a single storage area within the evaluation period as the single necessity value of the corresponding storage area. When the comprehensive necessity value exceeds the preset comprehensive necessity threshold or the single necessity value of any storage area exceeds the preset single necessity threshold, it is determined that the warehouse has the necessity for optimization, generate an allocation optimization analysis signal and send the allocation optimization analysis signal to the allocation optimization analysis module; otherwise, it is determined that the warehouse does not have the necessity for optimization; evaluate the necessity of AGV allocation optimization in the storage area, count the total number of generated allocation optimization signals in the warehouse and the number of generated allocation optimization signals in a single storage area, and trigger the allocation optimization analysis when the criteria for allocation optimization analysis are met.

[0027] The allocation optimization analysis module is used to perform AGV allocation optimization analysis on the storage area: sum and average the actual interruption coefficients when the storage area performs storage and retrieval tasks within the evaluation period to obtain the resource inclination value of the storage area, mark the sum value of the resource inclination values of all storage areas as the resource comprehensive value, mark the ratio of the total number of fault-free AGVs to the resource comprehensive value as the resource average score value, round down the product of the resource inclination value of the storage area and the resource average score value to obtain the allocation optimization value of the storage area, allocate the fault-free AGVs to the storage areas according to the allocation optimization value, and then allocate the remaining AGVs in descending order of the floor area values of the storage areas; perform AGV allocation optimization analysis on the storage area. Different from the existing method of AGV allocation according to the total task volume, the present invention considers the influence of parameters such as the dynamic environment and task execution urgency of different storage areas on the normal charging of AGVs, and based on the monitoring results of the AGV charging interruption state, allocates AGVs to the storage areas to ensure the full charge rate of AGVs in the charging state, and further protects the AGV batteries from the perspective of management planning.

[0028] AGV Digital Twin Battery Management System for Dynamic Warehousing Environment. During operation, the warehouse is divided into several warehousing areas. After the division of the warehousing areas, AGVs are allocated in a uniform distribution manner. When each inventory task is completed within the warehousing area, the data of the next two tasks to be executed are retrieved, and the AGV battery power data at the start time of the prediction period is retrieved. The data of the tasks to be executed and the AGV battery power data are input into the prediction analysis model, and the charging timing and charging status of the AGV are predicted through the prediction analysis model; at the start time of the second-generation selected task, the ratio of the actual number of AGVs with charging terminals to the actual number of charging objects is marked as the actual interruption coefficient, and whether the task execution load rate meets the requirements is determined through the actual interruption coefficient; the absolute value of the difference between the predicted interruption coefficient and the actual interruption coefficient is marked as the prediction deviation value, and whether the charging prediction result meets the requirements is determined through the prediction deviation value.

[0029] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods to replace them. As long as they do not deviate from the structure of the invention or exceed the scope defined by this claim book, they should fall within the protection scope of the present invention.

[0030] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0031] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not elaborate all the details, nor do they limit the invention to only the specific implementation manners. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments to better explain the principle and practical application of the present invention, so that those skilled in the art of this technology can well understand and utilize the present invention. The present invention is only limited by the claim book and its full scope and equivalents.

Claims

1. An AGV digital twin battery management system for a dynamic warehousing environment, characterized in that, It includes a warehousing area segmentation module, a task load analysis module, and an interruption monitoring module that are connected in sequence; The warehousing area segmentation module is used to segment the warehouse area and allocate AGVs: the warehouse is segmented into several warehousing areas, and after the warehousing area segmentation is completed, AGVs are allocated in an evenly distributed manner; The task load analysis module is used to evaluate and analyze the load of the AGVs in the warehouse when performing storage and retrieval tasks: when each storage and retrieval task in the warehousing area is completed, the data of the next two tasks to be executed are retrieved. The data of the tasks to be executed include the task start time, the task execution duration, and the quantity of stored and retrieved goods. The AGV power data at the start time of the prediction period is retrieved. The data of the tasks to be executed and the AGV power data are input into the prediction analysis model. The prediction analysis model predicts the charging time and charging status of the AGV and marks the first execution object, the charging object, the regression object, the second execution object, and the prediction interruption coefficient; The interruption monitoring module is used to monitor and analyze the charging interruption status of the AGV and obtain the actual interruption coefficient, and determine whether the task execution load rate meets the requirements through the actual interruption coefficient; the absolute value of the difference between the prediction interruption coefficient and the actual interruption coefficient is marked as the prediction deviation value, and it is determined whether the charging prediction result meets the requirements through the prediction deviation value.

2. The AGV digital twin battery management system for a dynamic warehousing environment according to claim 1, wherein The specific process of allocating AGVs in an evenly distributed manner includes: rounding down the ratio of the total number of fault-free AGVs to the number of warehousing areas to obtain an average value, allocating the AGVs to each warehousing area according to the average value, and then allocating the remaining AGVs in descending order of the floor area values of the warehousing areas.

3. The AGV digital twin battery management system for a dynamic warehousing environment according to claim 2, wherein The specific process of marking the first execution object includes: marking the data of the task to be executed with an earlier task start time as the first candidate task, marking the data of the task to be executed with a later task start time as the second candidate task, converting the power data of the AGV into the continuous operation duration according to full-load transportation, marking the AGV with a continuous operation duration not less than the task execution duration in the first candidate task as the first execution object, marking the remaining AGVs as the objects to be charged, marking the sum value of the quantity of stored and retrieved goods when the full-load transportation duration of all the first execution objects reaches the task execution duration as the first execution value, comparing the first execution value with the quantity of stored and retrieved goods in the first candidate task, and marking the charging object according to the comparison result.

4. The AGV digital twin battery management system for a dynamic warehousing environment according to claim 3, wherein The specific process of marking the charging object includes: comparing the first execution value with the quantity of stored and retrieved goods in the first candidate task: if the execution value is less than the quantity of stored and retrieved goods, marking the object to be charged with the highest remaining power as the first execution object, and then recalculating the first execution value, and so on, until the first execution value is greater than or equal to the quantity of stored and retrieved goods; if the first execution value is greater than or equal to the quantity of stored and retrieved goods, marking the object to be charged with a remaining power lower than the preset power threshold among the objects to be charged as the charging object.

5. The AGV digital twin battery management system for a dynamic warehousing environment according to claim 4, characterized in that, The specific process of marking the regression object includes: the first execution object executes the first candidate task to charge the charging object; then, based on the task execution duration, the remaining power of all first execution objects when completing the first candidate task is predicted, and the charging object whose predicted remaining power reaches the full charge state before the start time of the second candidate task is marked as the regression object.

6. The AGV digital twin battery management system for a dynamic warehousing environment according to claim 5, wherein, The specific process of marking the second execution object and the predicted interruption coefficient includes: converting the remaining power of the regression object and the first execution object into the continuous operation duration according to full-load operation, marking the regression object and the first execution object whose continuous operation duration is not less than the task execution duration in the second candidate task as the second execution object, marking the sum value of the inventory access quantities when the full-load operation duration of all second execution objects reaches the task execution duration as the second execution value, comparing the second execution value with the inventory access quantity in the second candidate task, and marking the AGV with the highest remaining power among the remaining AGVs as the second execution object when the second execution value is less than the inventory access quantity until the second execution value is not less than the inventory access quantity; marking the ratio of the number of AGVs with charging interruption to the number of charging objects as the predicted interruption coefficient.

7. The AGV digital twin battery management system for a dynamic warehousing environment according to claim 6, characterized in that The specific process of determining whether the task execution load rate meets the requirements includes: at the start time of the second candidate task, marking the ratio of the actual number of AGVs with charging terminals to the actual number of charging objects as the actual interruption coefficient, and comparing the actual interruption coefficient with the preset actual interruption threshold: if the actual interruption coefficient is less than the actual interruption threshold, it is determined that the task execution load rate meets the requirements; if the actual interruption coefficient is greater than or equal to the actual interruption threshold, it is determined that the task execution load rate does not meet the requirements.

8. The AGV digital twin battery management system for a dynamic warehousing environment according to claim 7, wherein The specific process of determining whether the charging prediction result meets the requirements includes: comparing the prediction deviation value with the preset prediction deviation threshold: if the prediction deviation value is less than the prediction deviation threshold, it is determined that the charging prediction result meets the requirements; if the prediction deviation value is greater than or equal to the prediction deviation threshold, it is determined that the charging prediction result does not meet the requirements, generating a prediction optimization signal and sending the prediction optimization signal to the mobile terminal of the management personnel.

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