Intelligent warehouse full-freedom agv warehousing access method and system

Abstract

The application relates to the technical field of warehouse storage and access, in particular to a full-freedom AGV warehouse storage and access method and system for an intelligent warehouse. The method comprises the following steps: acquiring position information and task information of each AGV, and performing path planning and navigation according to the position information and the task information of each AGV; acquiring working state data of each AGV, analyzing the working state data to obtain a cooperative regulation instruction of each AGV; and mobilizing the AGV according to the cooperative regulation instruction, and the AGV performing storage and access and carrying of goods according to the cooperative regulation instruction. The application obtains task priority information by analyzing each goods storage and access task, and obtains AGV state information by analyzing AGV data, so as to perform task distribution of the AGV on the goods storage and access task, analyzes the working state data to obtain the cooperative regulation instruction of each AGV, and then controls the working load of each AGV according to the cooperative regulation instruction, so that the task distribution of each AGV is more reasonable, faults caused by excessive load of the AGV are avoided, and the warehouse operation efficiency is ensured.

Description

Technical Field

[0001] This invention relates to the field of warehousing and retrieval technology, and in particular to a fully free-degree-of-freedom AGV warehousing and retrieval method and system for intelligent warehouses. Background Technology

[0002] With the continuous development of automation technology, automated warehousing systems have gradually become an important development direction for modern warehouse management. Automated warehousing systems introduce automated equipment, such as AGVs, stacker cranes, and four-way shuttles, as well as intelligent management systems, such as WMS and WCS, to realize the automatic storage, retrieval, handling and management of goods in the warehouse, thereby improving warehousing efficiency and accuracy.

[0003] Existing smart warehouses using fully free-degree AGVs for storage and retrieval can enable multiple AGVs to work together to complete warehouse storage and retrieval tasks, and can ensure that there are no collisions or conflicts between AGVs. However, when multiple AGVs work together, the task allocation may be unreasonable, causing some AGVs to be overloaded while others are idle, affecting the efficiency of warehouse operations. Summary of the Invention

[0004] This invention provides a fully free-degree-of-freedom AGV storage and retrieval method and system for intelligent warehouses, which solves the above-mentioned technical problems existing in the prior art.

[0005] The first aspect of this invention provides a fully degree-of-freedom AGV warehousing and retrieval method for intelligent warehouses, comprising the following steps:

[0006] Step 1: Task Reception and Preprocessing: Receive cargo storage and retrieval task information and identify the cargo storage and retrieval task information to obtain task data. Perform priority analysis on the task data of each cargo storage and retrieval task to obtain task priority information.

[0007] As a further improvement of the present invention, priority analysis is performed on the task data of each cargo storage and retrieval task, specifically as follows:

[0008] The task data is identified to obtain task information and cargo information. The task information includes the task source and task duration. Based on the task source, customer information for each cargo storage and retrieval task is obtained. Based on the customer information, multiple customer levels are set for each customer, and each customer level corresponds to an importance index. The customer information corresponding to each cargo storage and retrieval task is matched with multiple customer levels to obtain the corresponding importance index. The remaining duration of each cargo storage and retrieval task is calculated by comparing the task duration with the current time. The remaining duration of each cargo storage and retrieval task is sorted from most to least to obtain the ranking of the remaining duration of each cargo storage and retrieval task.

[0009] The cargo information includes cargo value and cargo type. The cargo value for each cargo corresponding to a cargo storage / retrieval task is obtained, and the cargo value is divided into multiple value ranges. Each value range corresponds to a cargo priority value. The cargo value of each cargo is matched with multiple value ranges to obtain the corresponding cargo priority value. The cargo priority values ​​of each cargo in each cargo storage / retrieval task are summed to obtain the total cargo priority value. Based on the storage timeframe, cargo types are divided into long-term storage cargo, medium-term storage cargo, and short-term storage cargo. Each cargo category corresponds to a storage impact value. The storage impact values ​​of each cargo type corresponding to a cargo in each cargo storage / retrieval task are summed to obtain the total storage impact value. Long-term storage cargo typically has high value stability, such as precious metals and antiques. These cargoes can be stored for a long time without significant depreciation. Medium-term storage cargo indicates that these cargoes are relatively stable and will not significantly deteriorate or depreciate within a certain period, but they cannot be stored for a long time, such as some daily necessities and clothing. Short-term storage cargo indicates perishable cargo, such as fresh food and flowers, with a short storage timeframe, requiring prompt processing to ensure quality.

[0010] The importance index, total cargo priority value, and total storage impact value are normalized and their values ​​are taken according to the formula. Obtain the priority status value; where mc represents the ranking with remaining time, and its value is... ZY, HY, and CY represent the importance index, total priority value of goods, and total impact value of storage, respectively; z1, z2, z3, and z4 are preset weighting factors with values ​​of 1.97, 1.21, 0.99, and 2.02, respectively; the priority status values ​​of each goods storage and retrieval task are compared and sorted to obtain the task priority ranking, and the corresponding priority information is obtained according to the task priority ranking of each goods storage and retrieval task.

[0011] Step 2, Intelligent Task Allocation: Obtain priority information and AGV data for each retrieval task, analyze the AGV data to obtain AGV status information, and when the AGV status information is normal, intelligently allocate each cargo retrieval task through an intelligent algorithm to obtain the task information for each AGV.

[0012] As a further improvement to the present invention, the AGV data is analyzed, and the specific analysis steps are as follows:

[0013] A1: Battery power data, driving speed data, and fault data are obtained by identifying AGV data;

[0014] A2: Based on the battery power data of each AGV, obtain the remaining power percentage, get the pre-set remaining power threshold, when the remaining power percentage is less than the remaining power threshold, calculate the power difference by the difference between the remaining power threshold and the remaining power percentage, divide the power difference into multiple power difference intervals, each power difference interval corresponds to a power impact value, match the power difference of each AGV with multiple power difference intervals to obtain the corresponding power impact value.

[0015] A3: Identify the driving speed data to obtain the average speed, maximum speed, and minimum speed per unit time;

[0016] A31: Obtain the preset minimum speed limit value per unit time, compare the average speed of each AGV per unit time with the minimum speed limit value, and when the average speed is less than the minimum speed limit value, calculate the speed difference impact value by calculating the difference between the minimum speed limit value and the average speed.

[0017] A32: Based on the maximum and minimum speeds of each AGV, obtain the speed fluctuation range, acquire the pre-set speed reference range, match the speed fluctuation range of each AGV with the speed reference range, obtain the upper limit and lower limit exceedance values ​​corresponding to the maximum and minimum speeds, and calculate and sum the upper limit and lower limit exceedance values ​​to obtain the total exceedance value.

[0018] A33: The speed influence index is obtained by summing the speed difference influence value and the excess value.

[0019] A4: Identify fault data to obtain historical fault data and fault time;

[0020] A41: Based on historical fault data, obtain the number of faults per unit time, obtain a pre-set fault number threshold, compare the number of faults of each AGV with the fault number threshold, and when the number of faults is greater than the fault number threshold, mark the fault number as the fault impact value.

[0021] A42: Obtain the preset fault time threshold, compare the fault time of each AGV within a unit time with the fault time threshold, and mark the corresponding fault as a high-impact fault when the fault time is greater than the fault time threshold. Count the number of high-impact faults and mark them as high-shadow faults. Calculate the ratio of the high-shadow faults to the total number of faults within the corresponding unit time to obtain the high-shadow fault ratio.

[0022] A43: The fault impact index is calculated by multiplying the fault impact value by the proportion of high-profile faults.

[0023] A5: Construct a right triangle using the values ​​of the power impact value and the speed impact index as the two legs of the right triangle. Starting from the intersection of the two legs, draw a straight line perpendicular to the right triangle. The length of the straight line is equal to the value of the fault impact index. Then, construct a triangular pyramid using the right triangle and the straight line. Calculate the volume of the triangular pyramid and mark the volume value as the AGV status value.

[0024] A6: When the AGV status value is greater than the set threshold, the corresponding AGV status information is generated as an abnormal status.

[0025] Step 3, Path Planning and Navigation: Obtain the location and task information of each AGV, and perform path planning and navigation based on the location and task information of each AGV.

[0026] Step 4: Collaborative Operation Control: Obtain the working status data of each AGV, analyze the working status data to obtain the collaborative adjustment instructions for each AGV.

[0027] As a further improvement to the present invention, the working status data is analyzed, and the specific analysis steps are as follows:

[0028] S1: Obtain task status, workload, and task progress data by identifying work status data;

[0029] S2: Set the task status of the AGV to working status and idle status. When the task status of the AGV is working, mark the corresponding AGV as a working AGV and record the working time of each working AGV per unit time. Calculate the working percentage by the ratio of the working time to the total running time, obtain the pre-set working percentage threshold, and compare the working percentage with the working percentage threshold. When the working percentage is greater than the working percentage threshold, mark the corresponding working percentage as a busy status value.

[0030] S3: Obtain the energy consumption value of each AGV per unit time based on the operating load, compare the energy consumption value per unit time with the set threshold, and mark the corresponding energy consumption value as the energy consumption impact value when the energy consumption value of each AGV per unit time is greater than the set threshold.

[0031] S4: Identify the task progress data to obtain the total task amount, task completion amount, and task time data;

[0032] S41: Calculate the remaining task quantity by the difference between the total task quantity and the task completion quantity. Divide the remaining task quantity into multiple remaining task quantity intervals. Each remaining task quantity interval corresponds to a task impact value. Match the remaining task quantity corresponding to each task with multiple remaining task quantity intervals to obtain the corresponding task impact value.

[0033] S42: Based on the task time data, obtain the task deadline and the current time. Calculate the difference between the task deadline and the current time to obtain the remaining task time. Divide the remaining task time into multiple remaining time intervals. Each remaining time interval corresponds to a remaining time shadow value. Match the remaining task time with multiple remaining time intervals to obtain the corresponding remaining time shadow value.

[0034] S43: The progress impact value is calculated by adding the task impact value to the remaining time shadow value.

[0035] S5: Construct two equilateral triangles with the values ​​of busy status and energy consumption impact as the sides of the equilateral triangle. Using the centroids of the two equilateral triangles as the starting and ending points, draw a straight line perpendicular to the two equilateral triangles. The length of the straight line is equal to the value of the progress impact. Then construct a truncated triangular prism with the two equilateral triangles and the straight line. Calculate the volume of the truncated triangular prism and mark the volume value as the working status value.

[0036] S6: Compare the working status value with the set threshold. When the working status value is greater than the set threshold, generate the corresponding emergency information. Based on the emergency information, redistribute each AGV and generate the corresponding coordinated adjustment command.

[0037] Step 5: Goods storage, retrieval and handling: The AGV is dispatched according to the coordination instructions, and the AGV performs goods storage, retrieval and handling according to the coordination instructions.

[0038] A second aspect of the present invention provides a fully free-degree-of-freedom AGV storage and retrieval system for intelligent warehouses, comprising: a task management and analysis module, a path planning and navigation module, an execution control module, a real-time monitoring and analysis module, and a database.

[0039] The task management and analysis module is used to receive task information and analyze it to obtain task priority information.

[0040] The path planning and navigation module is used to plan and navigate the AGV's path in real time.

[0041] The execution control module is used to control the coordinated operation of multiple AGVs and to adjust and control the tasks of each AGV.

[0042] The real-time monitoring and analysis module is used to monitor the storage status of goods and the operation status of AGVs in the warehouse in real time, and provides abnormal alarms and data analysis functions.

[0043] The database is used to receive and store data information from each module.

[0044] The beneficial effects of the technical solution provided by this invention compared with the prior art are as follows:

[0045] This invention analyzes the cargo storage and retrieval tasks to obtain task priority information and analyzes AGV data to obtain AGV status information. Based on this, the invention allocates tasks to AGVs for cargo storage and retrieval tasks and analyzes the working status data to obtain the coordination and adjustment instructions for each AGV. Then, based on the coordination and adjustment instructions, the invention controls the workload of each AGV to make the task allocation of each AGV more reasonable, avoid AGV overload causing failure, and ensure the efficiency of warehousing operations. Attached Figure Description

[0046] Figure 1 This is a flowchart of the method of the present invention;

[0047] Figure 2 This is a schematic diagram of a fully free-degree-of-freedom AGV storage and retrieval system for an intelligent warehouse. Detailed Implementation

[0048] The technical solution of the present invention will now be clearly and completely described in conjunction with the accompanying drawings and specific embodiments.

[0049] For ease of understanding, the specific process of the embodiments of the present invention will be described below, such as... Figure 1 As shown in the figure, one embodiment of a fully-free AGV warehousing and retrieval method for intelligent warehouses includes the following steps:

[0050] Step 1: Task Reception and Preprocessing: Receive cargo storage and retrieval task information and identify the cargo storage and retrieval task information to obtain task data. Perform priority analysis on the task data of each cargo storage and retrieval task to obtain task priority information.

[0051] Priority analysis is performed on the task data of each cargo storage and retrieval task. Specifically, the task data is identified to obtain task information and cargo information. The task information includes the task source and task deadline. Based on the task source, customer information for each cargo storage and retrieval task is obtained. Based on the customer information, multiple customer levels are set, and each customer level corresponds to an importance index. The customer information corresponding to each cargo storage and retrieval task is matched with multiple customer levels to obtain the corresponding importance index. The remaining time of each cargo storage and retrieval task is calculated by comparing the task deadline with the current time. The remaining time of each cargo storage and retrieval task is sorted from most to least to obtain the ranking of the remaining time of each cargo storage and retrieval task.

[0052] The cargo information includes cargo value and cargo type. The cargo value for each cargo corresponding to a cargo storage / retrieval task is obtained, and the cargo value is divided into multiple value ranges. Each value range corresponds to a cargo priority value. The cargo value of each cargo is matched with multiple value ranges to obtain the corresponding cargo priority value. The cargo priority values ​​of each cargo in each cargo storage / retrieval task are summed to obtain the total cargo priority value. Based on the storage timeframe, cargo types are divided into long-term storage cargo, medium-term storage cargo, and short-term storage cargo. Each cargo category corresponds to a storage impact value. The storage impact values ​​of each cargo type corresponding to a cargo in each cargo storage / retrieval task are summed to obtain the total storage impact value. Long-term storage cargo typically has high value stability, such as precious metals and antiques. These cargoes can be stored for a long time without significant depreciation. Medium-term storage cargo indicates that these cargoes are relatively stable and will not significantly deteriorate or depreciate within a certain period, but they cannot be stored for a long time, such as some daily necessities and clothing. Short-term storage cargo indicates perishable cargo, such as fresh food and flowers, with a short storage timeframe, requiring prompt processing to ensure quality.

[0053] The importance index, total cargo priority value, and total storage impact value are normalized and their values ​​are taken according to the formula. Obtain the priority status value; where mc represents the ranking with remaining time, and its value is... ZY, HY, and CY represent the importance index, total priority value of goods, and total impact value of storage, respectively; z1, z2, z3, and z4 are preset weighting factors with values ​​of 1.97, 1.21, 0.99, and 2.02, respectively; the priority status values ​​of each goods storage and retrieval task are compared and sorted to obtain the task priority ranking, and the corresponding priority information is obtained according to the task priority ranking of each goods storage and retrieval task.

[0054] Step 2, Intelligent Task Allocation: Obtain priority information and AGV data for each retrieval task, analyze the AGV data to obtain AGV status information, and when the AGV status information is normal, intelligently allocate each cargo retrieval task through an intelligent algorithm to obtain the task information for each AGV.

[0055] The specific steps for analyzing AGV data are as follows:

[0056] A1: Battery power data, driving speed data, and fault data are obtained by identifying AGV data;

[0057] A2: Based on the battery power data of each AGV, obtain the remaining power percentage, get the pre-set remaining power threshold, when the remaining power percentage is less than the remaining power threshold, calculate the power difference by the difference between the remaining power threshold and the remaining power percentage, divide the power difference into multiple power difference intervals, each power difference interval corresponds to a power impact value, match the power difference of each AGV with multiple power difference intervals to obtain the corresponding power impact value.

[0058] A3: Identify the driving speed data to obtain the average speed, maximum speed, and minimum speed per unit time;

[0059] A31: Obtain the preset minimum speed limit value per unit time, compare the average speed of each AGV per unit time with the minimum speed limit value, and when the average speed is less than the minimum speed limit value, calculate the speed difference impact value by calculating the difference between the minimum speed limit value and the average speed.

[0060] A32: Based on the maximum and minimum speeds of each AGV, obtain the speed fluctuation range, acquire the pre-set speed reference range, match the speed fluctuation range of each AGV with the speed reference range, obtain the upper limit and lower limit exceedance values ​​corresponding to the maximum and minimum speeds, and calculate and sum the upper limit and lower limit exceedance values ​​to obtain the total exceedance value.

[0061] A33: The speed influence index is obtained by summing the speed difference influence value and the excess value.

[0062] A4: Identify fault data to obtain historical fault data and fault time;

[0063] A41: Based on historical fault data, obtain the number of faults per unit time, obtain a pre-set fault number threshold, compare the number of faults of each AGV with the fault number threshold, and when the number of faults is greater than the fault number threshold, mark the fault number as the fault impact value.

[0064] A42: Obtain the preset fault time threshold, compare the fault time of each AGV within a unit time with the fault time threshold, and mark the corresponding fault as a high-impact fault when the fault time is greater than the fault time threshold. Count the number of high-impact faults and mark them as high-shadow faults. Calculate the ratio of the high-shadow faults to the total number of faults within the corresponding unit time to obtain the high-shadow fault ratio.

[0065] A43: The fault impact index is calculated by multiplying the fault impact value by the proportion of high-profile faults.

[0066] A5: Construct a right triangle using the values ​​of the power impact value and the speed impact index as the two legs of the right triangle. Starting from the intersection of the two legs, draw a straight line perpendicular to the right triangle. The length of the straight line is equal to the value of the fault impact index. Then, construct a triangular pyramid using the right triangle and the straight line. Calculate the volume of the triangular pyramid and mark the volume value as the AGV status value.

[0067] A6: When the AGV status value is greater than the set threshold, the corresponding AGV status information is generated as an abnormal status.

[0068] Step 3, Path Planning and Navigation: Obtain the location and task information of each AGV, and perform path planning and navigation based on the location and task information of each AGV.

[0069] Step 4: Collaborative Operation Control: Obtain the working status data of each AGV, analyze the working status data to obtain the collaborative adjustment instructions for each AGV.

[0070] The specific analysis steps for the work status data are as follows:

[0071] S1: Obtain task status, workload, and task progress data by identifying work status data;

[0072] S2: Set the task status of the AGV to working status and idle status. When the task status of the AGV is working, mark the corresponding AGV as a working AGV and record the working time of each working AGV per unit time. Calculate the working percentage by the ratio of the working time to the total running time, obtain the pre-set working percentage threshold, and compare the working percentage with the working percentage threshold. When the working percentage is greater than the working percentage threshold, mark the corresponding working percentage as a busy status value.

[0073] S3: Obtain the energy consumption value of each AGV per unit time based on the operating load, compare the energy consumption value per unit time with the set threshold, and mark the corresponding energy consumption value as the energy consumption impact value when the energy consumption value of each AGV per unit time is greater than the set threshold.

[0074] S4: Identify the task progress data to obtain the total task amount, task completion amount, and task time data;

[0075] S41: Calculate the remaining task quantity by the difference between the total task quantity and the task completion quantity. Divide the remaining task quantity into multiple remaining task quantity intervals. Each remaining task quantity interval corresponds to a task impact value. Match the remaining task quantity corresponding to each task with multiple remaining task quantity intervals to obtain the corresponding task impact value.

[0076] S42: Based on the task time data, obtain the task deadline and the current time. Calculate the difference between the task deadline and the current time to obtain the remaining task time. Divide the remaining task time into multiple remaining time intervals. Each remaining time interval corresponds to a remaining time shadow value. Match the remaining task time with multiple remaining time intervals to obtain the corresponding remaining time shadow value.

[0077] S43: The progress impact value is calculated by adding the task impact value to the remaining time shadow value.

[0078] S5: Construct two equilateral triangles with the values ​​of busy status and energy consumption impact as the sides of the equilateral triangle. Using the centroids of the two equilateral triangles as the starting and ending points, draw a straight line perpendicular to the two equilateral triangles. The length of the straight line is equal to the value of the progress impact. Then construct a truncated triangular prism with the two equilateral triangles and the straight line. Calculate the volume of the truncated triangular prism and mark the volume value as the working status value.

[0079] S6: Compare the working status value with the set threshold. When the working status value is greater than the set threshold, generate the corresponding emergency information. Based on the emergency information, redistribute each AGV and generate the corresponding coordinated adjustment command.

[0080] Step 5: Goods storage, retrieval and handling: The AGV is dispatched according to the coordination instructions, and the AGV performs goods storage, retrieval and handling according to the coordination instructions.

[0081] like Figure 2 As shown, the present invention also provides a fully free-degree-of-freedom AGV warehousing and retrieval system for intelligent warehouses, including a task management and analysis module, a path planning and navigation module, an execution control module, a real-time monitoring and analysis module, and a database.

[0082] The task management and analysis module receives task information and analyzes it to obtain task priority information.

[0083] The path planning and navigation module plans and navigates the AGV's path in real time.

[0084] The execution control module controls the coordinated operation of multiple AGVs and adjusts the tasks of each AGV.

[0085] The real-time monitoring and analysis module monitors the storage status of goods and the operation status of AGVs in the warehouse in real time, and provides abnormal alarms and data analysis functions.

[0086] The database receives and stores data information from each module.

[0087] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; those skilled in the art can modify or make equivalent substitutions to the technical solutions described in the foregoing embodiments; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for warehousing and retrieving goods using a fully degree-of-freedom AGV in an intelligent warehouse, characterized in that, Includes the following steps: Step 1: Receive cargo storage and retrieval task information and identify the cargo storage and retrieval task information to obtain task data; perform priority analysis on the task data of each cargo storage and retrieval task to obtain task priority information. The priority analysis of task data for each cargo storage and retrieval task specifically involves: identifying task data to obtain task information and cargo information; task information including task source and task duration; obtaining customer information for each cargo storage and retrieval task based on the task source; assigning multiple customer levels to customers based on the customer information, with each customer level corresponding to an importance index; matching the customer information corresponding to each cargo storage and retrieval task with multiple customer levels to obtain the corresponding importance index; calculating the remaining duration of each cargo storage and retrieval task by the task duration corresponding to each cargo storage and retrieval task and the current time; and sorting the remaining durations of each cargo storage and retrieval task from most to least to obtain the ranking of the remaining durations of each cargo storage and retrieval task. The cargo information includes cargo value and cargo type. The cargo value for each cargo corresponding to a cargo storage / retrieval task is obtained, and the cargo value is divided into multiple cargo value ranges. Each cargo value range corresponds to a cargo priority value. The cargo value of each cargo is matched with multiple cargo value ranges to obtain the corresponding cargo priority value. The cargo priority values ​​of each cargo in each cargo storage / retrieval task are summed to obtain the total cargo priority value. Based on the storage timeframe of the cargo, cargo types are divided into long-term storage cargo, medium-term storage cargo, and short-term storage cargo. Each cargo category corresponds to a storage impact value. The storage impact values ​​of each cargo type corresponding to each cargo in a cargo storage / retrieval task are summed to obtain the total storage impact value. The priority status value is obtained by comprehensively calculating the importance index, the total priority value of goods, and the total impact value of storage; the priority status values ​​of each goods access task are compared and sorted to obtain the task priority ranking; and the corresponding priority information is obtained according to the task priority ranking of each goods access task. Step 2: Obtain priority information and AGV data for each retrieval task; analyze the AGV data to obtain AGV status information; when the AGV status information is normal, intelligently allocate each cargo retrieval task through an intelligent algorithm to obtain the task information for each AGV. The specific steps for analyzing AGV data are as follows: A1: Battery power data, driving speed data, and fault data are obtained by identifying AGV data; A2: Based on the battery power data of each AGV, obtain the remaining power percentage, get the pre-set remaining power threshold, when the remaining power percentage is less than the remaining power threshold, calculate the power difference by the difference between the remaining power threshold and the remaining power percentage, divide the power difference into multiple power difference intervals, each power difference interval corresponds to a power impact value, match the power difference of each AGV with multiple power difference intervals to obtain the corresponding power impact value; A3: Identify the driving speed data to obtain the average speed, maximum speed, and minimum speed per unit time; A31: Obtain the preset minimum speed limit value per unit time, compare the average speed of each AGV per unit time with the minimum speed limit value, and when the average speed is less than the minimum speed limit value, calculate the speed difference impact value by calculating the difference between the minimum speed limit value and the average speed. A32: Based on the maximum and minimum speeds of each AGV, obtain the speed fluctuation range, acquire the pre-set speed reference range, match the speed fluctuation range of each AGV with the speed reference range, obtain the upper limit and lower limit exceedance values ​​corresponding to the maximum and minimum speeds, and calculate and sum the upper limit and lower limit exceedance values ​​to obtain the total exceedance value. A33: The speed influence index is obtained by summing the speed difference influence value and the value exceeding the total value; A4: Identify fault data to obtain historical fault data and fault time; A41: Based on historical fault data, obtain the number of faults per unit time, obtain a pre-set fault number threshold, compare the number of faults of each AGV with the fault number threshold, and when the number of faults is greater than the fault number threshold, mark the fault number as the fault impact value. A42: Obtain the preset fault time threshold, compare the fault time of each AGV within a unit time with the fault time threshold, and mark the corresponding fault as a high-impact fault when the fault time is greater than the fault time threshold. Count the number of high-impact faults and mark them as high-shadow faults. Calculate the ratio of the high-shadow faults to the total number of faults within the corresponding unit time to obtain the high-shadow fault ratio. A43: The fault impact index is calculated by multiplying the fault impact value with the proportion of high-profile faults; A5: Construct a right triangle using the values ​​of the power impact value and the speed impact index as the two legs of the right triangle. Starting from the intersection of the two legs, draw a straight line perpendicular to the right triangle. The length of the straight line is equal to the value of the fault impact index. Then, construct a triangular pyramid using the right triangle and the straight line. Calculate the volume of the triangular pyramid and mark the volume value as the AGV status value. A6: When the AGV status value is greater than the set threshold, the corresponding AGV status information is generated as an abnormal status; Step 3: Obtain the location and task information of each AGV, and perform path planning and navigation based on the location and task information of each AGV; Step 4: Obtain the working status data of each AGV, analyze the working status data to obtain the coordinated adjustment instructions of each AGV; The specific steps for analyzing the working status data are as follows: S1: Obtain task status, workload, and task progress data by identifying work status data; S2: Set the task status of the AGV to working status and idle status. When the task status of the AGV is working status, mark the corresponding AGV as working AGV and record the working time of each working AGV per unit time. Calculate the working percentage by the ratio of the working time to the total running time, obtain the pre-set working percentage threshold, and compare the working percentage with the working percentage threshold. When the working percentage is greater than the working percentage threshold, mark the corresponding working percentage as busy status value. S3: Obtain the energy consumption value of each AGV per unit time based on the operating load, compare the energy consumption value per unit time with the set threshold, and mark the corresponding energy consumption value as the energy consumption impact value when the energy consumption value of each AGV per unit time is greater than the set threshold. S4: Identify the task progress data to obtain the total task amount, task completion amount, and task time data; S41: Calculate the remaining task quantity by the difference between the total task quantity and the task completion quantity. Divide the remaining task quantity into multiple remaining task quantity intervals. Each remaining task quantity interval corresponds to a task impact value. Match the remaining task quantity corresponding to each task with multiple remaining task quantity intervals to obtain the corresponding task impact value. S42: Based on the task time data, obtain the task deadline and the current time. Calculate the difference between the task deadline and the current time to obtain the remaining task time. Divide the remaining task time into multiple remaining time intervals. Each remaining time interval corresponds to a remaining time shadow value. Match the remaining task time with multiple remaining time intervals to obtain the corresponding remaining time shadow value. S43: The progress impact value is calculated by adding the task impact value to the remaining time shadow value; S5: Construct two equilateral triangles with the values ​​of busy status and energy consumption impact as the sides of the equilateral triangle. With the centroid of the two equilateral triangles as the starting and ending points, draw a straight line perpendicular to the two equilateral triangles. The length of the straight line is equal to the value of the progress impact. Then construct a truncated triangular prism with the two equilateral triangles and the straight line. Calculate the volume of the truncated triangular prism and mark the volume value as the working status value. S6: Compare the working status value with the set threshold. When the working status value is greater than the set threshold, generate the corresponding emergency information. Based on the emergency information, redistribute each AGV and generate the corresponding coordinated adjustment command. Step 5: Deploy the AGV according to the coordination instructions. The AGV will then store, retrieve, and transport the goods according to the coordination instructions.

2. A fully free-degree-of-freedom AGV warehousing and retrieval system for intelligent warehouses, characterized in that, The intelligent warehouse storage and retrieval method using a fully free-degree-of-freedom AGV as described in claim 1 includes: The task management and analysis module is used to receive task information and analyze it to obtain task priority information. The path planning and navigation module is used to plan and navigate the AGV's path in real time. The execution control module is used to control the coordinated operation of multiple AGVs and to adjust and control the tasks of each AGV. The real-time monitoring and analysis module is used to monitor the storage status of goods and the operation status of AGVs in the warehouse in real time, and provides abnormal alarms and data analysis functions. The database is used to receive and store data information from various modules.

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