Full-degree-of-freedom AGV storage access method and system for intelligent warehouse

Abstract

The invention relates to the technical field of storage and taking, in particular to a full-degree-of-freedom AGV storage and taking method and system for an intelligent warehouse. Comprising 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; obtaining working state data of each AGV, and analyzing the working state data to obtain a cooperative adjustment instruction of each AGV; and transferring the AGV according to the cooperation instruction, and storing, taking and carrying the goods by the AGV according to the cooperation instruction. According to the invention, the task priority information is obtained by analyzing each cargo access task, and the AGV state information is obtained by analyzing the AGV data, so that AGV task allocation is carried out on the cargo access tasks, the cooperative adjustment instruction of each AGV is obtained by analyzing the working state data, and then the workload of each AGV is controlled according to the cooperative adjustment instruction. Task allocation of each AGV is more reasonable, faults caused by too large load of the AGV are avoided, and the warehousing operation efficiency is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of warehouse storage and retrieval, and in particular to a full-degree-of-freedom AGV warehouse storage and retrieval method and system for intelligent warehouses. Background Art

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

[0003] The existing full-degree-of-freedom AGV warehouse storage and retrieval for intelligent warehouses should be able to perform collaborative operations among multiple AGVs to jointly complete the storage and retrieval tasks of the warehouse and ensure that there are no collisions and conflicts among the AGVs. However, when multiple AGVs cooperate, the task allocation may be unreasonable, resulting in some AGVs being overloaded while other AGVs are idle, affecting the warehouse operation efficiency. Summary of the Invention

[0004] The present invention provides a full-degree-of-freedom AGV warehouse storage and retrieval method and system for intelligent warehouses to solve the above technical problems existing in the prior art.

[0005] In the first aspect of the present invention, a full-degree-of-freedom AGV warehouse storage and retrieval method for intelligent warehouses is provided, including the following steps:

[0006] Step 1: Task reception and preprocessing: Receive the goods storage and retrieval task information and identify the task data of the goods storage and retrieval task information, and perform priority analysis on the task data of each goods storage and retrieval task to obtain task priority information.

[0007] As a further improvement of the present invention, performing priority analysis on the task data of each goods storage and retrieval task specifically includes:

[0008] Identify the task information and goods information from the task data. The task information includes the task source and task deadline. Obtain the customer information of each goods storage and retrieval task according to the task source. Based on the customer information, set multiple customer levels for the customers, and each customer level corresponds to an importance index. Match the customer information corresponding to each goods storage and retrieval task with the multiple customer levels to obtain the corresponding importance index; Calculate the remaining duration of each goods storage and retrieval task by calculating the task deadline corresponding to each goods storage and retrieval task and the current moment, and sort the remaining durations of each goods storage and retrieval task from more to less to obtain the remaining duration ranking of each goods storage and retrieval task.

[0009] The goods information includes the goods value and the goods type. Obtain the goods value of each good corresponding to the goods access task, divide the goods value into multiple goods value intervals, each goods value interval corresponds to a goods priority value, match the goods value of each good with the multiple goods value intervals to obtain the corresponding goods priority value, and sum up the goods priority values of each good in each goods access task to obtain the total goods priority value; Based on the storage time limit of the goods, the goods types are divided into long-time storage goods, medium-time storage goods, and short-time storage goods. Each goods classification corresponds to a storage influence value. Sum up the storage influence values of the goods types corresponding to each good in the goods access task to obtain the total storage influence value; Long-time storage goods usually have high value stability, such as precious metals, antiques, etc. These goods can be stored for a long time without significant depreciation; Medium-time storage goods indicate that such goods are relatively stable and will not significantly deteriorate or depreciate within a certain period of time, but they cannot be stored for a long time. For example, some daily necessities, clothing, etc.; Short-time storage goods indicate perishable goods, such as fresh food, flowers, etc. They have a short storage time limit and need to be processed as soon as possible to ensure quality.

[0010] Normalize the importance index, the total goods priority value, and the total storage influence value and take their numerical values. According to the formula obtain the priority status value; where mc represents the ranking of the remaining time, and the value is ZY, HY, and CY respectively represent the importance index, the total goods priority value, and the total storage influence value; z1, z2, z3, and z4 are all preset weight factors, and their values are 1.97, 1.21, 0.99, and 2.02 respectively; Compare and sort the priority status values of each goods access task to obtain the task priority ranking, and obtain the corresponding priority information according to the task priority ranking of each goods access task.

[0011] Step 2: Intelligent task allocation: Obtain the priority information and AGV data of each goods access task, analyze the AGV data to obtain the AGV status information. When the AGV status information is normal, use an intelligent algorithm to intelligently allocate each goods access task to obtain the task information of each AGV.

[0012] As a further improvement of the present invention, analyze the AGV data. The specific analysis steps are as follows:

[0013] A1: Identify the battery power data, driving speed data, and fault data by analyzing the AGV data;

[0014] A2: Obtain the remaining power percentage based on the battery power data of each AGV, acquire the pre-set remaining power threshold. When the remaining power percentage is less than the remaining power threshold, calculate the power difference by subtracting the remaining power percentage from the remaining power threshold. Divide the power difference into multiple power difference intervals, with each power difference interval corresponding to a power impact value. Match the power differences of each AGV with the multiple power difference intervals to obtain the corresponding power impact values.

[0015] A3: Identify the average speed, maximum speed, and minimum speed per unit time from the travel speed data;

[0016] A31: Obtain the pre-set minimum speed limit per unit time, compare the average speed of each AGV per unit time with the minimum speed limit. When the average speed is less than the minimum speed limit, calculate the speed difference impact value by subtracting the average speed from the minimum speed limit;

[0017] A32: Obtain the speed fluctuation range based on the maximum speed and minimum speed of each AGV, acquire the pre-set speed reference range. Match the speed fluctuation ranges of each AGV with the speed reference range to obtain the upper limit exceedance value and lower limit exceedance value corresponding to the maximum speed and minimum speed. Calculate the sum of the upper limit exceedance value and the lower limit exceedance value to obtain the total exceedance value;

[0018] A33: Calculate the sum of the speed difference impact value and the total exceedance value to obtain the speed impact index.

[0019] A4: Identify the historical fault data and fault time from the fault data;

[0020] A41: Obtain the number of faults per unit time based on the historical fault data, acquire the pre-set fault number threshold. Compare the number of faults of each AGV with the fault number threshold. When the number of faults is greater than the fault number threshold, mark the number of faults as the fault impact value;

[0021] A42: Obtain the pre-set fault time threshold, compare the fault time of each AGV per unit time with the fault time threshold. When the fault time is greater than the fault time threshold, mark the corresponding fault as a high-impact fault, count the number of high-impact faults and mark it as the high-impact fault number. Calculate the ratio of the high-impact fault number to the total number of faults per unit time to obtain the high-impact fault ratio;

[0022] A43: Calculate the product of the fault impact value and the high-impact fault ratio to obtain the fault impact index.

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

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

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

[0026] Step Four: Cooperative Operation Control: Obtain the working status data of each AGV, and analyze the working status data to obtain the cooperative adjustment instructions for each AGV.

[0027] As a further improvement of the present invention, the analysis of the working status data is as follows:

[0028] S1: Identify the task status, operating load, and task progress data by analyzing the working status data.

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

[0030] S3: Obtain the energy consumption value per unit time of each AGV according to the operating load, and compare the energy consumption value per unit time with the set threshold. When the energy consumption value per unit time of each AGV is greater than the set threshold, mark the corresponding energy consumption value as the energy consumption influence value.

[0031] S4: Identify the total task amount, task completion amount, and task time data from the task progress data.

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

[0033] S42: Obtain the task deadline and the current time point based on the task time data, calculate the difference between the task deadline and the current time point 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, and match the remaining task time with the multiple remaining time intervals to obtain the corresponding remaining time shadow value;

[0034] S43: Add the task impact value and the remaining time shadow value to calculate the progress impact value.

[0035] S5: Construct two equilateral triangles with the values of the busy state value and the energy consumption impact value as the sides of the equilateral triangle. Starting from and ending at the centroids of the two equilateral triangles, 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 value. Then, construct a triangular prism with the two equilateral triangles and the straight line, calculate the volume of the triangular prism and mark the value of the volume as the working state value.

[0036] S6: Compare the working state value with the set threshold. When the working state value is greater than the set threshold, generate the corresponding work emergency information, reallocate each AGV according to the work emergency information and generate the corresponding collaborative adjustment instruction.

[0037] Step Five: Goods storage and handling: Mobilize the AGV according to the collaborative instruction, and the AGV performs goods storage and handling according to the collaborative instruction.

[0038] The second aspect of the present invention provides a full-degree-of-freedom AGV storage and retrieval system for an intelligent warehouse, 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.

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

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

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

[0042] The real-time monitoring and analysis module is used to monitor the storage state of goods in the warehouse and the running state of the AGV in real time, and provide functions of abnormal alarm and data analysis.

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

[0044] In the technical solution provided by the present invention, compared with the prior art, the beneficial effects are:

[0045] The present invention analyzes each goods storage and retrieval task to obtain task priority information, analyzes the AGV data to obtain AGV status information, and based on this, allocates AGV tasks for the goods storage and retrieval tasks. It also analyzes the working status data to obtain collaborative adjustment instructions for each AGV, and then controls the workload of each AGV according to the collaborative adjustment instructions, making the task allocation of each AGV more reasonable, avoiding failures caused by excessive AGV load, and ensuring the efficiency of warehousing operations. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0047] Figure 2 is a schematic block diagram of a full-degree-of-freedom AGV warehousing and storage system for an intelligent warehouse. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] Next, the technical solutions of the present invention will 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 is described below. As Figure 1 shown, in an embodiment of the present invention, an embodiment of a full-degree-of-freedom AGV warehousing and storage method for an intelligent warehouse includes the following steps:

[0050] Step 1: Task reception and preprocessing: Receive goods storage and retrieval task information and identify the task data of the goods storage and retrieval task information, and perform priority analysis on the task data of each goods storage and retrieval task to obtain task priority information.

[0051] Performing priority analysis on the task data of each goods storage and retrieval task specifically includes: identifying the task data to obtain task information and goods information. The task information includes the task source and the task deadline. Based on the task source, customer information of each goods storage and retrieval task is obtained. Multiple customer levels are set for customers based on the customer information, and each customer level corresponds to an importance index. Matching the customer information corresponding to each goods storage and retrieval task with multiple customer levels to obtain the corresponding importance index; calculating the remaining duration of each goods storage and retrieval task by comparing the task deadline corresponding to each goods storage and retrieval task with the current moment, and sorting the remaining durations of each goods storage and retrieval task from more to less to obtain the remaining duration ranking of each goods storage and retrieval task.

[0052] The goods information includes the goods value and the goods type. Obtain the goods value of each good corresponding to the goods access task, divide the goods value into multiple goods value ranges, and each goods value range corresponds to a goods priority value. Match the goods value of each good with the multiple goods value ranges to obtain the corresponding goods priority value, and sum up the goods priority values of each good in each goods access task to obtain the total goods priority value; Based on the storage timeliness of the goods, the goods types are divided into long-term storage goods, medium-term storage goods, and short-term storage goods. Each goods classification corresponds to a storage impact value. Sum up the storage impact values of the goods types corresponding to each good in the goods access task to obtain the total storage impact value; Long-term storage goods usually have high value stability, such as precious metals, antiques, etc. These goods can be stored for a long time without significant depreciation; Medium-term storage goods indicate that such goods are relatively stable and will not significantly deteriorate or depreciate within a certain period of time, but they cannot be stored for a long time. For example, some daily necessities, clothing, etc.; Short-term storage goods indicate perishable goods, such as fresh food, flowers, etc. They have a short storage timeliness and need to be processed as soon as possible to ensure quality.

[0053] Normalize the importance index, the total goods priority value, and the total storage impact value and take their numerical values. According to the formula obtain the priority status value; where mc represents the remaining duration ranking, and the value is ZY, HY, and CY respectively represent the importance index, the total goods priority value, and the total storage impact value; z1, z2, z3, and z4 are all preset weight factors, and their values are 1.97, 1.21, 0.99, and 2.02 respectively; Compare and sort the priority status values of each goods access task to obtain the task priority ranking, and obtain the corresponding priority information according to the task priority ranking of each goods access task.

[0054] Step 2: Intelligent task allocation: Obtain the priority information of each goods access task and the AGV data, analyze the AGV data to obtain the AGV status information. When the AGV status information is normal, use an intelligent algorithm to perform intelligent allocation on each goods access task to obtain the task information of each AGV.

[0055] Analyze the AGV data. The specific analysis steps are as follows:

[0056] A1: Identify the battery power data, driving speed data, and fault data by analyzing the AGV data;

[0057] A2: Obtain the remaining power percentage based on the battery power data of each AGV, acquire the preset remaining power threshold. When the remaining power percentage is less than the remaining power threshold, calculate the power difference by subtracting the remaining power percentage from the remaining power threshold. Divide the power difference into multiple power difference intervals, with each power difference interval corresponding to a power impact value. Match the power differences of each AGV with the multiple power difference intervals to obtain the corresponding power impact values.

[0058] A3: Identify the average speed, maximum speed, and minimum speed per unit time from the traveling speed data;

[0059] A31: Obtain the preset minimum speed limit per unit time, compare the average speed of each AGV per unit time with the minimum speed limit. When the average speed is less than the minimum speed limit, calculate the speed difference impact value by subtracting the average speed from the minimum speed limit;

[0060] A32: Obtain the speed fluctuation range based on the maximum speed and minimum speed of each AGV, acquire the preset speed reference range. Match the speed fluctuation ranges of each AGV with the speed reference range to obtain the upper limit excess value and lower limit excess value corresponding to the maximum speed and minimum speed. Calculate the sum of the upper limit excess value and the lower limit excess value to obtain the total excess value;

[0061] A33: Calculate the sum of the speed difference impact value and the total excess value to obtain the speed impact index.

[0062] A4: Identify the historical fault data and fault time from the fault data;

[0063] A41: Obtain the number of faults per unit time based on the historical fault data, acquire the preset fault number threshold. Compare the number of faults of each AGV with the fault number threshold. When the number of faults is greater than the fault number threshold, mark the number of faults as the fault impact value;

[0064] A42: Obtain the preset fault time threshold, compare the fault time of each AGV per unit time with the fault time threshold. When the fault time is greater than the fault time threshold, mark the corresponding fault as a high - impact fault, count the number of high - impact faults and mark it as the high - impact fault number. Calculate the ratio of the high - impact fault number to the total number of faults per unit time to obtain the high - impact fault ratio;

[0065] A43: Calculate the product of the fault impact value and the high - impact fault ratio to obtain the fault impact index.

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

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

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

[0069] Step 4: Cooperative operation control: Obtain the working status data of each AGV, and analyze the working status data to obtain the cooperative adjustment instructions for each AGV.

[0070] Analyze the working status data. The specific analysis steps are as follows:

[0071] S1: Identify the task status, operating load, and task progress data by analyzing the working status data.

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

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

[0074] S4: Identify the total task amount, task completion amount, and task time data from the task progress data.

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

[0076] S42: Obtain the task deadline and the current time point based on the task time data, calculate the difference between the task deadline and the current time point 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, and match the remaining task time with the multiple remaining time intervals to obtain the corresponding remaining time shadow value;

[0077] S43: Add the task impact value and the remaining time shadow value to calculate the progress impact value.

[0078] S5: Construct two equilateral triangles with the values of the busy state value and the energy consumption impact value as the sides of the equilateral triangle. Use the centroids of the two equilateral triangles as the starting point and the ending point, 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 value, and then construct a triangular prism with the two equilateral triangles and the straight line, calculate the volume of the triangular prism and mark the value of the volume as the working state value.

[0079] S6: Compare the working state value with the set threshold. When the working state value is greater than the set threshold, generate the corresponding work emergency information, and reallocate each AGV according to the work emergency information and generate the corresponding collaborative adjustment instruction.

[0080] Step Five: Goods storage and handling: Mobilize the AGV according to the collaborative instruction, and the AGV performs the storage and handling of goods according to the collaborative instruction.

[0081] As Figure 2 shown, the present invention also provides a full-degree-of-freedom AGV storage and retrieval system for an intelligent warehouse, 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 the task information and analyzes the task information to obtain the task priority information.

[0083] The path planning and navigation module plans and real-time navigates the path of the AGV.

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

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

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

[0087] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; those of ordinary skill in the art can modify or equivalently replace the technical solutions recorded in the foregoing embodiments; and these modifications or replacements 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 full-degree-of-freedom AGV storage and retrieval method for an intelligent warehouse, characterized in that, It includes the following steps: Step 1: Receive the goods storage and retrieval task information, identify the task data from the goods storage and retrieval task information, and perform priority analysis on the task data of each goods storage and retrieval task to obtain task priority information; Step 2: Obtain the priority information and AGV data of each goods storage and retrieval task, analyze the AGV data to obtain AGV status information. When the AGV status information is normal, perform intelligent allocation on each goods storage and retrieval task through an intelligent algorithm to obtain the task information of each AGV; Step 3: Obtain the location information and task information of each AGV, and perform path planning and navigation based on the location information and task information of each AGV; Step 4: Obtain the working status data of each AGV, and analyze the working status data to obtain the cooperative adjustment instructions of each AGV; Step 5: Mobilize the AGV according to the cooperative instructions, and the AGV performs the storage, retrieval and handling of goods according to the cooperative instructions.

2. The full-degree-of-freedom AGV storage and retrieval method for an intelligent warehouse according to claim 1, wherein The specific method for performing priority analysis on the task data of each goods storage and retrieval task is as follows: Identify the task information and goods information from the task data. The task information includes the task source and task deadline. Obtain the customer information of each goods storage and retrieval task according to the task source. Based on the customer information, set multiple customer levels for the customers. Each customer level corresponds to an importance index. Match the customer information corresponding to each goods storage and retrieval task with the multiple customer levels to obtain the corresponding importance index; Calculate the remaining duration of each goods storage and retrieval task by calculating the task deadline corresponding to each goods storage and retrieval task and the current time. Sort the remaining durations of each goods storage and retrieval task from more to less to obtain the remaining duration ranking of each goods storage and retrieval task; The goods information includes the goods value and goods type. Obtain the goods value of each goods corresponding to the goods storage and retrieval task. Divide the goods value into multiple goods value intervals. Each goods value interval corresponds to a goods priority value. Match the goods value of each goods with the multiple goods value intervals to obtain the corresponding goods priority value. Add up the goods priority values of each goods in each goods storage and retrieval task to obtain the total goods priority value; Classify the goods types into long-term storage goods, medium-term storage goods and short-term storage goods based on the storage timeliness of the goods. Each goods classification corresponds to a storage impact value. Add up the storage impact values of the goods types corresponding to each goods in the goods storage and retrieval task to obtain the total storage impact value; Perform comprehensive calculation on the importance index, total goods priority value and total storage impact value to obtain the priority status value; Compare and sort the priority status values of each goods storage and retrieval task to obtain the task priority ranking, and obtain the corresponding priority information according to the task priority ranking of each goods storage and retrieval task.

3. The full-degree-of-freedom AGV storage and retrieval method for an intelligent warehouse according to claim 1, characterized in that, The specific analysis steps for analyzing the AGV data are as follows: A1: Identify the battery power data, driving speed data and fault data from the AGV data; A2: Obtain the remaining power percentage based on the battery power data of each AGV, obtain the preset remaining power threshold. When the remaining power percentage is less than the remaining power threshold, calculate the difference between the remaining power threshold and the remaining power percentage to obtain the power difference. Divide the power difference into multiple power difference intervals, and each power difference interval corresponds to a power influence value. Match the power differences of each AGV with the multiple power difference intervals to obtain the corresponding power influence values; 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 per unit time, compare the average speed of each AGV per unit time with the minimum speed limit. When the average speed is less than the minimum speed limit, calculate the difference between the minimum speed limit and the average speed to obtain the speed difference influence value; A32: Obtain the speed fluctuation range based on the maximum speed and minimum speed of each AGV, obtain the preset speed reference range, perform overlapping matching between the speed fluctuation ranges of each AGV and the speed reference range to obtain the upper limit exceedance value and lower limit exceedance value corresponding to the maximum speed and minimum speed, and calculate the sum of the upper limit exceedance value and the lower limit exceedance value to obtain the total exceedance value; A33: Calculate the sum of the speed difference influence value and the total exceedance value to obtain the speed influence index; A4: Identify the fault data to obtain the historical fault data and fault time; A41: Obtain the number of faults per unit time based on the historical fault data, obtain the preset fault number threshold, compare the number of faults of each AGV with the fault number threshold. When the number of faults is greater than the fault number threshold, mark the number of faults as the fault influence value; A42: Obtain the preset fault time threshold, compare the fault time of each AGV's faults per unit time with the fault time threshold. When the fault time is greater than the fault time threshold, mark the corresponding fault as a high - impact fault, count the number of high - impact faults and mark it as the high - impact fault number, and calculate the ratio of the high - impact fault number to the total number of faults per unit time to obtain the high - impact fault ratio; A43: Calculate the product of the fault influence value and the high - impact fault ratio to obtain the fault influence index; A5: Construct a right - angled triangle with the values of the power influence value and the speed influence index as the two right - angled sides of the right - angled triangle. Starting from the intersection point of the two right - angled sides, draw a straight line perpendicular to the right - angled triangle, and the length of the straight line is equal to the value of the fault influence index. Then construct a triangular pyramid with the right - angled triangle and the straight line, calculate the volume of the triangular pyramid and mark the value of the volume as the AGV status value; A6: When the AGV status value is greater than the set threshold, generate the corresponding AGV status information as status abnormal.

4. The full-degree-of-freedom AGV storage and retrieval method for an intelligent warehouse according to claim 1, wherein, The analysis of the working state data is specifically as follows: S1: Identify the task status, operating load, and task progress data by analyzing the working state data; S2: Set the task status of the AGV to the working status and the idle status. When the task status of the AGV corresponds to the working status, mark the corresponding AGV as a working AGV, and record the working duration of each working AGV per unit time; calculate the ratio of the working duration to the total running duration to obtain the working ratio, obtain the preset working ratio threshold, and compare the working ratio with the working ratio threshold. When the working ratio is greater than the working ratio threshold, mark the corresponding working ratio as the busy status value; S3: Obtain the energy consumption value of each AGV per unit time according to the running load, compare the energy consumption value per unit time with the set threshold, and when the energy consumption value of each AGV per unit time is greater than the set threshold, mark the corresponding energy consumption value as the energy consumption impact value; S4: Identify the task progress data to obtain the total task amount, the completed task amount, and the task time data; S41: Calculate the difference between the total task amount and the completed task amount to obtain the remaining task amount, divide the remaining task amount into multiple remaining task amount intervals, each remaining task amount interval corresponds to a task impact value, and match the remaining task amount corresponding to each task with the multiple remaining task amount intervals to obtain the corresponding task impact value; S42: Obtain the task deadline and the current time point based on the task time data, calculate the difference between the task deadline and the current time point 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 impact value, and match the remaining task time with the multiple remaining time intervals to obtain the corresponding remaining time impact value; S43: Add the task impact value and the remaining time impact value to calculate the progress impact value; S5: Construct two equilateral triangles with the values of the busy status value and the energy consumption impact value as the sides of the equilateral triangle. Starting from and ending at the centroids of the two equilateral triangles, 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 value. Then construct a triangular prism with the two equilateral triangles and the straight line, calculate the volume of the triangular prism and mark the value of the volume 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 work emergency information, re-allocate each AGV according to the work emergency information and generate the corresponding collaborative adjustment instruction.

5. An AGV storage and retrieval system with full degrees of freedom for an intelligent warehouse, characterized in that, Applying the full-degree-of-freedom AGV storage and retrieval method for an intelligent warehouse according to any one of claims 1-4, comprising: A task management analysis module, configured to receive task information and analyze the task information to obtain task priority information; A path planning and navigation module, configured to plan and navigate the path of the AGV in real time; An execution control module, configured to control the collaborative operation control of multiple AGVs and adjust and control the tasks of each AGV; A real-time monitoring and analysis module, configured to monitor the storage status of goods in the warehouse and the running status of AGVs in real time, and provide functions of abnormal alarm and data analysis; A database, configured to receive and store the data information of each module.

Images