AGV task allocation method and system

Abstract

The invention discloses an AGV task allocation method and system, and relates to the technical field of automatic navigation trucks, and the method comprises the steps: carrying out the initialization of AGV state information; acquiring environment information in real time through ground sensor equipment; performing priority evaluation on the task instructions, and adjusting task priorities in real time according to emergencies; task merging and splitting are carried out; task allocation is carried out by using an ant colony algorithm, and a task allocation result is output to the corresponding AGV to guide the AGV to execute operation according to the allocated task; an optimal path is planned for each AGV, and path planning is dynamically adjusted in the task execution process of the AGVs; and optimizing a task allocation algorithm by adjusting a pheromone volatilization coefficient. Limited AGV resources are reasonably scheduled by arranging a task instruction obtaining module, a task information processing module and a task planning and distributing module, high environment perception and adaptability are achieved, and task priorities and path planning can be rapidly adjusted.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic guided vehicle (AGV), and in particular to an AGV task allocation method and system. Background Art

[0002] Automated logistics systems play a vital role in modern warehousing and manufacturing. Automated guided vehicles, as core equipment, are responsible for tasks such as material handling and assembly line distribution, greatly improving production efficiency and flexibility. However, with the expansion of production scale and the increase in task complexity, how to efficiently and intelligently allocate AGV tasks to ensure the smooth operation of the logistics system has become a problem to be solved.

[0003] In large warehouses or complex production lines, AGVs need to handle a large number of tasks of various types, including handling tasks of different priorities, different destinations, and different load requirements. The environment inside the warehouse or factory (such as road conditions, obstacle locations) may change frequently due to equipment movement, temporary construction, etc., requiring the AGV system to have a high degree of environmental perception and adaptability. In order to improve overall efficiency, it is necessary to reasonably schedule limited AGV resources to avoid idleness, congestion or overload, while considering the power management of AGVs to ensure continuous operation capabilities. In the face of emergencies in the production process (such as emergency orders, equipment failures), the system needs to be able to quickly adjust task priorities and path planning to ensure the smooth execution of production plans. Summary of the invention

[0004] In order to solve the above technical problems, a method and system for allocating AGV tasks are provided. This technical solution solves the problems raised in the above background technology.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is: An AGV task allocation method, comprising: The AGV dispatch center receives the task instructions from the warehouse management center and initializes the AGV status information, which includes the location, power and current task status; Acquire environmental information in real time through ground sensor equipment and monitor the real-time status information of AGV, wherein the environmental information includes road conditions and obstacle location information; Prioritize task instructions based on the urgency and importance of the task, and adjust task priorities in real time according to emergencies during the production process; For task instructions that can be processed in parallel, tasks are merged. For task instructions with waypoints greater than a preset threshold or with a total mass of goods to be transported greater than a preset mass threshold, they are split and assigned to at least two AGVs for execution; Based on task priority, AGV status information and environmental information, the ant colony algorithm is used to assign tasks, and the task assignment results are output to the corresponding AGV to guide it to perform operations according to the assigned tasks; After the task is assigned, the optimal path is planned for each AGV based on the real-time environment information and AGV status information, and the path planning is dynamically adjusted according to the changes in the environment, AGV status and priority during the AGV task execution; The AGV dispatch center monitors the driving status and task progress of resources in real time, and optimizes the task allocation algorithm by adjusting the pheromone volatility coefficient.

[0006] Preferably, the AGV dispatch center receives the task instruction from the warehouse management center and initializes the AGV status information, specifically including: The warehouse management center sends the task instructions to the AGV dispatch center through the communication connection, and the task instructions include handling tasks, sorting tasks and storage tasks; The AGV dispatch center parses the verified task instructions and extracts key information from the instructions, including task type, target location, and cargo information; After the AGV is turned on, it performs a self-test to check the working status of each vehicle's subsystems and sensors; The AGV obtains its current position through the built-in positioning chip and reports it to the AGV dispatch center; The AGV obtains the current power information through the battery management system and reports it to the AGV dispatch center; The AGV reports whether it is currently executing a task, the task type, and task progress information.

[0007] Preferably, the priority evaluation of task instructions based on the urgency and importance of the task, and the real-time adjustment of task priorities according to emergencies during the production process specifically include: Output each task instruction to the xy coordinate system, where the horizontal axis is the urgency index of the task instruction and the vertical axis is the importance index of the task instruction; Analyze the urgency and importance of tasks based on order deadlines, production line requirements, and inventory level information; Outputting the distance from the midpoint of the coordinate system corresponding to each task instruction to the origin as the priority of the task instruction, and recording the determined priority information in the task instruction; Through the data acquisition system, real-time monitoring and tracking of task execution, equipment status and inventory change information in the production process; Based on the monitoring information, identify various emergencies that affect task execution, including equipment failure, material shortages, and order changes; Assess the impact of unexpected situations on task priorities based on the likelihood of task delays, impact on the production line, and potential impact on customer satisfaction; According to the evaluation results, the priorities of the affected tasks are adjusted, the adjusted priority information is updated to the task instructions, and the AGV dispatch center is notified.

[0008] Preferably, the task instructions that can be processed in parallel are merged, and the task instructions whose waypoints are greater than a preset threshold or whose total mass of the goods to be transported is greater than a preset mass threshold are split and assigned to at least two AGVs for execution specifically include: Receive task instructions from the warehouse management center; Analyze the received task instructions and identify the task instructions that can be executed overlappingly in space and time; Merge the identified tasks that can be processed in parallel to form a new task instruction; Set preset thresholds based on the navigation capability, driving speed, and battery life of the AGV, obtain the maximum carrying capacity of the AGV and output it as a preset quality threshold; Determine whether the waypoints of the task instruction are greater than the preset threshold or whether the total mass of the goods to be transported is greater than the preset mass threshold. If so, split the task into at least two subtasks. If not, do not output it.

[0009] Preferably, the method of using an ant colony algorithm to perform task allocation based on task priority, AGV status information and environmental information, and outputting the task allocation result to the corresponding AGV to guide it to perform operations according to the allocated task specifically includes: S101: Preset the number of ant colonies, pheromone volatility coefficient and maximum number of iterations; S102: setting the inverse of the task priority as the ant colony weight, where the ant colony weight determines the probability of the ants selecting a path; S103: taking each task instruction and AGV as a node of the solution space; S104: constructing edges according to the matching relationship between the task instructions and the AGVs, and calculating the weight of the edges based on the path length and time cost; S105: Create a two-dimensional pheromone matrix to record the pheromone concentration on the path between the task instruction and the AGV; S106: During initialization, the pheromone concentration on all paths can be set to 0, and each ant randomly selects a task from the task set as the starting point; S107: Calculate the probability of the ant transferring from the current task to the next AGV based on the pheromone concentration and the task priority weight; S108: After each ant completes a path selection, the pheromone matrix is ​​updated based on the size of the preset pheromone volatility coefficient; S109: Determine whether the preset maximum number of iterations has been reached. If so, output the optimal solution with the lowest total time cost. If not, return to step S107. S110: Outputting the optimal task allocation plan in the form of a list, wherein the list includes the task number, the allocated AGV number, the starting position and the target position information; S111: Send the cargo type, quantity, location and transportation destination in the task allocation result to the corresponding AGV via the wireless network.

[0010] Preferably, after the task is assigned, the optimal path is planned for each AGV based on the real-time environment information and AGV status information, and during the AGV task execution, the path planning is dynamically adjusted according to the environment changes, AGV status changes and priority changes. Specifically, it includes: S201: Based on the acquired environmental information, a factory environmental model is constructed using a grid map method, and walkable areas, obstacle locations, and path node information are marked in the model; S202: Set the distance of the starting point to 0, and the distances of other nodes to infinity; S203: Add the starting point to the visited node set, and add other nodes to the unvisited node set; S204: Selecting a node closest to the current node from the unvisited node set as a new current node; S205: Update the distance value of the adjacent node of the current node, and determine whether the distance from the current node to the adjacent node is shorter than the distance from the current node to the adjacent node directly. If so, update the distance value and record the predecessor node information. If not, do not output it. S206: remove the current node from the unvisited node set and add it to the visited node set; S207: Determine whether the passing point and the destination of the task are both in the visited node set. If so, end the iteration and trace back from the end point to the starting point according to the predecessor node matrix to reconstruct the shortest path. If not, return to step S204; S208: Based on the real-time power change of the AGV, determine whether the current path will cause the AGV to be short of power. If so, the AGV returns to the charging cabin for charging and reassigns tasks. If not, no output is made. S209: Based on the real-time load status of the AGV, determine whether the load of the AGV is higher than the preset load. If so, re-plan a flat and easy-to-drive path. If not, no output is made. S210: During the AGV task execution, determine whether a new obstacle or path change is detected. If so, return to step S201; if not, do not output; S211: During the AGV task execution process, determine whether a task priority change is detected. If so, reallocate the task; if not, do not output.

[0011] Furthermore, an AGV task allocation system is proposed, which is used to implement the above-mentioned AGV task allocation method, including: A task instruction acquisition module, wherein the task instruction acquisition module includes an AGV dispatch center receiving task instructions from a warehouse management center, initializing AGV status information, acquiring environmental information in real time through ground sensor equipment, and monitoring the real-time status information of the AGV, wherein the environmental information includes road conditions and obstacle location information; A task information processing module, which includes a task information processing module that evaluates the priority of task instructions based on the urgency and importance of the task, and adjusts the task priority in real time according to emergencies during the production process, merges tasks for task instructions that can be processed in parallel, and splits and assigns task instructions with waypoints greater than a preset threshold or a total mass of the goods to be transported greater than a preset mass threshold to at least two AGVs for execution; The task planning and allocation module includes task allocation based on task priority, AGV status information and environmental information, using ant colony algorithm, and outputting the task allocation results to the corresponding AGV to guide it to perform operations according to the assigned tasks. After the task is allocated, the optimal path is planned for each AGV based on real-time environmental information and AGV status information. During the AGV task execution process, the path planning is dynamically adjusted according to environmental changes, AGV status changes and priority changes. The AGV dispatch center monitors the driving status and task progress of resources in real time, and optimizes the task allocation algorithm by adjusting the pheromone volatility coefficient.

[0012] Compared with the prior art, the present invention has the following beneficial effects: Tasks can be dynamically and efficiently assigned based on task priority, AGV status and environmental information, significantly improving the overall efficiency of the system, dynamically adjusting path planning, effectively avoiding congestion, optimizing driving paths, and ensuring that tasks are completed on time. Task priority, AGV power and other status information are comprehensively considered to reasonably assign tasks, effectively manage resources, reduce idle and overload phenomena, and improve resource utilization. The system's adaptability is enhanced by real-time evaluation of task priority, dynamic adjustment of path planning and task assignment, ensuring efficient operation in complex and changing production environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1It is a flow chart of the AGV task allocation method of the present invention; Figure 2 It is a flow chart of the method for initializing the AGV status information of the present invention; Figure 3 A flow chart of a method for evaluating the priority of a task instruction according to the present invention; Figure 4 A flow chart of the method for merging and splitting tasks of the present invention; Figure 5 This is a flow chart of the method for performing task allocation using ant colony algorithm of the present invention; Figure 6 This is a flow chart of a method for dynamically adjusting path planning according to environmental changes, AGV state changes, and priority changes of the present invention. DETAILED DESCRIPTION

[0014] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0015] Reference Figure 1 As shown, an AGV task allocation method includes: The AGV dispatch center receives the task instructions from the warehouse management center and initializes the AGV status information, which includes the location, power and current task status; Acquire environmental information in real time through ground sensor equipment and monitor the real-time status information of AGV, wherein the environmental information includes road conditions and obstacle location information; Prioritize task instructions based on the urgency and importance of the task, and adjust task priorities in real time according to emergencies during the production process; For task instructions that can be processed in parallel, tasks are merged. For task instructions with waypoints greater than a preset threshold or with a total mass of goods to be transported greater than a preset mass threshold, they are split and assigned to at least two AGVs for execution; Based on task priority, AGV status information and environmental information, the ant colony algorithm is used to assign tasks, and the task assignment results are output to the corresponding AGV to guide it to perform operations according to the assigned tasks; After the task is assigned, the optimal path is planned for each AGV based on the real-time environment information and AGV status information, and the path planning is dynamically adjusted according to the changes in the environment, AGV status and priority during the AGV task execution; The AGV dispatch center monitors the driving status and task progress of resources in real time, and optimizes the task allocation algorithm by adjusting the pheromone volatility coefficient.

[0016] Reference Figure 2 As shown, the AGV dispatch center receives the task instructions from the warehouse management center and initializes the AGV status information, specifically including: The warehouse management center sends the task instructions to the AGV dispatch center through the communication connection, and the task instructions include handling tasks, sorting tasks and storage tasks; The AGV dispatch center parses the verified task instructions and extracts key information from the instructions, including task type, target location, and cargo information; After the AGV is turned on, it performs a self-test to check the working status of each vehicle's subsystems and sensors; The AGV obtains its current position through the built-in positioning chip and reports it to the AGV dispatch center; The AGV obtains the current power information through the battery management system and reports it to the AGV dispatch center; The AGV reports whether it is currently executing a task, the task type, and task progress information.

[0017] The warehouse management center generates task instructions through its management system based on the warehouse operation needs. These instructions include but are not limited to handling tasks (moving specified goods from point A to point B), sorting tasks (picking specific goods from the shelves to the designated area), and storage tasks (placing goods in the designated storage location).

[0018] Reference Figure 3 As shown in the figure, based on the urgency and importance of the task, the task instructions are prioritized and the task priorities are adjusted in real time according to emergencies during the production process. Specifically, they include: Output each task instruction to the xy coordinate system, where the horizontal axis is the urgency index of the task instruction and the vertical axis is the importance index of the task instruction; Analyze the urgency and importance of tasks based on order deadlines, production line requirements, and inventory level information; Outputting the distance from the midpoint of the coordinate system corresponding to each task instruction to the origin as the priority of the task instruction, and recording the determined priority information in the task instruction; Through the data acquisition system, real-time monitoring and tracking of task execution, equipment status and inventory change information in the production process; Based on the monitoring information, identify various emergencies that affect task execution, including equipment failure, material shortages, and order changes; Assess the impact of unexpected situations on task priorities based on the likelihood of task delays, impact on the production line, and potential impact on customer satisfaction; According to the evaluation results, the priorities of the affected tasks are adjusted, the adjusted priority information is updated to the task instructions, and the AGV dispatch center is notified.

[0019] Design an xy coordinate system, where the horizontal axis represents the urgency index of the task instruction, and the length of time until the order deadline is calculated in hours or days. The vertical axis represents the importance index of the task instruction, which is comprehensively evaluated based on factors such as order value and customer demand urgency.

[0020] Reference Figure 4 As shown, for task instructions that can be processed in parallel, tasks are merged, and for task instructions whose waypoints are greater than a preset threshold or whose total mass of the goods to be transported is greater than a preset mass threshold, tasks are split and assigned to at least two AGVs for execution, specifically including: Receive task instructions from the warehouse management center; Analyze the received task instructions and identify the task instructions that can be executed overlappingly in space and time; Merge the identified tasks that can be processed in parallel to form a new task instruction; Set preset thresholds based on the navigation capability, driving speed, and battery life of the AGV, obtain the maximum carrying capacity of the AGV and output it as a preset quality threshold; Determine whether the waypoints of the task instruction are greater than the preset threshold or whether the total mass of the goods to be transported is greater than the preset mass threshold. If so, split the task into at least two subtasks. If not, do not output it.

[0021] Each task instruction received is analyzed, focusing on the spatial location of the task, including the starting point, end point, transit points and time requirements, to identify whether there are opportunities for parallel processing. The spatial relationship of the tasks is determined using geographic information systems or indoor positioning technology, combined with timestamp information, and the spatiotemporal overlap detection algorithm is used to determine which tasks can be executed in parallel.

[0022] Reference Figure 5 As shown in the figure, based on the task priority, AGV status information and environmental information, the ant colony algorithm is used to allocate tasks, and the task allocation results are output to the corresponding AGV to guide it to perform operations according to the assigned tasks. Specifically, the following are included: S101: Preset the number of ant colonies, pheromone volatility coefficient and maximum number of iterations; S102: setting the inverse of the task priority as the ant colony weight, where the ant colony weight determines the probability of the ants selecting a path; S103: taking each task instruction and AGV as a node of the solution space; S104: constructing edges according to the matching relationship between the task instructions and the AGVs, and calculating the weight of the edges based on the path length and time cost; S105: Create a two-dimensional pheromone matrix to record the pheromone concentration on the path between the task instruction and the AGV; S106: During initialization, the pheromone concentration on all paths can be set to 0, and each ant randomly selects a task from the task set as the starting point; S107: Calculate the probability of the ant transferring from the current task to the next AGV based on the pheromone concentration and the task priority weight; S108: After each ant completes a path selection, the pheromone matrix is ​​updated based on the size of the preset pheromone volatility coefficient; S109: Determine whether the preset maximum number of iterations has been reached. If so, output the optimal solution with the lowest total time cost. If not, return to step S107. S110: Outputting the optimal task allocation plan in the form of a list, wherein the list includes the task number, the allocated AGV number, the starting position and the target position information; S111: Send the cargo type, quantity, location and transportation destination in the task allocation result to the corresponding AGV via the wireless network.

[0023] The walking paths of ants represent feasible solutions to the problem to be optimized. All paths of the entire ant colony constitute the solution space of the problem to be optimized. Ants with shorter paths release more pheromones. As time goes by, the accumulated pheromone concentration on the shorter paths gradually increases, and the number of ants choosing this path also increases. Eventually, the entire ant colony will concentrate on the best path under the action of positive feedback, which corresponds to the optimal solution to the problem to be optimized.

[0024] Reference Figure 6 As shown in the figure, after the task is assigned, the optimal path is planned for each AGV based on the real-time environment information and AGV status information, and during the AGV task execution, the path planning is dynamically adjusted according to the changes in the environment, AGV status and priority. Specifically, it includes: S201: Based on the acquired environmental information, a factory environmental model is constructed using a grid map method, and walkable areas, obstacle locations, and path node information are marked in the model; S202: Set the distance of the starting point to 0, and the distances of other nodes to infinity; S203: Add the starting point to the visited node set, and add other nodes to the unvisited node set; S204: Selecting a node closest to the current node from the unvisited node set as a new current node; S205: Update the distance value of the adjacent node of the current node, and determine whether the distance from the current node to the adjacent node is shorter than the distance from the current node to the adjacent node directly. If so, update the distance value and record the predecessor node information. If not, do not output it. S206: remove the current node from the unvisited node set and add it to the visited node set; S207: Determine whether the passing point and the destination of the task are both in the visited node set. If so, end the iteration and trace back from the end point to the starting point according to the predecessor node matrix to reconstruct the shortest path. If not, return to step S204; S208: Based on the real-time power change of the AGV, determine whether the current path will cause the AGV to be short of power. If so, the AGV returns to the charging cabin for charging and reassigns tasks. If not, no output is made. S209: Based on the real-time load status of the AGV, determine whether the load of the AGV is higher than the preset load. If so, re-plan a flat and easy-to-drive path. If not, no output is made. S210: During the AGV task execution, determine whether a new obstacle or path change is detected. If so, return to step S201; if not, do not output; S211: During the AGV task execution process, determine whether a task priority change is detected. If so, reallocate the task; if not, do not output.

[0025] In the graph model, each point is represented as a node, and the connection relationship between nodes is represented as an edge. The weight of the edge can represent the distance and time between two points. Based on the information of nodes and edges, an adjacency list or adjacency matrix of the graph is created to represent the connection relationship between nodes.

[0026] Furthermore, based on the same inventive concept as the above-mentioned AGV task allocation method, the present solution also proposes an AGV task allocation system, including: A task instruction acquisition module, wherein the task instruction acquisition module includes an AGV dispatch center receiving task instructions from a warehouse management center, initializing AGV status information, acquiring environmental information in real time through ground sensor equipment, and monitoring the real-time status information of the AGV, wherein the environmental information includes road conditions and obstacle location information; A task information processing module, which includes a task information processing module that evaluates the priority of task instructions based on the urgency and importance of the task, and adjusts the task priority in real time according to emergencies during the production process, merges tasks for task instructions that can be processed in parallel, and splits and assigns task instructions with waypoints greater than a preset threshold or a total mass of the goods to be transported greater than a preset mass threshold to at least two AGVs for execution; The task planning and allocation module includes task allocation based on task priority, AGV status information and environmental information, using ant colony algorithm, and outputting the task allocation results to the corresponding AGV to guide it to perform operations according to the assigned tasks. After the task is allocated, the optimal path is planned for each AGV based on real-time environmental information and AGV status information. During the AGV task execution process, the path planning is dynamically adjusted according to environmental changes, AGV status changes and priority changes. The AGV dispatch center monitors the driving status and task progress of resources in real time, and optimizes the task allocation algorithm by adjusting the pheromone volatility coefficient.

[0027] Furthermore, the present solution also proposes a computer-readable storage medium on which a computer-readable program is stored. When the computer-readable program is called, the above-mentioned AGV task allocation method is executed.

[0028] It is understandable that the storage medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid state drive (SSD).

[0029] To sum up, the advantages of the present invention are: tasks can be dynamically and efficiently allocated according to task priority, AGV status and environmental information, significantly improving the overall efficiency of the system, dynamically adjusting path planning, effectively avoiding congestion, optimizing driving paths, ensuring that tasks are completed on time, comprehensively considering task priority, AGV power and other status information, reasonably allocating tasks, effectively managing resources, reducing idle and overload phenomena, and improving resource utilization; by real-time evaluation of task priority, dynamic adjustment of path planning and task allocation, the system's adaptability is enhanced to ensure efficient operation in a complex and changing production environment.

[0030] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. An AGV task allocation method, characterized in that: include: The AGV dispatch center receives the task instructions from the warehouse management center and initializes the AGV status information, which includes the location, power and current task status; Acquire environmental information in real time through ground sensor equipment and monitor the real-time status information of AGV, wherein the environmental information includes road conditions and obstacle location information; Prioritize task instructions based on the urgency and importance of the task, and adjust task priorities in real time according to emergencies during the production process; For task instructions that can be processed in parallel, tasks are merged. For task instructions with waypoints greater than a preset threshold or with a total mass of goods to be transported greater than a preset mass threshold, they are split and assigned to at least two AGVs for execution; Based on task priority, AGV status information and environmental information, the ant colony algorithm is used to assign tasks, and the task assignment results are output to the corresponding AGV to guide it to perform operations according to the assigned tasks; After the task is assigned, the optimal path is planned for each AGV based on the real-time environment information and AGV status information, and the path planning is dynamically adjusted according to the changes in the environment, AGV status and priority during the AGV task execution; The AGV dispatch center monitors the driving status and task progress of resources in real time, and optimizes the task allocation algorithm by adjusting the pheromone volatility coefficient.

2. The AGV task allocation method according to claim 1, characterized in that: The AGV dispatch center receives the task instruction from the warehouse management center and initializes the AGV status information, specifically including: The warehouse management center sends the task instructions to the AGV dispatch center through the communication connection, and the task instructions include handling tasks, sorting tasks and storage tasks; The AGV dispatch center parses the verified task instructions and extracts key information from the instructions, including task type, target location, and cargo information; After the AGV is turned on, it performs a self-test to check the working status of each vehicle's subsystems and sensors; The AGV obtains its current position through the built-in positioning chip and reports it to the AGV dispatch center; The AGV obtains the current power information through the battery management system and reports it to the AGV dispatch center; The AGV reports whether it is currently executing a task, the task type, and task progress information.

3. The AGV task allocation method according to claim 2, characterized in that: The priority of task instructions is evaluated based on the urgency and importance of the task, and the task priority is adjusted in real time according to emergencies during the production process. Specifically, it includes: Output each task instruction to the xy coordinate system, where the horizontal axis is the urgency index of the task instruction and the vertical axis is the importance index of the task instruction; Analyze the urgency and importance of tasks based on order deadlines, production line requirements, and inventory level information; Outputting the distance from the midpoint of the coordinate system corresponding to each task instruction to the origin as the priority of the task instruction, and recording the determined priority information in the task instruction; Through the data acquisition system, real-time monitoring and tracking of task execution, equipment status and inventory change information in the production process; Based on the monitoring information, identify various emergencies that affect task execution, including equipment failure, material shortages, and order changes; Assess the impact of unexpected situations on task priorities based on the likelihood of task delays, impact on the production line, and potential impact on customer satisfaction; According to the evaluation results, the priorities of the affected tasks are adjusted, the adjusted priority information is updated to the task instructions, and the AGV dispatch center is notified.

4. The AGV task allocation method according to claim 3, characterized in that: The task instructions that can be processed in parallel are merged, and the task instructions whose waypoints are greater than a preset threshold or whose total weight of the goods to be transported is greater than a preset weight threshold are split and assigned to at least two AGVs for execution specifically include: Receive task instructions from the warehouse management center; Analyze the received task instructions and identify the task instructions that can be executed overlappingly in space and time; Merge the identified tasks that can be processed in parallel to form a new task instruction; Set preset thresholds based on the navigation capability, driving speed, and battery life of the AGV, obtain the maximum carrying capacity of the AGV and output it as a preset quality threshold; Determine whether the waypoints of the task instruction are greater than the preset threshold or whether the total mass of the goods to be transported is greater than the preset mass threshold. If so, split the task into at least two subtasks. If not, do not output it.

5. The AGV task allocation method according to claim 4, characterized in that: The method of using the ant colony algorithm to perform task allocation based on task priority, AGV status information and environmental information, and outputting the task allocation result to the corresponding AGV to guide it to perform operations according to the allocated task specifically includes: S101: Preset the number of ant colonies, pheromone volatility coefficient and maximum number of iterations; S102: setting the inverse of the task priority as the ant colony weight, where the ant colony weight determines the probability of the ants selecting a path; S103: taking each task instruction and AGV as a node of the solution space; S104: constructing edges according to the matching relationship between the task instructions and the AGVs, and calculating the weight of the edges based on the path length and time cost; S105: Create a two-dimensional pheromone matrix to record the pheromone concentration on the path between the task instruction and the AGV; S106: During initialization, the pheromone concentration on all paths can be set to 0, and each ant randomly selects a task from the task set as the starting point; S107: Calculate the probability of the ant transferring from the current task to the next AGV based on the pheromone concentration and the task priority weight; S108: After each ant completes a path selection, the pheromone matrix is ​​updated based on the size of the preset pheromone volatility coefficient; S109: Determine whether the preset maximum number of iterations has been reached. If so, output the optimal solution with the lowest total time cost. If not, return to step S107. S110: Outputting the optimal task allocation plan in the form of a list, wherein the list includes the task number, the allocated AGV number, the starting position and the target position information; S111: Send the cargo type, quantity, location and transportation destination in the task allocation result to the corresponding AGV via the wireless network.

6. The AGV task allocation method according to claim 5, characterized in that: After the task is assigned, the optimal path is planned for each AGV based on the real-time environment information and AGV status information, and the path planning is dynamically adjusted according to the environment changes, AGV status changes and priority changes during the AGV task execution process. Specifically, it includes: S201: Based on the acquired environmental information, a factory environmental model is constructed using a grid map method, and walkable areas, obstacle locations, and path node information are marked in the model; S202: Set the distance of the starting point to 0, and the distances of other nodes to infinity; S203: Add the starting point to the visited node set, and add other nodes to the unvisited node set; S204: Selecting a node closest to the current node from the unvisited node set as a new current node; S205: Update the distance value of the adjacent node of the current node, and determine whether the distance from the current node to the adjacent node is shorter than the distance from the current node to the adjacent node directly. If so, update the distance value and record the predecessor node information. If not, do not output it. S206: remove the current node from the unvisited node set and add it to the visited node set; S207: Determine whether the passing point and the destination of the task are both in the visited node set. If so, end the iteration and trace back from the end point to the starting point according to the predecessor node matrix to reconstruct the shortest path. If not, return to step S204; S208: Based on the real-time power change of the AGV, determine whether the current path will cause the AGV to be short of power. If so, the AGV returns to the charging cabin for charging and reassigns tasks. If not, no output is made. S209: Based on the real-time load status of the AGV, determine whether the load of the AGV is higher than the preset load. If so, re-plan a flat and easy-to-drive path. If not, no output is made. S210: During the AGV task execution, determine whether a new obstacle or path change is detected. If so, return to step S201; if not, do not output; S211: During the AGV task execution process, determine whether a task priority change is detected. If so, reallocate the task; if not, do not output.

7. An AGV task allocation system, used to implement the AGV task allocation method according to any one of claims 1 to 6, characterized in that: include: A task instruction acquisition module, wherein the task instruction acquisition module includes an AGV dispatch center receiving task instructions from a warehouse management center, initializing AGV status information, acquiring environmental information in real time through ground sensor equipment, and monitoring the real-time status information of the AGV, wherein the environmental information includes road conditions and obstacle location information; A task information processing module, which includes a task information processing module that evaluates the priority of task instructions based on the urgency and importance of the task, and adjusts the task priority in real time according to emergencies during the production process, merges tasks for task instructions that can be processed in parallel, and splits and assigns task instructions with waypoints greater than a preset threshold or a total mass of the goods to be transported greater than a preset mass threshold to at least two AGVs for execution; The task planning and allocation module includes task allocation based on task priority, AGV status information and environmental information, using ant colony algorithm, and outputting the task allocation results to the corresponding AGV to guide it to perform operations according to the assigned tasks. After the task is allocated, the optimal path is planned for each AGV based on real-time environmental information and AGV status information. During the AGV task execution process, the path planning is dynamically adjusted according to environmental changes, AGV status changes and priority changes. The AGV dispatch center monitors the driving status and task progress of resources in real time, and optimizes the task allocation algorithm by adjusting the pheromone volatility coefficient.

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