AGV Cluster Scheduling Method and System

Abstract

This invention relates to the field of automated guided vehicle (AGV) technology, specifically to an AGV cluster scheduling method and system. The method converts multi-source order information into a standard order format and generates order priorities based on time urgency, order source weight, material importance, and historical delivery evaluation, creating delivery tasks. Simultaneously, it acquires delivery constraints, performs feasibility analysis on delivery tasks, and generates executable tasks. Based on AGV status information, it selects the most suitable AGV to execute the executable task through a multi-dimensional attribute matching mechanism. It monitors the AGV execution status in real time; when an abnormal status is detected, it saves the task context and reselects an AGV to continue executing the task. It generates scheduling instructions, securely encapsulates the instructions, and sends them to the target AGV. The target AGV performs multi-level security checks on the received scheduling instructions before executing the task. Through a multi-source order intelligent fusion mechanism, it achieves unified processing of orders from different channels, improving the system's business adaptability.

Description

Technical Field

[0001] This invention relates to the field of automated guided vehicles (AGVs), specifically to an AGV cluster scheduling method and system, which is used to realize intelligent AGV task allocation and execution management under multi-source orders. Background Technology

[0002] With the rapid development of logistics, manufacturing, and other industries, AGVs (Automated Guided Vehicles) are widely used in factories, warehouses, hospitals, ports, and other scenarios for automated material transportation and distribution. As the scale of application expands, a single AGV can no longer meet the ever-increasing delivery demands, making AGV cluster collaborative operations an inevitable trend.

[0003] Currently, traditional AGV scheduling systems mainly suffer from the following problems: First, most adopt scheduling strategies based on simple rules, such as "nearest AGV priority" or "fixed path loop," which are inefficient in complex environments. Second, they lack the ability to intelligently integrate and process orders from multiple sources, making them unable to adapt to diverse business needs. Third, the AGV selection process often only considers distance factors, leading to uneven resource utilization, with some AGVs being overused while others are idle. Fourth, their ability to handle abnormal situations is limited; once an AGV malfunctions or is occupied, the task often cannot be completed smoothly. Finally, the security mechanisms are relatively simple, posing a risk of misoperation or malicious attacks.

[0004] To address the aforementioned issues, there is an urgent need for an AGV cluster scheduling method and system that can intelligently process multi-source orders, comprehensively select AGVs based on multiple dimensions, flexibly respond to abnormal situations, and ensure scheduling safety. Summary of the Invention

[0005] The purpose of this invention is to provide an AGV cluster scheduling method and system, which aims to solve the problems of single order processing, uneven resource allocation, poor adaptability to anomalies and insufficient security in traditional AGV scheduling systems.

[0006] This invention proposes an AGV cluster scheduling method, including:

[0007] Obtain multi-source order information, including terminal system orders, mobile client orders, inventory-triggered orders, and feedback-triggered orders;

[0008] The multi-source order information is converted into a standard order format, and order priorities are generated based on time urgency, order source weight, material importance, and historical delivery evaluation to create delivery tasks;

[0009] Obtain delivery constraints, and perform a feasibility analysis on the delivery task based on physical distance constraints, time window constraints, resource availability constraints, and path reachability constraints to generate an executable task;

[0010] Obtain AGV status information, which includes basic status information, functional parameter information, resource status information, and historical performance information;

[0011] Based on the executable task and the AGV status information, the most suitable AGV is selected to execute the executable task through a multi-dimensional attribute matching mechanism. The multi-dimensional attribute matching mechanism includes pre-screening, distance scoring, power consumption scoring, load balancing scoring, and time efficiency scoring.

[0012] The execution status of the AGV is monitored in real time. When abnormal states such as AGV being occupied, path being blocked, abnormal power, execution timeout or communication failure are detected, the task context is saved and the AGV is reselected to continue the task.

[0013] A scheduling instruction is generated, securely encapsulated, and sent to the target AGV. The target AGV performs multi-level security checks on the received scheduling instruction before executing the task.

[0014] Preferably, the process of converting multi-source order information into a standard order format and generating order priorities based on time urgency, order source weight, material importance, and historical delivery evaluation to create delivery tasks specifically includes:

[0015] Convert order information from different sources into a standard order format that includes a unique order identifier, order source type, order creation timestamp, delivery point coordinates, material type, weight, volume, and expected delivery time window;

[0016] A time urgency score is calculated based on the difference between the expected delivery time and the current time.

[0017] Different basic weights are assigned based on the order source type, with inventory-triggered orders having a higher weight than terminal system orders, and terminal system orders having a higher weight than mobile client orders.

[0018] The material importance score is calculated based on the importance coefficient corresponding to the material type;

[0019] Obtain users' historical delivery satisfaction ratings and calculate the historical rating influencing factors;

[0020] Based on the time urgency score, order source weight, material importance score, and historical evaluation influence factor, a comprehensive priority score is calculated;

[0021] Based on the comprehensive priority score, a delivery task is generated that includes a unique task identifier, associated order identifier, task priority, coordinates of delivery start and end points, material requirement parameters, and task deadline, and the delivery task is placed in the task queue.

[0022] Preferably, the feasibility analysis of the delivery task based on physical distance constraints, time window constraints, resource availability constraints, and path reachability constraints, to generate an executable task, specifically includes:

[0023] Obtain the distance between the delivery point and the nearest AGV station, and compare it with the preset maximum delivery radius to determine whether the physical distance constraint is met;

[0024] Based on the current moving speed and current position of the available AGVs, calculate the estimated completion time and compare it with the task deadline to determine whether the time window constraint is met.

[0025] Check if there are any AGVs among the currently available AGVs that have the ability to handle specific material types, weights, and volumes, and determine whether the resource availability constraints are met;

[0026] Analyze whether there are obstacles or restricted areas on the path from the delivery origin to the destination, and determine whether the path accessibility constraint is met;

[0027] When all constraints are met, update the task status to "executable" and add it to the executable task queue.

[0028] When physical distance constraints or path reachability constraints are not met, a "delivery not available" notification is sent to the user.

[0029] When the time window constraint is not met, provide the user with feasible alternative time options;

[0030] When resource availability constraints are temporarily not met, the task is marked as "waiting for resources" and re-evaluated periodically.

[0031] Preferably, the step of selecting the most suitable AGV to execute the executable task through a multi-dimensional attribute matching mechanism specifically includes:

[0032] Based on the material type, weight, and volume requirements of the task, a candidate set of AGVs with execution capabilities is selected from the AGV cluster.

[0033] Calculate the distance from each candidate AGV to the task start and end points, and generate a distance score based on the reciprocal of the distance;

[0034] Assess whether the power level of each candidate AGV is sufficient to complete the entire task, and generate a power score based on the remaining power.

[0035] Count the number of tasks recently executed by each candidate AGV, and generate a load balancing score based on the task allocation balance.

[0036] Based on the AGV's moving speed and historical task completion efficiency, a time efficiency score is generated.

[0037] Based on the current system status and task characteristics, dynamically adjust the weight coefficients of distance score, power consumption score, load balancing score and time efficiency score;

[0038] The scores for each candidate AGV are weighted and summed to generate a comprehensive score;

[0039] Select the AGV with the highest overall score as the target AGV, and generate an allocation result that includes task information and execution requirements.

[0040] Preferably, when an abnormal state such as AGV being occupied, path obstruction, abnormal power, execution timeout, or communication failure is detected, the task context is saved and a new AGV is selected to continue the task, specifically including:

[0041] Receive AGV status feedback information in real time, and identify an abnormal state when the AGV status parameters exceed the preset threshold;

[0042] Based on the type of anomaly, the abnormal status is classified as AGV occupied, path blocked, power abnormality, execution timeout, or communication abnormality.

[0043] Save the current task's execution progress, AGV position coordinates, list of executed subtasks, and list of remaining subtasks to be executed, as well as other task context information.

[0044] Send a task interrupt command to the original AGV and confirm the interrupt status;

[0045] Assess the current status of the task to determine whether to continue from the current position or restart.

[0046] The multi-dimensional attribute matching mechanism is reactivated to select a new suitable AGV.

[0047] Pass the saved task context information to the newly selected AGV;

[0048] Adjust the execution plan for the remaining tasks based on the characteristics of the new AGV;

[0049] Send a command to the newly selected AGV to continue the task from the breakpoint.

[0050] Preferably, the abnormal status is classified according to the type of abnormality into AGV being occupied, path being blocked, power being abnormal, execution timeout, or communication abnormality, specifically including:

[0051] When a signal is received that the AGV is temporarily occupied by other systems or manual operation, it is determined that the AGV is in an occupied state.

[0052] When the AGV detects an obstacle in its forward path and cannot detour around it, it is determined to be in a path-blocked state.

[0053] When the AGV's battery level drops abnormally or falls below the safety threshold, it is determined to be in an abnormal battery state.

[0054] When the AGV takes more than 20% longer than the expected time to execute a task, it is judged as an execution timeout state;

[0055] If no status feedback information is received from the AGV for three consecutive times or the communication quality is lower than the preset threshold, it is determined to be a communication abnormality.

[0056] Preferably, the scheduling instruction is securely encapsulated and sent to the target AGV. The target AGV performs multi-level security checks on the received scheduling instruction before executing the task, specifically including:

[0057] Generate standardized scheduling instructions that include a unique instruction identifier, instruction type code, target AGV identifier, instruction parameter set, instruction priority, sending timestamp, expected response time, and sender identity identifier;

[0058] Data verification, calculation, and digital signature generation are performed on scheduling instructions to ensure the integrity and authenticity of the instructions;

[0059] The scheduling instructions are sent to the target AGV through a secure communication channel;

[0060] After receiving the scheduling instruction, the target AGV first checks the integrity of the instruction data to ensure that there are no transmission errors.

[0061] Verify whether the sender of the instruction is an authorized scheduling system;

[0062] Check the timestamps of commands to prevent replay attacks;

[0063] Analyze whether the format of the command conforms to the predetermined specifications;

[0064] Check whether the instructions are within the capabilities of the AGV;

[0065] Assess whether there are any security risks associated with executing this instruction;

[0066] Check if the command is consistent with the current state of the AGV;

[0067] Based on the verification results, execute the response strategy of accepting, warning, or rejecting the instruction.

[0068] Preferably, the basic status information in the AGV status information includes the AGV's unique identifier, current position coordinates, and motion status; the functional parameter information includes a list of supported material types, maximum load capacity, and maximum load capacity; the resource status information includes power level, running time, and maintenance cycle status; and the historical performance information includes historical task completion rate, average delivery time, and abnormality rate.

[0069] Preferably, the step of dynamically adjusting the weighting coefficients of distance score, power consumption score, load balancing score, and time efficiency score based on the current system state and task characteristics specifically includes:

[0070] During system idle periods, increase the weight of load balancing score to promote balanced use of AGV resources;

[0071] During peak system periods, increase the weight of time efficiency scores and prioritize AGVs with high execution efficiency;

[0072] For urgent tasks, increase the weight of distance scoring and prioritize AGVs that are closer in distance;

[0073] For long-distance tasks, increase the weight of the battery rating to ensure that the AGV has enough power to complete the task;

[0074] Based on historical task execution data, the basic weight configuration of each scoring dimension is updated regularly.

[0075] It supports setting up multiple weight configuration schemes according to different business scenarios, enabling quick switching.

[0076] AGV cluster scheduling system, including:

[0077] The order management module is used to acquire multi-source order information, convert the multi-source order information into a standard order format, generate order priorities based on time urgency, order source weight, material importance and historical delivery evaluation, and create delivery tasks.

[0078] The feasibility analysis module is used to obtain delivery constraints and perform feasibility analysis on the delivery task based on physical distance constraints, time window constraints, resource availability constraints, and path reachability constraints, thereby generating an executable task.

[0079] The AGV scheduling module is used to obtain AGV status information, and based on the executable task and the AGV status information, select the most suitable AGV to execute the executable task through a multi-dimensional attribute matching mechanism.

[0080] The status monitoring module is used to monitor the execution status of the AGV in real time. When an abnormal status is detected, the task context is saved and the AGV scheduling module is triggered to reselect the AGV to continue executing the task.

[0081] The instruction management module is used to generate scheduling instructions, securely encapsulate the scheduling instructions, and send them to the target AGV.

[0082] The communication interface module is used to exchange data with the AGV, receive AGV status feedback, and forward it to the status monitoring module.

[0083] The status storage module is used to store order information, task information, AGV status information, and system configuration information.

[0084] The AGV cluster scheduling system achieves intelligent scheduling of the AGV cluster by executing the AGV cluster scheduling method.

[0085] The beneficial effects of this invention include:

[0086] 1. Through the intelligent fusion mechanism of multi-source orders, unified processing of orders from different channels is achieved, improving the system's business adaptability and enabling flexible allocation of priorities based on order characteristics;

[0087] 2. By conducting intelligent feasibility analysis based on delivery range, infeasible tasks can be filtered out in advance to avoid waste of resources, while providing users with more accurate delivery information;

[0088] 3. Through a multi-dimensional attribute matching AGV selection mechanism, which comprehensively considers factors such as distance, power consumption, load balancing, and efficiency, optimal resource allocation is achieved, reducing the average task completion time by 35% and the AGV empty-running rate by approximately 40%.

[0089] 4. Through a dynamic task reassignment mechanism, it effectively addresses abnormal situations such as AGV occupancy and path obstruction, increasing the task execution success rate to 99.5% and reducing the average fault recovery time by 65%;

[0090] 5. Through a multi-level security verification mechanism, the safe and reliable execution of scheduling instructions is ensured, effectively preventing misoperation and malicious attacks.

[0091] Overall, this invention constructs a complete intelligent delivery scheduling solution, which significantly improves the scheduling efficiency, system stability, and resource utilization of AGV clusters. Attached Figure Description

[0092] Figure 1 This is a diagram showing the overall architecture of the AGV cluster scheduling system of the present invention.

[0093] Figure 2 This is a flowchart of the multi-source order intelligent fusion and task creation process of the present invention;

[0094] Figure 3 This is a flowchart of the intelligent feasibility analysis process based on delivery range for this invention;

[0095] Figure 4 This is a flowchart of the AGV selection process based on multi-dimensional attribute matching in this invention.

[0096] Figure 5 This is a flowchart of the AGV task redistribution process under dynamic conditions according to the present invention.

[0097] Figure 6 This is a flowchart of the scheduling instruction security verification and intelligent execution process of the present invention. Detailed Implementation

[0098] Please refer to the attached document. Figure 1-6 The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0099] Reference Figure 2-6 This embodiment provides an AGV cluster scheduling method, including steps such as intelligent fusion of multi-source orders and task creation, intelligent feasibility analysis based on delivery range, intelligent AGV selection based on multi-dimensional attribute matching, AGV task reallocation in dynamic environments, and security verification and intelligent execution of scheduling instructions.

[0100] First, this invention acquires multi-source order information, including terminal system orders, mobile client orders, inventory-triggered orders, and feedback-triggered orders. In practical applications, terminal system orders are typically initiated directly by warehouse management terminals for internal material distribution; mobile client orders are initiated by users through mobile applications for external delivery services; inventory-triggered orders are automatically generated by the warehousing system based on inventory levels for replenishment needs; and feedback-triggered orders are generated based on user delivery feedback and are typically used to handle special service requests. For example, a manufacturing plant may simultaneously have terminal system orders requesting raw materials from the production line, mobile client orders from employees requesting office supplies via a mobile app, inventory-triggered orders automatically generated by the automated warehousing system when a component is found to be in short supply, and feedback-triggered orders for returned items based on user complaints.

[0101] Next, this invention converts the multi-source order information into a standard order format and generates order priorities based on time urgency, order source weight, material importance, and historical delivery evaluation, thus creating delivery tasks. For example... Figure 2 As shown, the process first converts heterogeneous orders from different sources into a standard order format that includes a unique order identifier, order source type, order creation timestamp, delivery point coordinates, material type, weight, volume, and expected delivery time window.

[0102] Preferably, the order priority is calculated using a multi-factor weighted method, with the specific formula as follows:

[0103] ,

[0104] in: This is the overall priority score for the order, with a score range of 0-100. The higher the value, the higher the priority. The time urgency score is calculated by the difference between the expected delivery time and the current time, using the following formula: ,in For the expected delivery time, For the current time, This is the maximum time difference allowed by the system, and is usually set to 24 hours. The order source weights are as follows: M represents the material importance score, calculated based on the importance coefficient of the material type. For example, critical production materials receive 100 points, ordinary materials receive 60 points, and low-priority materials receive 30 points. H represents the historical evaluation impact factor, calculated based on users' historical delivery satisfaction ratings. The calculation formula is... ,in The average rating from the user's last 10 deliveries, ranging from 1 to 5 stars; , , , These are the weight coefficients of each factor, and they satisfy... .

[0105] In this embodiment, the weighting coefficient can be configured according to actual business needs. For example, in an AGV delivery system of a factory, it is usually set to... , , , The reason for this configuration is that time is often the most critical factor in a factory environment, followed by the importance of the order source, then the importance of materials, and historical evaluation has the least impact. Based on the calculated comprehensive priority score, the system generates a delivery task that includes a unique task identifier, associated order identifier, task priority, coordinates of the delivery start and end points, material requirement parameters, and task deadline, and places it in the task queue.

[0106] Subsequently, the present invention obtains delivery constraints, and based on physical distance constraints, time window constraints, resource availability constraints, and path reachability constraints, performs a feasibility analysis on the delivery task to generate an executable task. For example... Figure 3 As shown, the system performs a multi-dimensional feasibility analysis on each task in the task queue.

[0107] During the physical distance constraint analysis, the system obtains the distance between the delivery point and the nearest AGV station and compares it with the preset maximum delivery radius. In practical applications, the maximum delivery radius is usually determined based on the AGV's endurance and service range. For example, it might be set to 500 meters in a factory environment, 1000 meters in a large logistics center, and only 100 meters in a small warehouse. For instance, if a delivery point is located in a corner of a factory, 600 meters from the nearest AGV station, and the system's maximum delivery radius is set to 500 meters, then the task does not meet the physical distance constraint, and the system will send a "out of service range" notification to the user.

[0108] In the time window constraint analysis, the system calculates the estimated completion time based on the current moving speed and current position of the available AGVs. The formula for calculating the estimated completion time is:

[0109] ,

[0110] in: The estimated completion time indicates the point in time when the AGV is expected to complete the entire task; This is the current time, i.e., the point in time when the system performs the calculation; The total travel distance (unit: meters) includes the distance from the current position of the AGV to the starting point and the distance from the starting point to the destination, and is usually calculated using Euclidean distance or Manhattan distance. The average moving speed of the AGV (unit: meters per second) is usually 1.2-2.0 meters per second, which varies depending on the AGV model and working environment. For example, it can reach 2.0 meters per second on a flat factory ground, but may drop to 1.2 meters per second in a complex environment. Loading time (in seconds) varies from approximately 30 to 120 seconds depending on the type of material. For example, small items take about 30 seconds, while large items or items requiring precise positioning may take up to 120 seconds. The unloading time (in seconds) is approximately 30-90 seconds, usually slightly shorter than the loading time. The system compares the estimated completion time with the task deadline to determine if the time window constraint is met. For example, if an AGV is currently located in area A of the factory and needs to pick up goods in area B and deliver them to area C, with a total distance of 300 meters, an AGV speed of 1.5 meters per second, a loading time of 60 seconds, and an unloading time of 45 seconds, then the estimated completion time is the current time plus 305 seconds. If this time is later than the task deadline, the time window constraint is not met.

[0111] In resource availability constraint analysis, the system checks whether there are any available AGVs capable of handling specific material types, weights, and volumes. For example, for materials weighing over 200 kg, the system needs to confirm the availability of heavy-duty AGVs; for special materials such as fragile items, the system needs to confirm the availability of AGVs equipped with shock-absorbing devices; for liquid materials, the system needs to confirm the availability of AGVs with sealing and leak-proof functions. In a hospital logistics scenario, if medicine needs to be delivered, the system needs to confirm the availability of AGVs with temperature control functions, and that the number of such AGVs is not less than the task requirement.

[0112] In path reachability constraint analysis, the system analyzes whether there are obstacles or restricted areas on the path from the delivery origin to the destination. Path analysis uses the A* algorithm or Dijkstra's algorithm, combined with environmental map data for planning. For example, in a factory, if the direct path from area A to area C is temporarily closed due to equipment maintenance, the system will attempt to find an alternative path. If no valid alternative path can be found or the alternative path is too long (more than 150% of the direct path), then the path reachability constraint is determined not to be satisfied.

[0113] When all constraints are met, the system updates the task status to "executable" and adds it to the executable task queue; when physical distance constraints or path reachability constraints are not met, the system sends a "not deliverable" notification to the user and explains the specific reasons; when time window constraints are not met, the system provides the user with feasible alternative time options, such as postponing the estimated delivery time by 30 minutes or 1 hour; when resource availability constraints are temporarily not met, the system marks the task as "waiting for resources" and re-evaluates it every 30 seconds until resources become available or the waiting time exceeds a preset threshold (such as 20 minutes) before canceling the task.

[0114] Next, this invention acquires AGV status information, which includes basic status information, functional parameter information, resource status information, and historical performance information. AGV status information is a crucial basis for AGV selection, and the system obtains this information through real-time collection and processing.

[0115] Basic status information includes the AGV's unique identifier, current position coordinates, and motion status. The AGV's unique identifier is usually a combination of letters and numbers, such as "AGV001" or "AGV002". The current position coordinates are represented in two-dimensional or three-dimensional coordinates, such as (x,y) format in a factory flat environment, and (x,y,z) format in a multi-level warehouse. The coordinate unit is usually meters. The motion status includes stationary, accelerating, constant speed, decelerating, and turning. The system determines the AGV's motion status by observing changes in its speed and direction.

[0116] The functional parameter information includes a list of supported material types, maximum load capacity, and maximum load volume. The material type list is the set of material types that the AGV can handle, such as "general goods, fragile items, liquids, and hazardous materials"; the maximum load capacity is usually the AGV's rated load, such as "100kg", "300kg", "500kg", etc., and different AGV models have different load capacities; the maximum load volume is the maximum space that the AGV can load, such as "0.5m³", "1m³", "2m³", etc., depending on the size of the AGV loading platform.

[0117] Resource status information includes power level, running time, and maintenance cycle status. Power level is usually expressed as a percentage, such as "85%" indicating that 85% of the power is still available; running time is the continuous working time of the AGV in the current shift, in hours, used to assess the fatigue of the AGV; maintenance cycle status indicates the time until the AGV's next routine maintenance, used to assess the reliability of the AGV, usually expressed as remaining hours or remaining days.

[0118] Historical performance information includes historical task completion rate, average delivery time, and anomaly rate. The historical task completion rate is the percentage of AGVs that successfully complete tasks; for example, "98%" indicates that the AGV has a very high task success rate. The average delivery time is the average time it takes for the AGV to complete a task, used to evaluate the AGV's efficiency. The anomaly rate is the frequency of anomalies that occur during the AGV's task execution; for example, "2%" indicates that the AGV rarely experiences anomalies.

[0119] Based on the executable task and the AGV status information, this invention selects the most suitable AGV to execute the executable task through a multi-dimensional attribute matching mechanism. This multi-dimensional attribute matching mechanism includes pre-screening, distance scoring, power consumption scoring, load balancing scoring, and time efficiency scoring. Figure 4 As shown, the AGV selection process consists of multiple steps.

[0120] First, the system filters out a candidate set of AGVs capable of performing the task from the AGV cluster based on the material type, weight, and volume requirements. For example, for materials weighing 350 kg, the system will select AGVs with a maximum load capacity of at least 350 kg; for materials with a volume of 1.5 cubic meters, the system will select AGVs with a maximum load volume of at least 1.5 cubic meters; and for fragile materials, the system will select AGVs equipped with shockproof features. In a factory, if there is a task to transport precision instruments weighing 120 kg and with a volume of 0.8 cubic meters, the system will select 5 AGVs from 10 AGVs that are shockproof, have a load capacity exceeding 120 kg, and a load volume exceeding 0.8 cubic meters as a candidate set.

[0121] The system then calculates the distance from each candidate AGV to the task's starting and ending points, and generates a distance score based on the reciprocal of each distance. The distance score calculation formula is as follows:

[0122] ,

[0123] in: The distance score ranges from 0 to 1, with a higher value indicating a better AGV position. The distance (in meters) from the current position of the AGV to the starting point of the task can be calculated using the actual path distance or Euclidean distance. The distance from the start point to the end point of the task (unit: meters); and These are the weighting coefficients for the starting distance and the ending distance, respectively. , This setting is because the distance from the AGV to the starting point has a greater impact on the task start time, while the distance from the starting point to the end point has a greater impact on the total task time. Considering both together allows for a more accurate assessment of the AGV's positional advantage. For example, if AGV1 is currently 200 meters from the task starting point and the distance from the starting point to the end point is 300 meters; and AGV2 is 100 meters from the starting point and the distance from the starting point to the end point is 300 meters; then AGV1's distance score is 0.6 × (1 / 200) + 0.4 × (1 / 300) / (0.6 + 0.4) = 0.0043, and AGV2's distance score is... AGV2 has a higher distance score and a better position.

[0124] Next, the system evaluates whether the battery level of each candidate AGV is sufficient to complete the entire task and generates a battery score based on the remaining battery power. The battery score calculation formula is as follows:

[0125] ,

[0126] in: The battery level is rated on a scale of 0-1, with a higher value indicating better battery condition. This displays the current battery percentage of the AGV, ranging from 0-100%. The base battery percentage required to complete the task, via Calculation, where Total distance traveled (unit: meters) The maximum travel distance of the AGV when fully charged (unit: meters); To ensure a safety margin of 20% of the battery capacity, the AGV is typically set to ensure that it still has enough power to cope with complex road conditions and return to the charging station after the task is completed. It is fully charged, i.e., 100%.

[0127] When the required power plus the safety margin is less than the full power, the score is based on the ratio of the remaining available power to the total available power; when the required power plus the safety margin exceeds the full power, the score is based on the ratio of the excess to the safety margin, ensuring that the formula can reasonably assess the AGV's power status under all circumstances.

[0128] For example, if an AGV's current battery level is 85%, the total task distance is 10 kilometers, and the AGV can travel 20 kilometers on a full charge, then the basic battery level required to complete the task is (10 / 20)×100%=50%. Adding a 20% safety margin, the battery level is 70%. Since 85%>(50%+20%) and (50%+20%)<100%, the AGV's battery level score is (85%-(50%+20%)) / (100%-(50%+20%))=0.5, indicating that the battery level is moderate.

[0129] Subsequently, the system counts the number of tasks recently executed by each candidate AGV and generates a load balancing score based on the task allocation balance. The load balancing score calculation formula is:

[0130] ,

[0131] in: The load balancing score ranges from 0 to 1, with a higher value indicating a more balanced AGV load. The number of tasks performed by the AGV recently (e.g., in the past 4 hours); This represents the maximum number of tasks performed by a single AGV within the same period. For example, in a factory with 10 AGVs, the number of tasks performed in the past 4 hours were 5, 8, 3, 6, 7, 4, 9, 2, 6, and 5 respectively. The load balancing score of AGV8 is The load balancing score of AGV7 is This indicates that AGV8 has a lower task load and is more suitable for assigning new tasks.

[0132] Finally, the system generates a time efficiency score based on the AGV's moving speed and historical task completion efficiency. The formula for calculating the time efficiency score is:

[0133] ,

[0134] in: The time efficiency is rated, ranging from 0 to 1, with a higher value indicating higher AGV efficiency. The moving speed of the AGV (unit: meters per second); The maximum moving speed of the AGVs in the cluster (unit: meters per second); The historical task completion efficiency of AGVs is usually expressed as the ratio of expected completion time to actual completion time. For example, 0.95 means that the actual time took slightly longer than expected. The highest historical task completion efficiency of the AGVs in the cluster; and These are the weighting coefficients for speed and efficiency, respectively. This indicates that both are equally important. For example, if an AGV moves at a speed of 1.8 m / s, while the fastest AGV in the cluster moves at 2.0 m / s, and the AGV's historical task completion efficiency is 0.92, while the highest efficiency in the cluster is 0.98, then the AGV's time efficiency score is 0.98. .

[0135] Based on the current system status and task characteristics, the system dynamically adjusts the weighting coefficients of each scoring dimension. During system idle periods (e.g., when the number of tasks in the task queue is less than 20% of the number of AGVs), the system increases the weight of the load balancing score (e.g., from 0.2 to 0.4) to promote balanced use of AGV resources. During system busy periods (e.g., when the number of tasks in the task queue exceeds 80% of the number of AGVs), the system increases the weight of the time efficiency score (e.g., from 0.2 to 0.4) to prioritize AGVs with high execution efficiency. For urgent tasks (e.g., tasks with a priority in the top 10%), the system increases the weight of the distance score (e.g., from 0.3 to 0.5) to prioritize AGVs with shorter distances. For long-distance tasks (e.g., delivery distance exceeding 500 meters), the system increases the weight of the battery power score (e.g., from 0.2 to 0.4) to ensure that AGVs have sufficient battery power to complete the task.

[0136] The formula for calculating the overall score is:

[0137] ,

[0138] in The score is based on a comprehensive evaluation, ranging from 0 to 1. A higher value indicates that the AGV is more suitable for performing the task. These are the weighting coefficients for distance score, power consumption score, load balancing score, and time efficiency score, respectively, and they satisfy the following conditions: In standard configurations, these weighting coefficients are typically set to... , , , This indicates that distance and efficiency are the primary considerations, while power consumption and load balancing are secondary.

[0139] The system selects the AGV with the highest overall score as the target AGV and generates an allocation result containing task information and execution requirements. If multiple AGVs have the same overall score (the difference in score is less than 0.01), the system will select the AGV with the smaller number to ensure the certainty of the result. For example, in a factory environment, for a task of transporting raw materials, the system may calculate that AGV2 has an overall score of 0.78, AGV5 has an overall score of 0.65, and AGV8 has an overall score of 0.71. The system will select AGV2 with the highest score to execute the task.

[0140] During task execution, this invention monitors the AGV's execution status in real time. When abnormal states such as AGV being occupied, path obstruction, abnormal power, execution timeout, or communication failure are detected, the task context is saved and a new AGV is selected to continue task execution. Figure 5 As shown, the system is designed with a comprehensive exception handling mechanism.

[0141] The system receives AGV status feedback information in real time. When AGV status parameters exceed preset thresholds, they are identified as abnormal states. For example, if the AGV position does not update for more than 10 seconds, it may indicate a communication failure; if the AGV's battery level drops by more than 150% of the normal rate, it may indicate an abnormal battery level; if the AGV remains in the same position for more than 30 seconds without being in a loading or unloading state, it may indicate a blocked path. These thresholds are set based on actual operating experience and can be adjusted according to different environments and AGV models.

[0142] Based on the type of anomaly, the system categorizes abnormal states into different situations. When a signal is received that the AGV is temporarily occupied by another system or manual operation, the system determines that the AGV is occupied. This situation is common in environments where multiple systems share AGV resources, such as when an AGV is temporarily called by an emergency maintenance system. When the AGV detects an obstacle in its forward path and cannot detour, the system determines that the path is blocked. For example, in a factory, a passage may be blocked due to temporary material storage. When the AGV's battery level drops abnormally (e.g., it drops by more than 8% per hour when it is normally 5%) or falls below the safety threshold (usually 30%), the system determines that the battery level is abnormal. This may be due to aging AGV batteries or excessive load. When the AGV takes more than 20% longer to complete a task than expected, the system determines that the task has timed out. For example, a task originally expected to be completed in 30 minutes has been completed in 36 minutes. When no status feedback information is received from the AGV for three consecutive times or the communication quality (e.g., signal strength, data integrity) is below a preset threshold, the system determines that the communication is abnormal. This may be due to network problems or AGV communication module failure.

[0143] Upon detecting an anomaly, the system saves task context information such as the current task's execution progress, AGV position coordinates, a list of executed subtasks, and a list of remaining unexecuted subtasks. This information is encapsulated into a "task context" data packet, containing sufficient information for a new AGV to continue the task from its breakpoint. For example, in a pickup and delivery task, if an AGV has completed pickup but encounters an anomaly during delivery, the system saves information such as the goods picked up, its current location, and destination, allowing a newly assigned AGV to continue delivery directly from its current location.

[0144] Then, the system sends a task interruption command to the original AGV to confirm the interruption status. The task interruption command includes information such as the task ID, interruption reason, and interruption time. The system waits for the AGV to return an interruption confirmation. If no confirmation is received within 10 seconds, the system will resend the command, up to a maximum of 3 times. This design ensures that the AGV has indeed received the interruption command and avoids the situation where the same task is executed simultaneously by multiple AGVs.

[0145] The system then assesses the current status of the task to determine whether to continue execution from the current position or restart. For tasks that are partially completed (such as those that have been loaded but have not yet started transport), the system typically chooses to continue from the current position; for tasks that have just begun (such as those that have not yet reached the starting point), the system may choose to restart. This decision is based on a comprehensive consideration of factors such as task type, completion progress, and the position of the new AGV.

[0146] Next, the system re-invokes the multi-dimensional attribute matching mechanism to select a new suitable AGV. To avoid selecting AGVs that may also have the same problems, the system will appropriately adjust the selection criteria. For example, for path obstruction situations, the system will increase the score of AGVs with strong path planning capabilities; for abnormal battery levels, the system will increase the score of AGVs with good battery management records. For instance, in a factory, if AGV3 is interrupted while performing its task due to abnormal battery levels, the system will give special consideration to AGVs with good battery conditions, such as AGV6 and AGV9, when reselecting, and ultimately select the one with the higher overall score.

[0147] The system passes the saved task context information to the newly selected AGV, adjusts the execution plan of the remaining tasks based on the characteristics of the new AGV, and sends an instruction to the newly selected AGV to continue executing the task from the breakpoint. For example, if the original AGV stopped at a corner, the system will use that position as the starting point for the new AGV's task and plan the optimal path from that point to the destination.

[0148] Finally, this invention generates scheduling instructions, securely encapsulates these instructions, and sends them to the target AGV. The target AGV performs multi-level security checks on the received scheduling instructions before executing the task. Figure 6As shown, the system is designed with a comprehensive instruction security mechanism.

[0149] The system generates standardized scheduling instructions that include a unique instruction identifier, instruction type code, target AGV identifier, instruction parameter set, instruction priority, sending timestamp, expected response time, and sender identity. The instruction unique identifier is usually a system-generated UUID (Universally Unique Identifier), such as "XYZ"; the instruction type code identifies the type of instruction, such as "001" for task assignment, "002" for path adjustment, and "003" for status query; the instruction parameter set contains specific execution parameters, such as the task start coordinates (120.5, 56.8), end coordinates (230.2, 78.4), and material ID "MAT2458"; the instruction priority indicates the urgency of the instruction, usually divided into 5 levels, with level 1 being the highest and level 5 the lowest; the sending timestamp records the precise time the instruction was sent, such as "2025-05-29T10:15:30.123Z"; the expected response time specifies how long the AGV should respond to the instruction, such as "5 seconds"; the sender identity identifier is used to verify the legitimacy of the instruction sender, such as "SYS_SCHEDULER".

[0150] The system performs data verification and calculation, and generates digital signatures for scheduling instructions to ensure the integrity and authenticity of the instructions. The data verification uses CRC32 or MD5 algorithms, such as "EFG"; the digital signature uses RSA or ECDSA algorithms, and the system's private key is used to sign the instruction digest, generating a signature string such as "ABC".

[0151] Scheduling instructions are sent to the target AGV via a secure communication channel (such as a TLS encrypted channel). During communication, the system monitors the data transmission status, and if a transmission anomaly is detected (such as a packet loss rate exceeding 1%), the instructions are resent. In industrial environments, communication quality may be affected by electromagnetic interference or network congestion, making this monitoring mechanism essential.

[0152] After receiving the scheduling instruction, the target AGV first checks the integrity of the instruction data to ensure there are no transmission errors. The AGV recalculates the checksum of the received data and compares it with the checksum included in the instruction. If they do not match, a transmission error is determined, and a retransmission is requested. For example, if an AGV receives an instruction and calculates a checksum of "EFG", which is the same as the checksum included in the instruction, then the integrity check passes.

[0153] Next, the AGV verifies whether the sender of the instruction is an authorized scheduling system. The AGV uses a pre-stored public key from the scheduling system to verify the digital signature, confirming that the instruction indeed comes from a legitimate scheduling system and preventing forged instruction attacks. In a factory environment, this verification mechanism can prevent unauthorized systems (such as hacked terminals) from sending malicious instructions to the AGV.

[0154] Next, the AGV checks the timestamp of the instruction to prevent replay attacks. If the difference between the instruction's timestamp and the current time exceeds a preset threshold (usually 30 seconds), the AGV will refuse to execute the instruction to prevent attackers from intercepting and replaying old instructions. For example, if the AGV receives an instruction with a timestamp of "2025-05-29T10:15:30.123Z" while the current time is "2025-05-29T10:20:45.789Z", the difference exceeds 30 seconds, and the AGV will reject the instruction.

[0155] Subsequently, the AGV analyzes whether the format of the instruction conforms to the predetermined specifications, checks whether the content of the instruction is within the AGV's capabilities, assesses whether there are any safety risks in executing the instruction, and checks whether the instruction is consistent with the AGV's current state. For example, if the instruction requires the AGV to move 500 kg of material, but the AGV's maximum load capacity is 300 kg, the AGV will refuse the instruction; if the instruction requires the AGV to go to a restricted area, the AGV will also refuse to execute it.

[0156] Finally, based on the verification results, the AGV executes a response strategy of accepting, issuing a warning, or rejecting the instruction. If all verifications pass, the AGV accepts the instruction and begins execution, while simultaneously sending an acceptance confirmation to the scheduling system; if non-critical verifications (such as a slight timestamp timeout of 5 seconds) fail, the AGV sends a warning message but still executes the instruction; if critical verifications (such as authentication) fail, the AGV rejects the instruction and returns a detailed reason for the failure, such as "signature verification failed, instruction may have been tampered with."

[0157] Reference Figure 1 This embodiment provides an AGV cluster scheduling system, including an order management module 1, a feasibility analysis module 2, an AGV scheduling module 3, a status monitoring module 4, an instruction management module 5, a communication interface module 6, and a status storage module 7.

[0158] The order management module 1 is used to acquire multi-source order information, convert the multi-source order information into a standard order format, and generate order priorities based on time urgency, order source weight, material importance, and historical delivery evaluation, and create delivery tasks. The order management module 1 includes an order acquisition unit 11, an order standardization unit 12, a priority calculation unit 13, and a task creation unit 14.

[0159] Order acquisition unit 11 retrieves raw order data from the terminal system, mobile client, warehousing system, and user feedback system via API interfaces or message queues. The order data from different sources has different formats and requires unified processing. For example, the terminal system may use XML format, the mobile client may use JSON format, the warehousing system may use CSV format, and the user feedback system may use a custom binary format.

[0160] The order standardization unit 12 converts the acquired raw order data into a system standard format. For example, it converts different date and time formats to ISO8601 format (e.g., "2025-05-29T10:15:30Z"), converts different weight units to kilograms, and converts different volume units to cubic meters. The priority calculation unit 13 calculates the priority score for each order according to the priority calculation formula described in Example 1. The task creation unit 14 creates delivery tasks based on the standardized orders and priority scores and places them in the task queue.

[0161] The feasibility analysis module 2 is used to obtain delivery constraints. Based on physical distance constraints, time window constraints, resource availability constraints, and path reachability constraints, it performs a feasibility analysis on the delivery task and generates an executable task. The feasibility analysis module 2 includes a constraint acquisition unit 21, a multi-dimensional constraint analysis unit 22, and a task status update unit 23.

[0162] The constraint acquisition unit 21 retrieves various constraint parameters from system configuration and environmental data, such as maximum delivery radius and average AGV speed. In a factory application, these parameters might include a maximum delivery radius of 500 meters, an average AGV speed of 1.5 meters per second, and a maximum waiting time of 20 minutes. The multidimensional constraint analysis unit 22 performs constraint analysis across four dimensions: physical distance, time window, resource availability, and path reachability. The task status update unit 23 updates the task status based on the analysis results and adds executable tasks to the execution queue.

[0163] The AGV scheduling module 3 is used to acquire AGV status information and, based on the executable task and the AGV status information, selects the most suitable AGV to execute the executable task through a multi-dimensional attribute matching mechanism. The AGV scheduling module 3 includes a status acquisition unit 31, a candidate AGV filtering unit 32, a multi-dimensional scoring unit 33, and an AGV selection unit 34.

[0164] The status acquisition unit 31 acquires the real-time status information of the AGVs through the communication interface module 6. For example, it sends a status query request to all AGVs every 2 seconds and receives responses containing information such as location, battery level, and movement status. The candidate AGV screening unit 32 filters a set of candidate AGVs with execution capabilities according to task requirements. The multi-dimensional scoring unit 33 calculates the distance score, battery level score, load balancing score, and time efficiency score for each candidate AGV and dynamically adjusts the weights according to the system status. The AGV selection unit 34 selects the optimal AGV based on the comprehensive score and generates the allocation result.

[0165] The status monitoring module 4 is used to monitor the execution status of the AGV in real time. When an abnormal status is detected, the task context is saved and the AGV scheduling module 3 is triggered to reselect the AGV to continue executing the task. The status monitoring module 4 includes a status monitoring unit 41, an anomaly identification unit 42, a context saving unit 43, and a reallocation triggering unit 44.

[0166] The status monitoring unit 41 periodically (typically every 1 second) acquires status feedback information from the AGVs. In a factory implementation, this unit might be configured to collect approximately 20KB of status data per second from 20 AGVs, including position, speed, and battery level. The anomaly identification unit 42 identifies abnormal states based on preset thresholds and rules. The context saving unit 43 saves the task context information when an anomaly is detected. The reassignment triggering unit 44 sends a reassignment request to the AGV scheduling module 3, triggering a reselection process.

[0167] The instruction management module 5 is used to generate scheduling instructions, securely encapsulate the scheduling instructions, and send them to the target AGV. The instruction management module 5 includes an instruction generation unit 51, a secure encapsulation unit 52, and an instruction sending unit 53.

[0168] The instruction generation unit 51 generates standardized scheduling instructions based on task allocation results or system control requirements. For example, for task allocation instructions, it generates an instruction data structure containing parameters such as task ID, start and end coordinates, and material information. The security encapsulation unit 52 performs data verification, calculation, and digital signature on the instructions to ensure their security. The instruction sending unit 53 securely transmits the instructions to the target AGV through the communication interface module 6.

[0169] The communication interface module 6 is used to exchange data with the AGV, receive AGV status feedback, and forward it to the status monitoring module 4. The communication interface module 6 includes a data sending unit 61, a data receiving unit 62, and a protocol conversion unit 63.

[0170] The data sending unit 61 is responsible for sending system instructions to the AGV via the network. For example, it uses the TCP / IP protocol to send instruction data packets and ensures successful data transmission. The data receiving unit 62 receives status feedback and response information from the AGV. The protocol conversion unit 63 converts between different communication protocols to ensure system compatibility with various types of AGVs. For example, some older AGV models may use the RS485 communication protocol, while newer models use Ethernet or wireless communication; the protocol conversion unit ensures that the system can communicate normally with all types of AGVs.

[0171] State storage module 7 stores order information, task information, AGV status information, and system configuration information. It employs a distributed database architecture to ensure high data availability and consistency. The main stored data includes order tables, task tables, AGV status tables, scheduling log tables, and system configuration tables. For example, in an implementation at a large logistics center, this module processes approximately 100,000 order records and 500,000 AGV status records daily, retaining 30 days of historical data for analysis and optimization.

[0172] The AGV cluster scheduling system in this embodiment achieves intelligent scheduling of the AGV cluster by executing the AGV cluster scheduling method described in Embodiment 1. The modules work collaboratively through standardized interfaces and data flows to form a complete closed-loop scheduling system.

[0173] Preferably, this system supports pre-setting multiple weight configuration schemes according to different business scenario requirements, enabling rapid switching. For example, in a logistics and delivery scenario, three schemes can be preset: "efficiency priority," "balance priority," and "energy saving priority," which are respectively suitable for scheduling needs during peak, off-peak, and low-peak periods. In the "efficiency priority" scheme, the time efficiency score weight can be set to 0.5, the distance score weight to 0.3, and the power consumption score and load balancing score weights to 0.1 each; in the "balance priority" scheme, the load balancing score weight can be increased to 0.4, and the other three items to 0.2 each; in the "energy saving priority" scheme, the power consumption score weight can be increased to 0.4, the distance score weight to 0.3, and the time efficiency and load balancing score weights to 0.15 each.

[0174] This system also boasts excellent scalability and adaptability, supporting collaborative work among different types of AGVs and meeting the needs of various application scenarios such as factories, warehouses, hospitals, and ports. For example, in a hospital environment, the system can simultaneously dispatch general delivery AGVs, pharmaceutical-specific AGVs, and medical waste disposal AGVs, selecting the most suitable AGV type based on the characteristics of different tasks.

[0175] In summary, the AGV cluster scheduling method and system provided by this invention, through innovative technologies such as intelligent fusion of multi-source orders, intelligent feasibility analysis based on delivery range, AGV selection based on multi-dimensional attribute matching, dynamic task reallocation, and security verification of scheduling instructions, constructs a complete intelligent delivery scheduling solution, which significantly improves the scheduling efficiency, system stability, and resource utilization of AGV clusters.

[0176] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An AGV cluster scheduling method, characterized in that, include: Obtain multi-source order information, including terminal system orders, mobile client orders, inventory-triggered orders, and feedback-triggered orders; The multi-source order information is converted into a standard order format, and order priorities are generated based on time urgency, order source weight, material importance, and historical delivery evaluation to create delivery tasks; Obtain delivery constraints, and perform a feasibility analysis on the delivery task based on physical distance constraints, time window constraints, resource availability constraints, and path reachability constraints to generate an executable task; Obtain AGV status information, which includes basic status information, functional parameter information, resource status information, and historical performance information; Based on the executable task and the AGV status information, the most suitable AGV is selected to execute the executable task through a multi-dimensional attribute matching mechanism. The multi-dimensional attribute matching mechanism includes pre-screening, distance scoring, power consumption scoring, load balancing scoring, and time efficiency scoring. The execution status of the AGV is monitored in real time. When an abnormal status is detected, such as AGV being occupied, path being blocked, abnormal power, execution timeout, or communication failure, the task context is saved and a new AGV is selected to continue the task. A scheduling instruction is generated, securely encapsulated, and sent to the target AGV. The target AGV performs multi-level security checks on the received scheduling instruction before executing the task.

2. The AGV cluster scheduling method according to claim 1, characterized in that, Convert multi-source order information into a standard order format, and generate order priorities based on time urgency, order source weight, material importance, and historical delivery evaluation to create delivery tasks. Specifically, this includes: Convert order information from different sources into a standard order format that includes a unique order identifier, order source type, order creation timestamp, delivery point coordinates, material type, weight, volume, and expected delivery time window; A time urgency score is calculated based on the difference between the expected delivery time and the current time. Different basic weights are assigned based on the order source type, with inventory-triggered orders having a higher weight than terminal system orders, and terminal system orders having a higher weight than mobile client orders. The material importance score is calculated based on the importance coefficient corresponding to the material type; Obtain users' historical delivery satisfaction ratings and calculate the historical rating influencing factors; Based on the time urgency score, order source weight, material importance score, and historical evaluation influence factor, a comprehensive priority score is calculated; Based on the comprehensive priority score, a delivery task is generated that includes a unique task identifier, associated order identifier, task priority, coordinates of delivery start and end points, material requirement parameters, and task deadline, and the delivery task is placed in the task queue.

3. The AGV cluster scheduling method according to claim 1, characterized in that, The feasibility analysis of the delivery task, based on physical distance constraints, time window constraints, resource availability constraints, and path reachability constraints, generates an executable task, specifically including: Obtain the distance between the delivery point and the nearest AGV station, and compare it with the preset maximum delivery radius to determine whether the physical distance constraint is met; Based on the current moving speed and current position of the available AGVs, calculate the estimated completion time and compare it with the task deadline to determine whether the time window constraint is met. Check if there are any AGVs among the currently available AGVs that have the ability to handle specific material types, weights, and volumes, and determine whether the resource availability constraints are met; Analyze whether there are obstacles or restricted areas on the path from the delivery origin to the destination, and determine whether the path accessibility constraint is met; When all constraints are met, update the task status to "executable" and add it to the executable task queue. When physical distance constraints or path reachability constraints are not met, a "delivery not available" notification is sent to the user. When the time window constraint is not met, provide the user with feasible alternative time options; When resource availability constraints are temporarily not met, the task is marked as "waiting for resources" and re-evaluated periodically.

4. The AGV cluster scheduling method according to claim 1, characterized in that, The step of selecting the most suitable AGV to execute the executable task through a multi-dimensional attribute matching mechanism specifically includes: Based on the material type, weight, and volume requirements of the task, a candidate set of AGVs with execution capabilities is selected from the AGV cluster. Calculate the distance from each candidate AGV to the task start and end points, and generate a distance score based on the reciprocal of the distance; Assess whether the power level of each candidate AGV is sufficient to complete the entire task, and generate a power score based on the remaining power. Count the number of tasks recently executed by each candidate AGV, and generate a load balancing score based on the task allocation balance. Based on the AGV's moving speed and historical task completion efficiency, a time efficiency score is generated. Based on the current system status and task characteristics, dynamically adjust the weight coefficients of distance score, power consumption score, load balancing score and time efficiency score; The scores for each candidate AGV are weighted and summed to generate a comprehensive score; Select the AGV with the highest overall score as the target AGV, and generate an allocation result that includes task information and execution requirements.

5. The AGV cluster scheduling method according to claim 1, characterized in that, When an AGV is detected to be occupied, its path is blocked, its battery is abnormal, its execution times out, or its communication is abnormal, the task context is saved and a new AGV is selected to continue the task. Specifically, this includes: Receive AGV status feedback information in real time, and identify an abnormal state when the AGV status parameters exceed the preset threshold; Based on the type of anomaly, the abnormal status is classified as AGV occupied, path blocked, power abnormality, execution timeout, or communication abnormality. Save the current task's execution progress, AGV position coordinates, list of executed subtasks, list of remaining subtasks to be executed, and task context information; Send a task interrupt command to the original AGV and confirm the interrupt status; Assess the current status of the task to determine whether to continue from the current position or restart. The multi-dimensional attribute matching mechanism is reactivated to select a new suitable AGV. Pass the saved task context information to the newly selected AGV; Adjust the execution plan for the remaining tasks based on the characteristics of the new AGV; Send a command to the newly selected AGV to continue the task from the breakpoint.

6. The AGV cluster scheduling method according to claim 5, characterized in that, The abnormal status is classified according to the type of abnormality into AGV being occupied, path being blocked, power abnormality, execution timeout, or communication abnormality, specifically including: When a signal is received that the AGV is temporarily occupied by other systems or manual operation, it is determined that the AGV is in an occupied state. When the AGV detects an obstacle in its forward path and cannot detour around it, it is determined to be in a path-blocked state. When the AGV's battery level drops abnormally or falls below the safety threshold, it is determined to be in an abnormal battery state. When the AGV takes more than 20% longer than the expected time to execute a task, it is judged as an execution timeout state; If no status feedback information is received from the AGV for three consecutive times or the communication quality is lower than the preset threshold, it is determined to be a communication abnormality.

7. The AGV cluster scheduling method according to claim 1, characterized in that, The process involves securely encapsulating the scheduling instruction and sending it to the target AGV. The target AGV then performs multi-level security checks on the received scheduling instruction before executing the task. Specifically, this includes: Generate standardized scheduling instructions that include a unique instruction identifier, instruction type code, target AGV identifier, instruction parameter set, instruction priority, sending timestamp, expected response time, and sender identity identifier; Data verification, calculation, and digital signature generation are performed on scheduling instructions to ensure the integrity and authenticity of the instructions; The scheduling instructions are sent to the target AGV through a secure communication channel; After receiving the scheduling instruction, the target AGV first checks the integrity of the instruction data to ensure that there are no transmission errors. Verify whether the sender of the instruction is an authorized scheduling system; Check the timestamps of commands to prevent replay attacks; Analyze whether the format of the command conforms to the predetermined specifications; Check whether the instructions are within the capabilities of the AGV; Assess whether there are any security risks associated with executing this instruction; Check if the command is consistent with the current state of the AGV; Based on the verification results, execute the response strategy of accepting, warning, or rejecting the instruction.

8. The AGV cluster scheduling method according to claim 1, characterized in that, The basic status information in the AGV status information includes the AGV's unique identifier, current position coordinates, and motion status; the functional parameter information includes a list of supported material types, maximum load capacity, and maximum load capacity; the resource status information includes power level, running time, and maintenance cycle status; and the historical performance information includes historical task completion rate, average delivery time, and abnormality rate.

9. The AGV cluster scheduling method according to claim 4, characterized in that, The step of dynamically adjusting the weighting coefficients of distance score, power consumption score, load balancing score, and time efficiency score based on the current system status and task characteristics specifically includes: During system idle periods, increase the weight of load balancing score to promote balanced use of AGV resources; During peak system periods, increase the weight of time efficiency scores and prioritize AGVs with high execution efficiency; For urgent tasks, increase the weight of distance scoring and prioritize AGVs that are closer in distance; For long-distance tasks, increase the weight of the battery rating to ensure that the AGV has enough power to complete the task; Based on historical task execution data, the basic weight configuration of each scoring dimension is updated regularly. It supports setting up multiple weight configuration schemes according to different business scenarios, enabling quick switching.

10. An AGV cluster scheduling system, characterized in that, include: The order management module is used to acquire multi-source order information, convert the multi-source order information into a standard order format, generate order priorities based on time urgency, order source weight, material importance and historical delivery evaluation, and create delivery tasks. The feasibility analysis module is used to obtain delivery constraints and perform feasibility analysis on the delivery task based on physical distance constraints, time window constraints, resource availability constraints, and path reachability constraints, thereby generating an executable task. The AGV scheduling module is used to obtain AGV status information, and based on the executable task and the AGV status information, select the most suitable AGV to execute the executable task through a multi-dimensional attribute matching mechanism. The status monitoring module is used to monitor the execution status of the AGV in real time. When an abnormal status is detected, the task context is saved and the AGV scheduling module is triggered to reselect the AGV to continue executing the task. The instruction management module is used to generate scheduling instructions, securely encapsulate the scheduling instructions, and send them to the target AGV. The communication interface module is used to exchange data with the AGV, receive AGV status feedback, and forward it to the status monitoring module. The status storage module is used to store order information, task information, AGV status information, and system configuration information. The AGV cluster scheduling system achieves intelligent scheduling of the AGV cluster by executing the AGV cluster scheduling method according to any one of claims 1 to 9.

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