An agv flexible line body scheduling method and system suitable for a digital intelligent factory

Abstract

The application provides an AGV flexible line scheduling method and system suitable for a digital intelligent factory, the method comprising: establishing a scheduling model of a work workshop and initializing a scheduling environment according to machining machine information, AGV information and workpiece information, setting a model clock as an initial time, and then calculating an initial variable period driving time based on the average arrival time of the workpieces and the historical machining time; acquiring the machining machine state and the workpiece state in real time, updating the workshop scheduling environment and the variable period driving time when an emergency event occurs or the model clock reaches the initial period scheduling time, constructing an objective function, and solving to generate machining machine allocation results and AGV allocation results with the objective function minimization as the goal, and generating the shortest path and walking time of the AGV transportation process based on a preset algorithm. The mixed scheduling strategy combining the variable period driving and the event driving ensures that the emergency event can be quickly processed while ensuring the continuity and stability of production.

Description

Technical Field

[0001] This invention relates to the field of dynamic scheduling in workshops, and in particular to a method and system for scheduling flexible AGV production lines suitable for smart factories. Background Technology

[0002] With the continuous development of Industry 4.0 and smart factories, manufacturing systems are moving towards intelligence. The efficiency and level of logistics and transportation determine the production line's ability to respond to orders. As a key carrier for workpiece transportation, the intelligent scheduling level of automated guided vehicles (AGVs) has become the core technology for smart factories to achieve flexible line scheduling of AGVs.

[0003] In existing technologies, rigid scheduling schemes based on fixed time periods for rescheduling struggle to balance dynamism and stability. Excessively long periods prevent timely responses to changes in the workshop's real-time status, while excessively short periods increase the system's computational burden. Furthermore, dynamic scheduling schemes based on fixed path networks, in production environments with frequent dynamic events, lead to continuous plan changes, and the AGV system's frequent adaptation to new instructions affects production continuity and stability. Faced with the variable production rhythms, complex equipment layouts, and high-concurrency task requests in smart factories, traditional scheduling methods struggle to guarantee the real-time performance, stability, and resource coupling of flexible AGV line scheduling when dealing with production line layout adjustments, multi-machine collaborative operations, and handling faulty machines. Therefore, a flexible AGV line scheduling method with real-time perception and dynamic adjustment capabilities is needed. Summary of the Invention

[0004] This invention provides a method and system for scheduling flexible AGV production lines in smart factories. Its main purpose is to solve the problem that it is difficult to achieve real-time perception and dynamic adjustment in the scheduling process of flexible AGV production lines in the prior art.

[0005] To achieve the above objectives, this invention provides a flexible AGV production line scheduling method suitable for smart factories, the method comprising: Establish a scheduling model for the workshop and initialize the scheduling environment based on the processing machine information, AGV information and workpiece information. After setting the model clock to the initial time, calculate the initial variable cycle drive time based on the average arrival time of the workpiece and the historical processing time. Real-time acquisition of machine and workpiece status; updating the shop floor scheduling environment and variable cycle drive time when an emergency event occurs or the model clock reaches the initial cycle scheduling time; emergency events include workpiece insertion and machine failure. Construct an objective function and solve it with the goal of minimizing the objective function to generate processing machine allocation results and AGV allocation results. Based on a preset algorithm and the processing machine allocation results and AGV allocation results, generate the shortest path and travel time for the AGV transportation process.

[0006] Optionally, the expression for calculating the initial variable-cycle drive time based on the average arrival time of the workpiece and the historical processing time is as follows: in, Indicates the sequence number is The time it takes for the workpiece to arrive at the workshop; Indicates the total number of workpieces; This represents the total processing time for all scheduled workpieces; Indicates the number of scheduling attempts.

[0007] Optionally, the process of updating the shop floor scheduling environment and variable-cycle drive time in the event of an emergency includes: When an emergency event is a workpiece insertion, the workpiece being processed by the processing machine continues to perform the processing procedure, the workpiece being transported by the AGV continues to perform the transfer task, the inserted workpiece is added to the workpiece set to be processed and the workshop scheduling environment and variable cycle drive time are updated. When an emergency event is a machine malfunction, the system obtains the occurrence time of the malfunctioning machine and the information of the workpieces being processed on the malfunctioning machine. It then stops the processing of the workpieces on the malfunctioning machine, adds them to the workpiece set to be processed, and updates the workshop scheduling environment. If there are workpieces being sent to the malfunctioning machine on the AGV, it determines whether the start time of the workpiece being transported on the AGV is within the maintenance period. If the start time is within the maintenance period, it sorts the distances of the AGV's current position to other processing machines, sends the workpiece to the processing machine with the smallest distance, adds the workpiece to the workpiece set to be processed, and then updates the workshop scheduling environment and the variable cycle drive time.

[0008] Optionally, the process of updating the shop floor scheduling environment and the variable-cycle drive time when the model clock reaches the initial cycle scheduling time includes: Obtain the number of workpieces that have arrived at the workshop within the current cycle scheduling time and compare it with the number of workpieces that have arrived at the workshop within the previous cycle scheduling time. If the number of workpieces has not increased, maintain the current workshop scheduling environment and cycle scheduling time. If the number of workpieces increases, control the workpieces being processed and the workpieces being transported during the current cycle driving time to continue to complete the current work, update the set of workpieces to be processed, and update the workshop scheduling environment and cycle scheduling time.

[0009] Optionally, the expression for constructing the objective function is: ; in, , and Indicates the weighting coefficient; Indicates the total mileage traveled by the AGV; Indicates the number of AGVs used; Indicates the maximum completion time; Represents the process from the starting node to the task. Distance from the starting node; Indicates task Is it the serial number? The primary task of the AGV; Indicates task The distance from the starting node to the ending node; Indicates task Is it by serial number? AGV execution; Indicates from the task Termination node to task Distance from the starting node; Indicates the sequence number is Does the AGV complete its task? Execute the task after ; Indicates from the task The distance from the termination node to the parking area; Indicates task Is it the serial number? The tail task of the AGV; Indicates the sequence number is Is the AGV occupied? This indicates that the AGV has completed its task. Time; This indicates the speed at which the AGV travels.

[0010] Optionally, the constraints for constructing the objective function and solving it with the goal of minimizing the objective function include: Time constraints: The AGV's task execution time cannot be earlier than the task's set start time, and the completion time cannot be later than the task's set end time. The expression is: Task allocation constraints: Each transportation task is assigned to one AGV; the expression is: The task completion constraint stipulates that each AGV starts from the set starting node during the work process, completes the task, and returns to the set termination stage; the expression is: in, This indicates that the AGV has started executing the task. i Time; Indicates task i The earliest start time; This indicates that the AGV has completed its task. i Time; Indicates task i The latest completion time; Indicates the sequence number is The starting point for the AGV's first task is the parking area; Indicates the sequence number is The destination of the AGV's tail task is the parking area.

[0011] Optionally, the process of generating the shortest path and travel time from the AGV's starting point to its destination based on a preset algorithm and according to the allocation results of the processing machine and AGV includes: A grid map is constructed based on the layout of the workshop, and the stopping points of the AGVs are marked as the starting nodes, and the positions of the assigned processing machines are marked as the ending nodes. Based on the omnidirectional movement characteristics of AGV casters, the shortest path from the starting node to the ending node of the AGV is obtained through a jump point search algorithm. Calculate the total path length by obtaining the geometric distance between each node in the shortest path, and obtain the travel time of the AGV from the starting node to the ending node based on the preset AGV travel speed.

[0012] To address the aforementioned issues, this invention also provides an AGV flexible line scheduling system suitable for smart factories, comprising a processor and a memory. The memory stores computer instructions, and the processor executes the computer instructions stored in the memory. When the computer instructions are executed by the processor, the steps of the aforementioned method are implemented.

[0013] To address the aforementioned problems, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0014] To address the aforementioned problems, the present invention also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the above-described method.

[0015] The beneficial effects of this invention are as follows: This invention provides a flexible AGV production line scheduling method suitable for smart factories. It establishes a scheduling model for the workshop and initializes the scheduling environment based on machine information, AGV information, and workpiece information. It acquires the real-time status of the machines and workpieces, and updates the workshop scheduling environment and variable-cycle drive time when an emergency event occurs or the model clock reaches the initial cycle scheduling time. It constructs an objective function and solves it with the goal of minimizing the objective function to generate machine allocation results and AGV allocation results. Based on a preset algorithm and the machine allocation results and AGV allocation results, it generates the shortest path and travel time for AGV transportation. It adopts a hybrid scheduling strategy combining variable-cycle drive and event-driven scheduling. The event-driven scheduling method ensures rapid handling of emergency events, while periodic batch scheduling ensures production continuity and stability. Furthermore, it implements a tiered response mechanism for machine failures to avoid computational overhead and production disturbances caused by global rescheduling.

[0016] Additional advantages, objects, and features of the invention will be set forth in part in the description which follows, and will also become apparent in part to those skilled in the art upon studying the description, or may be learned by practice of the invention. The objects and other advantages of the invention can be realized and obtained by means of the structures specifically pointed out in the description and drawings.

[0017] Those skilled in the art will understand that the objectives and advantages achievable with the present invention are not limited to those specifically described above, and that the above and other objectives achievable with the present invention will become clearer from the following detailed description. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of an AGV flexible line scheduling method for smart factories according to an embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of the scheduling process according to an embodiment of the present invention.

[0020] Figure 3 This is a schematic diagram of an AGV flexible production line scheduling system suitable for smart factories, according to an embodiment of the present invention.

[0021] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. Here, the illustrative embodiments and descriptions of this invention are used to explain the invention, but are not intended to limit the invention.

[0023] It should also be noted that, in order to avoid obscuring the invention with unnecessary details, only the structures and / or processing steps closely related to the solution according to the invention are shown in the accompanying drawings, while other details that are not closely related to the invention are omitted.

[0024] It should be emphasized that the term "including / comprises" as used herein refers to the presence of a feature, element, step, or component, but does not exclude the presence or addition of one or more other features, elements, steps, or components.

[0025] It should also be noted that, unless otherwise specified, the term "connection" in this article can refer not only to a direct connection, but also to an indirect connection involving an intermediary.

[0026] In the following description, embodiments of the invention will be illustrated with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar parts, or the same or similar steps.

[0027] Figure 1 This is a schematic diagram of an AGV flexible line scheduling method suitable for smart factories according to an embodiment of the present invention; this application provides an AGV flexible line scheduling method suitable for smart factories, the method including the following steps S101~S103: Step S101: Establish the scheduling model of the workshop and initialize the scheduling environment based on the processing machine information, AGV information and workpiece information. After setting the model clock to the initial time, calculate the initial variable cycle drive time based on the average arrival time of the workpiece and the historical processing time.

[0028] Step S102: Real-time acquisition of the status of the processing machine and the workpiece, and updating the workshop scheduling environment and variable cycle drive time when an emergency event occurs or the model clock reaches the initial cycle scheduling time; emergency events include workpiece insertion, processing machine failure and dynamic changes in the number of workpieces.

[0029] Step S103: Construct an objective function and solve it with the goal of minimizing the objective function to generate processing machine allocation results and AGV allocation results. Based on the preset algorithm and the processing machine allocation results and AGV allocation results, generate the shortest path and travel time of the AGV transportation process.

[0030] In step S101, the model clock is initialized by setting it to zero; the cycle scheduling time is dynamically adjusted according to the workshop scheduling environment, and a cycle drive scheduling is performed when the workshop processing time reaches the cycle scheduling time.

[0031] In this embodiment of the invention, the expression for calculating the initial variable-cycle drive time based on the average arrival time of the workpiece and the historical processing time is as follows: in, Indicates the sequence number is The time it takes for the workpiece to arrive at the workshop; Indicates the total number of workpieces; This represents the total processing time for all scheduled workpieces; Indicates the number of scheduling operations. 10 is the load adjustment factor set according to the load conditions.

[0032] In step S102, in this embodiment of the invention, the process of updating the workshop scheduling environment when an emergency occurs includes: When an emergency event is a workpiece insertion, the workpiece being processed by the machine continues to perform the processing procedure, the workpiece being transported by the AGV continues to perform the transfer task, the inserted workpiece is added to the workpiece set to be processed, and the workshop scheduling environment and cycle scheduling time are updated.

[0033] When an emergency event is a machine malfunction, the system obtains the occurrence time of the malfunctioning machine and the information of the workpieces being processed on the malfunctioning machine. It then stops the processing of the workpieces on the malfunctioning machine, adds them to the workpiece set to be processed, and updates the workshop scheduling environment. If there are workpieces being sent to the malfunctioning machine on the AGV, it determines whether the start time of the workpieces being transported on the AGV is within the maintenance period. If the start time is within the maintenance period, it sorts the distances of the AGV's current position to other processing machines, sends the workpieces to the processing machine with the smallest distance, adds the workpieces to the workpiece set to be processed, and then updates the workshop scheduling environment and the cycle scheduling time.

[0034] Specifically, the scheduling environment updates include information on the processing machine, AGV, and workpiece, and the cycle scheduling time is recalculated based on these updates. When a workpiece is inserted, the workpiece being processed and the workpiece being transported by the AGV continue to perform the current process. The inserted workpiece is added to the set of workpieces to be processed and then scheduled together with the unprocessed workpieces. In case of machine failure, the Manhattan distance of the current position of the AGV to other processing machines is sorted.

[0035] Furthermore, when the workshop processing time reaches the cycle scheduling time, a cycle-driven scheduling is performed. In this embodiment of the invention, the number of workpieces that have arrived at the work workshop within the current cycle scheduling time is obtained and compared with the number of workpieces that have arrived at the work workshop within the previous cycle scheduling time. If the number of workpieces does not increase, the current workshop scheduling environment and cycle scheduling time are maintained; if the number of workpieces increases, the workpieces being processed and those being transported during the current cycle driving time are controlled to continue to complete their current work, the set of workpieces to be processed is updated, and the workshop scheduling environment and cycle scheduling time are updated.

[0036] In step S103, the processing machine allocation result and AGV allocation result are the optimal task execution sequence generated by the scheduling algorithm after multiple AGVs located at the starting node receive the delivery task. The delivery task includes a task number, a starting point number, an ending point number, a required time, and a deadline. The process of AGVs executing the transportation task includes: the AGV departs from the starting node in an empty state to the first task point; after the AGV completes loading at the task starting node, it travels to the task ending node in a loaded state and performs unloading. The next task starts from the current position and executes the task sequence until all transportation tasks are completed; when all AGVs return to the parking area, it means that all delivery tasks are completed. During the scheduling process, it is ensured that each task starts after the required time and is completed before the deadline to avoid affecting efficiency due to unreasonable task allocation. Based on the preset algorithm and according to the processing machine allocation result and AGV allocation result, the shortest path and travel time of the AGV transportation process are generated. After all task scheduling is completed, an AGV scheduling scheme is generated.

[0037] In this embodiment of the invention, the expression for constructing the objective function is: ; in, , and Indicates the weighting coefficient; Indicates the total mileage traveled by the AGV; Indicates the number of AGVs used; Indicates the maximum completion time; Represents the process from the starting node to the task. Distance from the starting node; Indicates task Is it the serial number? The primary task of the AGV; Indicates task The distance from the starting node to the ending node; Indicates task Is it by serial number? AGV execution; Indicates from the task Termination node to task Distance from the starting node; Indicates the sequence number is Does the AGV complete its task? Execute the task after ; Indicates from the task The distance from the termination node to the parking area; Indicates task Is it the serial number? The tail task of the AGV; Indicates the sequence number is Is the AGV occupied? This indicates that the AGV has completed its task. Time; This indicates the speed at which the AGV travels.

[0038] In this embodiment of the invention, the constraints for constructing the objective function and solving it with the goal of minimizing the objective function include: Time constraints: The AGV's task execution time cannot be earlier than the task's set start time, and the completion time cannot be later than the task's set end time. The expression is: Task allocation constraints: Each transportation task is assigned to one AGV; the expression is: The task completion constraint stipulates that each AGV starts from the set starting node during the work process, completes the task, and returns to the set termination stage; the expression is: in, This indicates that the AGV has started executing the task. iTime; Indicates task i The earliest start time; This indicates that the AGV has completed its task. i Time; Indicates task i The latest completion time; Indicates the sequence number is The starting point for the AGV's first task is the parking area; Indicates the sequence number is The destination of the AGV's tail task is the parking area.

[0039] In this embodiment of the invention, the process of generating the shortest path and travel time from the starting point to the destination of the AGV based on a preset algorithm and according to the allocation results of the processing machine and the AGV includes steps S1031 to S1033: Step S1031: Construct a grid map based on the layout of the work workshop, mark the AGV's stopping point as the starting node, and mark the location of the assigned processing machine as the ending node. Step S1032: Based on the omnidirectional movement characteristics of the AGV casters, obtain the shortest path from the starting node to the ending node of the AGV using the jump point search algorithm. Step S1033: Obtain the geometric distance between each node in the shortest path, calculate the total path length, and obtain the travel time of the AGV from the starting node to the ending node according to the preset AGV travel speed.

[0040] Specifically, the AGV is calculated at a preset constant speed, and the scheduling path adopts the shortest path principle; the Jump Point Search Algorithm (JPS) is used to complete the path planning of the omnidirectional AGV based on the AGV casters, avoiding obstacle avoidance and deadlock problems between devices; the AGV casters are Mecanum wheels.

[0041] On the other hand, the present invention also provides an AGV flexible line scheduling system suitable for smart factories, including a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the steps of the above method are implemented.

[0042] On the other hand, the invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0043] On the other hand, the present invention also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the above-described method.

[0044] The present invention will now be described with reference to a specific embodiment: Figure 2 This is a schematic diagram of the scheduling process according to an embodiment of the present invention. The present invention proposes a flexible AGV line scheduling method and system suitable for smart factories. It adopts a dynamic flexible scheduling strategy that combines event-driven and variable-cycle-driven approaches, while simultaneously updating the workpieces to be processed and the workshop scheduling environment in real time, to achieve dynamic scheduling of the flexible workshop. The process includes: Step 1: Initialize the flexible job shop scheduling environment and model clock.

[0045] Step 2: Initialize the variable cycle drive time based on the dynamic arrival frequency of the workpiece.

[0046] Step 3: Determine if there is an emergency work order insertion or machine malfunction event. If so, proceed to Step 7 to update the workshop scheduling environment.

[0047] Step 4: Calculate the variable cycle drive time based on the workshop load.

[0048] Step 5: Determine whether the current model clock meets the variable period drive timing. If it does, proceed to the next step; otherwise, jump to step 14, model clock update.

[0049] Step 6: Determine if any new workpieces have arrived in the workshop. If new workpieces have arrived, proceed to the next step; otherwise, jump to step 14, Model Clock Update.

[0050] Step 7: Update the workshop scheduling environment, mainly updating the information on workpieces, machines, and AGVs.

[0051] Step 8: Select the workpiece for scheduling.

[0052] Step 9: Select a machine for the workpiece processing procedure.

[0053] Step 10: Select an AGV for handling processed materials.

[0054] Step 11: Select an AGV for workpiece handling.

[0055] Step 12: The AGV determines the travel path of the workpiece and the start and end times of the transport through path planning, thereby determining the start time of the workpiece processing on the machine.

[0056] Step 13: Determine whether all workpieces in the current workshop have been scheduled. If all workpieces have been scheduled, output a new scheduling plan and jump to step 7; otherwise, jump to step 8.

[0057] Step 14: Update the model clock and jump to step 3.

[0058] The dynamic scheduling of the flexible workshop adopts a hybrid response dynamic scheduling strategy that combines events and variable cycles. While ensuring the stability of the production and processing process, it responds promptly to dynamic events in the actual environment. The dynamic scheduling strategy is divided into three scenarios: (1) For the dynamic event strategy of machine failure, the workshop processing information is obtained according to the time of machine failure as the initial scheduling environment of the scheduling algorithm. The processing information of different workpieces is corrected according to the degree of impact on the processing process. When there is a workpiece being processed on the faulty machine, the processing of the workpiece is stopped immediately and the workpiece is returned to the workshop's waiting workpiece collection. The current processing process and the remaining process of the workpiece are rescheduled for processing. When there is a workpiece being sent to the faulty machine by AGV, it is determined according to the calculated processing time of its arrival at the faulty machine. If the start processing time of the process is within the maintenance period of the faulty machine, the workpiece being transported is processed. The AGV is sorted by its Manhattan distance from its current position to other processing machines. Workpieces are sent to the machine with the smallest Manhattan distance and returned to the workpiece set for processing. The workpiece and the remaining unfinished workpieces are then rescheduled. Workpieces being processed on other machines that are not experiencing a fault will continue processing at the time of the fault. After updating the workpiece processing information, the idle time of each machine is determined based on the processing status of the workpieces on the machines, and the workshop scheduling environment is updated. The idle time of the faulty machine is the time when the fault repair is completed, the idle time of the machine currently processing is the time when the current processing step is completed, and the idle time of other machines is the time when the fault occurred.

[0059] (2) Response strategy for emergency workpiece insertion dynamic events: All workpieces being processed and those being transported by AGV at the time of emergency insertion continue to complete the current process, update the inserted workpiece to the workpiece set to be processed and update the workshop scheduling environment, and schedule the workpieces that have not been processed in the current workshop scheduling environment together with the urgently inserted workpieces.

[0060] (3) For the response strategy to the dynamic arrival of workpieces, during the variable period drive scheduling, the number of workpieces that have arrived is determined while updating the set of workpieces to be processed. If the number of workpieces that have arrived in the workshop has not changed, the original scheduling scheme is maintained. If the number of workpieces that have arrived in the workshop increases, all workpieces that are being processed at the time of the period drive scheduling and workpieces that are being transported by AGVs are allowed to continue to complete the current process. In this way, the workshop scheduling environment at the time of the period drive scheduling occurs is obtained, and the drive scheduling algorithm schedules the workpieces in the set of workpieces to be processed. The real-time performance of the period drive scheduling strategy is affected by the period frequency. The impact caused by improper period frequency setting is reduced by setting a variable period.

[0061] Furthermore, the variable cycle time is calculated based on the dynamic arrival frequency of the workpiece. To avoid excessively long machine idle time in the workshop and the inability to process dynamically arriving workpieces in a timely manner, which would lead to a decrease in production efficiency; the variable cycle time is proportional to the workshop load; when the workshop processing time reaches the cycle scheduling time, a cycle-driven scheduling is performed.

[0062] The AGV task scheduling problem in a smart logistics system can be described as optimizing the allocation of a set of material delivery tasks generated by a task order system within a certain time period. Each delivery task contains key information such as task number, starting point number, ending point number, required time, and deadline. After a delivery task is issued, multiple AGVs at the starting point are scheduled using an algorithm to complete the optimal task execution sequence. AGVs execute transportation tasks in two phases: in the first phase, the AGVs depart from the starting point in an empty state and head to the first task point; in the second phase, after loading at the task starting point, the AGVs travel to the task ending point in a loaded state and perform unloading operations; subsequent tasks start from the current position and execute the next task. After completing all tasks, the AGVs return to the ending point to await the next round of task sequence allocation; the return of all AGVs to the parking area indicates that all delivery tasks are completed. During the scheduling process, it is necessary to ensure that each task starts after the required time and is completed before the deadline.

[0063] When performing scheduling problems, this invention sets the AGV to a constant speed and adopts the shortest path principle for scheduling path planning. It uses the conventional JPS algorithm to complete the path planning of the omnidirectional AGV based on the Mecanum wheel, avoiding obstacle avoidance and deadlock problems between devices; Table 1 is the definition table of the core parameters of the AGV.

[0064] Table 1 Definition of AGV Core Parameters After defining variables, time constraints, task allocation constraints, and task termination constraints are applied to the AGV operation process. The time constraint requires that the AGV executes a task no earlier than the task's required time, and the completion time must not exceed the task's deadline. The task allocation constraint requires that AGVs be reasonably allocated when executing tasks to ensure that each task can be assigned to only one AGV. The task termination constraint requires that each AGV in use must start from the specified starting position, complete the delivery task according to the planned task sequence, and finally return to the specified ending position. Each AGV must have one and only one first task as the starting point of its task sequence, and it must also have one and only one last task as the ending point of its task sequence.

[0065] Furthermore, AGV scheduling optimization involves multi-dimensional indicators, including completion time, total AGV running distance, number of AGVs used, energy consumption, and task waiting time. Based on the actual requirements of the intelligent factory production line, the total AGV running distance, number of AGVs used, and maximum completion time are selected as optimization objectives. The multi-objective problem is a typical NP-hard problem. Genetic algorithms can be used to solve the combinatorial optimization problem and generate the corresponding optimal scheduling scheme. The AGVs follow the scheduling scheme to carry out production work in the workshop.

[0066] Figure 3 This is a schematic diagram of an AGV flexible production line scheduling system suitable for smart factories according to an embodiment of the present invention; the present invention also provides an AGV flexible production line scheduling system suitable for smart factories, used to execute the above-mentioned AGV flexible production line scheduling method suitable for smart factories; the system includes an input layer, a control and scheduling layer, a resource execution layer, a data storage layer, and an output and monitoring layer.

[0067] Input layer: The starting point for system scheduling decisions, providing the system with basic data including static workshop data, dynamic arrival distribution of workpieces, and dynamic event interfaces.

[0068] Control and Scheduling Layer: The core decision-making center of the system. The clock management module maintains the simulation clock and controls the time progression; the event listening and response module detects dynamic events in real time and executes response processing; the variable cycle drive module dynamically calculates the scheduling cycle based on the workshop load and decides to start and stop the scheduling; the scheduling execution module executes specific scheduling decisions and generates a process timetable.

[0069] Resource Execution Layer: Responsible for the maintenance and path calculation of specific resources, providing support for scheduling. The resource management module maintains the real-time status of machines and AGVs, recording resource occupancy and release; the path planning module calculates the travel path for AGVs and determines the start and end times of transport; the workshop physical entity represents the physical objects in the workshop and is the operation object of the resource management module.

[0070] Data storage layer: responsible for persistently storing data generated during system operation for subsequent querying and analysis; workshop status database stores real-time status data of all entities in the current workshop; scheduling scheme library stores each generated complete scheduling scheme and its evaluation indicators; event log library records all dynamic events and their processing.

[0071] Output and Monitoring Layer: This layer provides managers with visualization and statistical analysis functions, displaying scheduling plans graphically and calculating key performance indicators, showing the current operating status of the workshop.

[0072] In summary, this invention provides a flexible AGV production line scheduling method and system suitable for smart factories. It establishes a scheduling model for the workshop and initializes the scheduling environment based on machine information, AGV information, and workpiece information. After setting the model clock to the initial time, it calculates the initial variable-cycle drive time based on the average arrival time of workpieces and historical processing times. It acquires the status of the machine and workpiece in real time, and updates the workshop scheduling environment and variable-cycle drive time when an emergency event occurs or the model clock reaches the initial cycle scheduling time. Emergency events include workpiece insertion and machine failure. It constructs an objective function and solves it with the objective function minimization as the goal to generate machine allocation results and AGV allocation results. Based on a preset algorithm and the machine and AGV allocation results, it generates the shortest path and travel time for the AGV transportation process.

[0073] This invention also provides an AGV flexible line scheduling system suitable for smart factories, including a processor and a memory. The memory stores computer instructions, and the processor executes the computer instructions stored in the memory. When the computer instructions are executed by the processor, the steps of the above method are implemented.

[0074] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method.

[0075] This invention also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the above-described method.

[0076] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0077] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A flexible AGV production line scheduling method suitable for smart factories, characterized in that, The method includes: Establish a scheduling model for the workshop and initialize the scheduling environment based on the processing machine information, AGV information and workpiece information. After setting the model clock to the initial time, calculate the initial variable cycle drive time based on the average arrival time of the workpiece and the historical processing time. Real-time acquisition of machine and workpiece status; updating the shop floor scheduling environment and variable cycle drive time when an emergency event occurs or the model clock reaches the initial cycle scheduling time; emergency events include workpiece insertion and machine failure. Construct an objective function and solve it with the goal of minimizing the objective function to generate processing machine allocation results and AGV allocation results. Based on a preset algorithm and the processing machine allocation results and AGV allocation results, generate the shortest path and travel time for the AGV transportation process.

2. The AGV flexible line scheduling method for smart factories according to claim 1, characterized in that, The expression for calculating the initial variable-cycle drive time based on the average arrival time of the workpiece and the historical processing time is as follows: in, Indicates the sequence number is The time it takes for the workpiece to arrive at the workshop; Indicates the total number of workpieces; This represents the total processing time for all scheduled workpieces; Indicates the number of scheduling attempts.

3. The AGV flexible line scheduling method for smart factories according to claim 1, characterized in that, The process of updating the shop floor scheduling environment and variable cycle drive time in the event of an emergency includes: When an emergency event is a workpiece insertion, the workpiece being processed by the processing machine continues to perform the processing procedure, the workpiece being transported by the AGV continues to perform the transfer task, the inserted workpiece is added to the workpiece set to be processed and the workshop scheduling environment and variable cycle drive time are updated. When an emergency event is a machine malfunction, the system obtains the occurrence time of the malfunctioning machine and the information of the workpieces being processed on the malfunctioning machine. It then stops the processing of the workpieces on the malfunctioning machine, adds them to the workpiece set to be processed, and updates the workshop scheduling environment. If there are workpieces being sent to the malfunctioning machine on the AGV, it determines whether the start time of the workpiece being transported on the AGV is within the maintenance period. If the start time is within the maintenance period, it sorts the distances of the AGV's current position to other processing machines, sends the workpiece to the processing machine with the smallest distance, adds the workpiece to the workpiece set to be processed, and then updates the workshop scheduling environment and the variable cycle drive time.

4. The AGV flexible line scheduling method for smart factories according to claim 1, characterized in that, The process of updating the shop floor scheduling environment and variable-cycle drive time when the model clock reaches the initial cycle scheduling time includes: Obtain the number of workpieces that have arrived at the workshop within the current cycle scheduling time and compare it with the number of workpieces that have arrived at the workshop within the previous cycle scheduling time. If the number of workpieces has not increased, maintain the current workshop scheduling environment and cycle scheduling time. If the number of workpieces increases, control the workpieces being processed and the workpieces being transported during the current cycle driving time to continue to complete the current work, update the set of workpieces to be processed, and update the workshop scheduling environment and cycle scheduling time.

5. The AGV flexible line scheduling method for smart factories according to claim 1, characterized in that, The expression for constructing the objective function is: ； in, , and Indicates the weighting coefficient; Indicates the total mileage traveled by the AGV; Indicates the number of AGVs used; Indicates the maximum completion time; Represents the process from the starting node to the task. Distance from the starting node; Indicates task Is it the serial number? The primary task of the AGV; Indicates task The distance from the starting node to the ending node; Indicates task Is it by serial number? AGV execution; Indicates from the task Termination node to task Distance from the starting node; Indicates the sequence number is Does the AGV complete its task? Execute the task after ; Indicates from the task The distance from the termination node to the parking area; Indicates task Is it the serial number? The tail task of the AGV; Indicates the sequence number is Is the AGV occupied? This indicates that the AGV has completed its task. Time; This indicates the speed at which the AGV travels.

6. The AGV flexible line scheduling method for smart factories according to claim 1, characterized in that, The constraints for constructing the objective function and solving it with the goal of minimizing the objective function include: Time constraints: The AGV's task execution time cannot be earlier than the task's set start time, and the completion time cannot be later than the task's set end time. The expression is: Task allocation constraints: Each transportation task is assigned to one AGV; the expression is: The task completion constraint stipulates that each AGV starts from the set starting node during the work process, completes the task, and returns to the set termination stage; the expression is: in, This indicates that the AGV has started executing the task. i Time; Indicates task i The earliest start time; This indicates that the AGV has completed its task. i Time; Indicates task i The latest completion time; Indicates the sequence number is The starting point for the AGV's first task is the parking area; Indicates the sequence number is The destination of the AGV's tail task is the parking area.

7. The AGV flexible line scheduling method for smart factories according to claim 1, characterized in that, The process of generating the shortest path and travel time from the AGV's starting point to its destination based on a preset algorithm and the allocation results of processing machines and AGVs includes: A grid map is constructed based on the layout of the workshop, and the stopping points of the AGVs are marked as the starting nodes, and the positions of the assigned processing machines are marked as the ending nodes. Based on the omnidirectional movement characteristics of AGV casters, the shortest path from the starting node to the ending node of the AGV is obtained through a jump point search algorithm. Calculate the total path length by obtaining the geometric distance between each node in the shortest path, and obtain the travel time of the AGV from the starting node to the ending node based on the preset AGV travel speed.

8. A flexible AGV production line scheduling system suitable for smart factories, comprising a processor and a memory, characterized in that, The memory stores computer instructions, and the processor executes the computer instructions stored in the memory to implement the steps of the method as described in any one of claims 1 to 7 when the computer instructions are executed by the processor.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 7.

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