A method for improving the processing efficiency of an intelligent manufacturing system based on label promotion

Abstract

This application provides a method for improving the processing efficiency of an intelligent manufacturing system based on tags. The intelligent manufacturing system includes a data resource pool, a production scheduling center, an AGV robot scheduling center, AGV robots, processing stations, a waiting-to-process area, and a finished product area. The data resource pool is equipped with tags, including processing tags and handling tags. The processing tags include the processing sequence, processing steps, processing station, and processing time for each part to be processed, used to track the processing of each step of the part at different processing stations. After the part is labeled with a processing tag, it enters the waiting-to-process area. The handling tags are used by the AGV robots to transport parts between different locations in the raw material area, processing area, and finished product area according to planned paths and self-finding paths. This method solves the problem in existing intelligent manufacturing processes where the handling time of each step in the AGV workpiece transportation process is long, resulting in a long overall processing time and reduced production efficiency.

Description

Technical Field

[0001] This application belongs to the field of intelligent manufacturing technology, specifically relating to a method for improving the processing efficiency of intelligent manufacturing systems based on tags. Background Technology

[0002] Service-oriented manufacturing is a key trend in the global manufacturing upgrade. For manufacturing enterprises, enhancing specialization and service levels is a crucial means for my country's manufacturing industry to occupy the high end of the global value chain. AGVs (Automated Guided Vehicles) are intelligent transport vehicles and an important component of intelligent manufacturing systems. They can effectively complete designated tasks in unmanned factories and warehouses. In the process of producing workpieces according to optimized procedures in intelligent manufacturing systems, to ensure that workpieces are processed quickly at different workstations according to the procedures, AGV robots should be scheduled according to the actual processing situation to rapidly complete the workpiece transport based on optimized scheduling results.

[0003] There are many problems in the processing of existing intelligent manufacturing processes, including low scheduling efficiency and chaotic scheduling programs. The production scheduling process of AGV workpiece handling has long handling time for each process, resulting in long overall processing time and reduced production efficiency. Summary of the Invention

[0004] Therefore, the technical problem to be solved by this application is to provide a method for improving the processing efficiency of intelligent manufacturing systems based on tags, which can solve the problem that the processing scheduling process of AGV workpiece handling in existing intelligent manufacturing processes has long handling time in each process, resulting in long overall processing time and reduced production efficiency.

[0005] To address the aforementioned issues, this application provides a method for improving the processing efficiency of an intelligent manufacturing system based on tags. The intelligent manufacturing system includes a data resource pool, a production scheduling center, an AGV robot scheduling center, AGV robots, processing stations, a waiting area, and a finished product area. The data resource pool is equipped with tags.

[0006] The labels include processing labels and handling labels. The processing labels include the processing sequence, processing steps, processing stations, and processing time for each part to be processed. They are used to track the processing of each step of the part to be processed at different processing stations. After the part to be processed is labeled with a processing label, it enters the processing area.

[0007] The transport tags are used by AGV robots to transport materials between different locations in the raw material area, processing area, and finished product area according to the planned path and the self-finding path.

[0008] The processing labels and handling labels are continuously updated based on the processing and handling of the parts to be processed, to obtain information on the processing and handling process of the parts to be processed;

[0009] The method includes:

[0010] Retrieve order information from the data resource pool;

[0011] Based on order information, the production scheduling center obtains processing labels;

[0012] The processing label is scanned at the processing station to obtain the incomplete processing steps of the workpiece;

[0013] Based on the workshop environment map stored in the AGV robot scheduling center, obtain the handling tags of the parts to be processed;

[0014] Based on the handling tags, the AGV robot scheduling center obtains the instructions for the AGV robot to handle the workpieces to be processed, and the workpieces to be processed are handled according to the processing steps.

[0015] The entire processing of the workpiece is carried out by AGV robots until it is moved to the finished product area.

[0016] Processing labels and handling labels are stored in a data resource pool.

[0017] Optionally, the processing sequence, processing steps, and processing stations in the processing label are fixed values, while the processing time is continuously updated based on the processing time of the processing station.

[0018] Optionally, the processing tags can be obtained from the production scheduling center based on order information, including:

[0019] Based on the order information, the production scheduling center obtains the processing steps and processing time of the parts to be processed, sorts them according to the processing steps and processing time at the workshop processing stations, obtains the processing steps and processing time of the parts to be processed, assigns a processing sequence number to the parts to be processed, and processes them according to the processing sequence number.

[0020] Optionally, scanning the processing label at the processing station to obtain incomplete processing steps of the workpiece includes:

[0021] The workpiece to be processed is moved from the waiting area to the processing station. The processing time of the workpiece is obtained by scanning the processing label, and it is determined whether the processing step is completed.

[0022] If the processing time is complete, the processing procedure of this workstation is completed. If there is no processing time, the processing procedure of this workstation is completed according to the processing sequence. After processing is completed, the processing time of the processing procedure of this workstation is added to the processing label, and the processing procedure of this workstation is sent to the AGV robot scheduling center.

[0023] Optionally, based on the workshop environment map stored in the AGV robot scheduling center, the handling tags of the parts to be processed can be obtained, including:

[0024] Based on the AGV robot scanning the workshop environment, the location information of all processing stations, waiting areas, finished product areas and obstacles in the workshop is obtained;

[0025] Based on the acquired location information, a workshop environment map is created, and all processing stations, waiting areas, finished product areas, and obstacles are marked on the map;

[0026] Based on the workshop environment map, the paths between processing stations, between the waiting area and the work station, and between the finished product area and the processing station are updated to obtain the actual handling paths, which are then stored in the AGV robot scheduling center.

[0027] Based on the actual handling path obtained, a grid model is established by scanning the workshop environment with an AGV robot, the coordinates of each grid are marked, and the path cost function is obtained.

[0028] Optionally, based on the handling tag, obtaining the AGV robot scheduling center's instructions for scheduling AGV robots to handle workpieces includes:

[0029] The AGV robot scheduling center accepts two types of request instructions: the first type is a handling request sent by the production scheduling center according to the processing steps of the workpiece to be processed and the planned processing start time; the second type is a handling request issued based on the completion of processing at the processing station. The AGV robot scheduling center queues the handling requests and schedules the AGV robots to handle the tasks according to the request time and the location of the AGV robots.

[0030] Optionally, the AGV robot scheduling center may send the following first type of instruction:

[0031] AGV robots transport the workpieces from the processing area to the processing station corresponding to the first processing step until they are transported to the finished product area. The transport path for each transport is added to the transport tag of the workpiece, allowing for full tracking of the entire transport process.

[0032] The second type of instruction sent by the AGV robot dispatch center includes:

[0033] Locate any idle AGV robots around the processing station. Based on the application time and the location of available AGV robots, compare the distance between the processing station and the idle AGV robots. Assign AGV robots to handle tasks based on the distance. AGV robots that are in operation do not accept task assignments.

[0034] Optionally, all processing steps involving the use of AGV robots to transport the workpieces to the finished product area include:

[0035] The handling path for all processing steps of the AGV robot handling the workpiece includes:

[0036] When the first transport path is unobstructed, the AGV robot transports the workpiece according to the transport tag sent by the AGV robot scheduling center. After the transport is completed, it sends the transport completion status information to the production scheduling center and enters the non-working state of waiting for task assignment.

[0037] When there are obstacles in the second transport path, the AGV robot scans the environment around the obstacles and uses transport tags to avoid them, obtains a self-searching path, and completes the transport of the workpiece to be processed according to the self-searching path. After the transport is completed, it sends the transport completion status information to the production scheduling center, and the AGV robot enters the non-working state of waiting for task assignment.

[0038] Optionally, the first transport path includes:

[0039] The AGV robot runs to the assigned processing station or waiting area, scans the processing label on the workpiece to obtain the completed processing steps and the next processing step of the workpiece, and obtains the processing station for transporting the workpiece.

[0040] The AGV robot interacts with the AGV robot scheduling center. The AGV robot scheduling center obtains the first transport path based on the AGV robot's position and the processing station where the workpiece is to be transported, and sends the first transport path to the AGV robot. The AGV robot completes the transport of the workpiece and updates the first path and time information to the transport tag of the workpiece.

[0041] The second transport path includes:

[0042] When the AGV robot avoids obstacles, it scans the surrounding environment of the obstacle to obtain a map of the surrounding environment. Using a path cost function, based on the position of the AGV robot, it obtains the number of turns and the length of the path traveled by the AGV robot to obtain a second transport path.

[0043] Using the second transport path, the AGV robot passes through obstacles and updates the second transport path and time information to the transport label of the workpiece to be processed.

[0044] Optionally, storing processing tags and handling tags in a data resource pool includes:

[0045] Once the parts to be processed are finished, they enter the finished product area. By scanning the processing label and handling label of the parts to be processed, the processing information and handling information of the parts to be processed are obtained, and the processing information and handling information are uploaded to the production scheduling center, which then uploads them to the data resource pool.

[0046] Beneficial effects

[0047] The present invention provides a method for improving the processing efficiency of an intelligent manufacturing system based on tags. This method obtains order information from a data resource pool, acquires processing tags based on the order information, scans the processing tags to obtain the processing steps of the workpiece, and then uses an AGV robot to transport the workpiece using transport tags. During processing and transport, the processing and transport tags are continuously updated, thereby improving both processing and transport speeds. The updated processing and transport tags are stored in the data resource pool. This solves the problem in existing intelligent manufacturing processes where the transport time for each step in the AGV workpiece handling process is long, resulting in a long overall processing time and reduced production efficiency. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of the composition of an intelligent manufacturing system according to an embodiment of this application;

[0049] Figure 2 This is a production flow diagram of the intelligent manufacturing system according to an embodiment of this application;

[0050] Figure 3 This is a flowchart illustrating the AGV robot handling process according to an embodiment of this application.

[0051] Figure 4 This is a schematic diagram of the path from the processing area to processing station No. 1 in an embodiment of this application;

[0052] Figure 5 This is a schematic diagram of the path from machining station 1 to machining station 4 in an embodiment of this application.

[0053] Figure 6 This is a schematic diagram of the path from processing station No. 4 to the finished product area in an embodiment of this application;

[0054] Figure 7 This is a schematic diagram of the second transport path from the processing area to processing station No. 1 in an embodiment of this application;

[0055] Figure 8 This is a schematic diagram of the second transport path from processing station 1 to processing station 4 in an embodiment of this application.

[0056] Figure 9 This is a schematic diagram of the second transport path from processing station No. 4 to the finished product area in an embodiment of this application. Detailed Implementation

[0057] In the description of this application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting the present invention.

[0058] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0059] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0060] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0061] See also Figures 1 to 9 As shown, according to an embodiment of this application, please refer to... Figure 1A method for improving the processing efficiency of an intelligent manufacturing system based on tags is disclosed. The intelligent manufacturing system includes a data resource pool, a production scheduling center, an AGV robot scheduling center, AGV robots, processing stations, a waiting-to-process area, and a finished product area. The data resource pool is equipped with tags, including processing tags and handling tags. Processing tags include the processing sequence, processing steps, processing station, and processing time for each part to be processed, used to track the processing of each step at different processing stations. After the part is tagged, it enters the waiting-to-process area. Handling tags are used by the AGV robots to transport materials between different locations in the raw material area, processing area, and finished product area according to planned paths and autonomous paths. The processing tags and handling tags are continuously updated based on the processing and handling of the parts to be processed, obtaining information about the processing and handling process.

[0062] Please refer to Figure 2 The methods include:

[0063] Retrieve order information from the data resource pool;

[0064] Based on order information, the production scheduling center obtains processing labels;

[0065] The processing label is scanned at the processing station to obtain the incomplete processing steps of the workpiece;

[0066] Based on the workshop environment map stored in the AGV robot scheduling center, obtain the handling tags of the parts to be processed;

[0067] Based on the handling tags, obtain the AGV robot scheduling center's instructions to schedule AGV robots to handle the workpieces to be processed, and carry out the handling according to the processing steps of the workpieces to be processed;

[0068] The entire processing of the workpiece is carried out by AGV robots until it is moved to the finished product area.

[0069] Processing tags and handling tags are stored in a data resource pool. These tags guide the workpieces to be processed according to optimized procedures, while AGV robots transport them along optimized paths, improving processing efficiency and transport efficiency between different workstations. This ultimately enhances the overall production efficiency of the intelligent manufacturing system.

[0070] Furthermore, the data resource pool stores order information, the processes that can be processed at each processing station, their processing time, and processing tracking information. The order information and the processes that can be processed at each processing station, along with their processing time, are used to generate the optimal processing processes for the workpiece, as well as the corresponding processing stations and processing sequence.

[0071] Furthermore, to improve workpiece processing efficiency, the production scheduling center optimizes the processing stations corresponding to the processing steps of the workpieces to be processed in the order based on the user's order tasks, as well as the processing sequence and entry time of each workpiece into the processing area, thereby optimizing the completion time of the order tasks.

[0072] Furthermore, the AGV robot scheduling center, in order to improve the efficiency of transporting workpieces between different locations, stores all the optimal paths planned after the AGV robots scan the workshop environment. The stored optimal paths include globally optimal paths between different locations in the waiting area, processing stations, and finished product area.

[0073] Furthermore, the processing station scans the processing label, completes the processing of this workpiece, and updates the processing label.

[0074] Furthermore, the processing area stores workpieces arranged in the processing order, each with a processing label, waiting to be dispatched by the dispatch center to the first processing station in the processing area.

[0075] Furthermore, after the finished product area sends the processing label information and the handling label information to the production scheduling center, the processing label and handling label are cleared, the labels are retrieved, and they can continue to be used by the parts waiting to be processed in the raw material area.

[0076] Furthermore, processing labels and handling labels are used throughout the entire processing and handling process of each workpiece, enabling the workpiece to be processed according to the preferred procedures and transported along the preferred paths, thereby improving the production efficiency of the intelligent manufacturing system.

[0077] Furthermore, processing tags are used to track the processing of each workpiece after optimization at different processing stations. The initial information of the processing tags includes the processing sequence, processing steps, processing station, and processing time for each workpiece. After the workpiece is labeled with a processing tag, it enters the processing area. AGV handling tags are used by AGV robots to transport materials between different locations in the raw material area, processing area, and finished product area according to the planned optimal path and the self-finding optimal path. The processing tags and handling tags are updated as the workpiece is processed and transported, recording complete processing and handling information for the workpiece, providing effective data for further improving processing efficiency.

[0078] Furthermore, the processing tracking information includes the processing station, processing time, and transportation path and time between each processing step after the completion of each workpiece. The processing tracking information is used for subsequent production process analysis to further improve production efficiency.

[0079] The processing sequence, processing steps, and processing stations in the processing labels are fixed values, while the processing time is continuously updated based on the processing time of each processing station. By continuously updating the processing labels, the fastest processing time is selected, thereby improving the production efficiency of the intelligent manufacturing system.

[0080] Furthermore, based on the optimization results, processing labels are designed for each workpiece to be processed, ensuring that all workpieces are processed quickly according to the processing steps, thereby improving the processing efficiency of the intelligent manufacturing system.

[0081] Furthermore, the processing sequence, processing steps, and processing stations corresponding to each process of each workpiece are all generated by the scheduling center based on the order information. Each workpiece is numbered according to the processing sequence, and the system processes it in the order of the numbers. This part of the label information serves as the initial value of the processing label, which is also a fixed value and does not change with the processing process.

[0082] Based on order information, the production scheduling center obtains processing tags, including:

[0083] The production scheduling center obtains the processing steps and times for the parts to be processed based on order information. It then sorts the parts according to their processing steps and times at the workshop workstations, assigns a processing sequence number to each part, and processes them accordingly. The system continuously updates the processing labels based on changes in workstations and times, thus improving overall system efficiency.

[0084] Furthermore, the processing tag information generated by the production scheduling center is used to analyze the relevant processes and processing time of each process for the parts to be processed in the current order. The center then optimizes the sorting based on the processing processes and times that each workstation in the workshop can handle, resulting in the corresponding workstation and processing sequence for each part and each processing process. The processing time in the processing tag is updated based on the actual processing time of the workstation, with an initial value of 0.

[0085] Based on the definition of the processing label, the initial values ​​of the processing label are shown in Table 1 below:

[0086] P1 P2 A(1) S(1) T(1) ... A(i) S(i) T(i) ... A(N) S(N) T(N) END

[0087] Table 1

[0088] Where P1 represents the smart manufacturing workshop number for producing a certain product; P2 represents the order number of the product produced on that day; A represents the processing procedure; S represents the processing station; T represents the processing time; and the initial value of the processing time is 0.

[0089] By scanning the processing label at the processing station, the incomplete processing steps of the workpiece can be identified, including:

[0090] The workpiece to be processed is moved from the waiting area to the processing station. The processing time of the workpiece is obtained by scanning the processing label, and it is determined whether the processing step is completed.

[0091] If the processing time is complete, the processing step at this workstation is considered finished. If there is no processing time, the processing steps at this workstation are completed according to the processing sequence. After processing is completed, the processing time of the processing step at this workstation is added to the processing tag, and the AGV robot scheduling center is notified that the processing step at this workstation for the workpiece is complete. By scanning the processing tags at the processing workstation, the completed and incomplete processing steps of the workpiece can be obtained. This allows for real-time monitoring of the production progress of the workpiece, preventing any missed processing steps, and enabling adjustments to the processing steps and processing time, updating processing tags, and prioritizing the processing tags with the fastest processing speed, thereby improving the system's production efficiency.

[0092] Furthermore, the processing station scans the processing label, processes the workpiece according to the optimized processing procedure, scans the processing label to obtain processing label information, and completes the processing procedure for this workpiece.

[0093] Furthermore, after the processing station obtains the workpiece to be processed, it scans the processing label, obtains the completed processes of the workpiece based on the processing label, and processes the next process after the completed process according to the processing process sequence.

[0094] Furthermore, the completion status of a process is determined based on the processing time in the processing label. If the processing time is not zero, it indicates that the processing process is complete. The last processing process with a non-zero processing time is found, and the next processing process is completed in the order of this processing process.

[0095] Furthermore, the processing station completes the processing steps for the workpiece, updates the processing label, fills in the processing time for this processing step into the processing label, and sends the handling instruction for the workpiece to the AGV robot scheduling center.

[0096] Please refer to Figure 3 Based on the workshop environment map stored in the AGV robot scheduling center, the handling tags of the parts to be processed are obtained, including:

[0097] Based on the AGV robot scanning the workshop environment, the location information of all processing stations, waiting areas, finished product areas and obstacles in the workshop is obtained;

[0098] Based on the acquired location information, a workshop environment map is created, and all processing stations, waiting areas, finished product areas, and obstacles are marked on the map;

[0099] Based on the workshop environment map, the paths between processing stations, between the waiting area and the work station, and between the finished product area and the processing station are updated to obtain the actual handling paths, which are then stored in the AGV robot scheduling center.

[0100] Based on the acquired actual transport paths, a grid model is established using the workshop environment scanned by AGV robots. The coordinates of each grid cell are labeled, and the path cost function is obtained. By scanning the workshop environment with AGV robots, the path for transporting parts to be processed can be better determined, and different fast transport paths can be selected according to different parts, thereby improving the system's production efficiency.

[0101] Furthermore, in order to obtain the optimal handling path, the AGV robot scans the workshop environment to obtain the location information of all processing stations, waiting areas, finished product areas and obstacles in the workshop. Based on the obtained location information, a workshop environment map is created, and all processing stations, waiting areas, finished product areas and obstacles are marked on the map.

[0102] Furthermore, based on the constructed workshop environment map, path optimization is performed on all paths in each transport interval between each processing station, between the waiting area and each processing station, and between the finished product area and each processing station, to obtain the globally optimal path for different transport intervals.

[0103] Furthermore, to optimize the globally optimal paths between each processing station, between the waiting area and each processing station, and between the finished product area and each processing station, a grid model is established for the workshop environment scanned by the AGV robot. The coordinates of each grid are labeled, and transport labels for the transport paths are defined. The waiting area is defined as W, the processing station as S(i), and the processing station numbers are i = 1, 2, 3, ..., n. The finished product area is defined as P. In the optimal path planning, the path cost is the objective of path optimization, where the path cost value is:

[0104] minL=μ1·a+μ2·b

[0105] Where L is the path cost, μ1 is the weight of the number of turns, μ2 is the weight of the path length, a is the number of turns from the starting point to the ending point, and b is the path length from the starting point to the ending point.

[0106] Furthermore, the optimization methods in this application are not limited to genetic algorithms, ant colony algorithms, and particle swarm optimization algorithms.

[0107] Furthermore, the constructed paths after global optimal path optimization include: raw material area - processing station 1, path 1; raw material area - processing station 2, path 2; processing station i - processing station j, path i... processing station j - finished product area, path n;

[0108] Forming the global optimal path table 2:

[0109] 1 W S(1) a(1)(1) ... a(1)(j) ... a(1)(M) END 2 W S(2) a(2)(1) ... a(1)(j) ... a(1)(M) END ... ... ... ... ... ... ... ... END i S(i) S(j) a(i)(1) ... a(i)(j) ... a(i)(M) END n S(j) P a(n)(1) ... a(n)(j) ... a(n)(M) END

[0110] Table 2

[0111] Furthermore, the AGV robot scheduling center stores the globally optimal path between different locations. When the workpiece is being moved between different locations, it provides a handling tag for the workpiece. Before the AGV robot starts the handling task, the globally optimal path is sent to the AGV robot to update the handling tag.

[0112] Based on the handling tags, the instructions from the AGV robot scheduling center to dispatch AGV robots to handle workpieces include:

[0113] The AGV robot scheduling center accepts two types of requests: the first is a handling instruction sent by the production scheduling center according to the processing steps of the workpiece and the planned start time; the second is a handling request issued based on the completion of processing at the workstation. The AGV robot scheduling center queues the handling requests and schedules the AGV robots to handle the workpieces based on the request time and the AGV robot's position. By issuing different instructions from the AGV robot scheduling center, different handling paths can be selected for different workpieces, thereby improving the system's production efficiency.

[0114] Furthermore, the first type of instruction is a transport instruction sent by the production scheduling center according to the processing sequence of the parts to be processed and the planned processing start time.

[0115] Furthermore, the second type of instruction is that when a processing station completes its work and issues a completion signal, the AGV robot scheduling center will queue the requested handling tasks and arrange for the AGV robot to handle them according to the application time and the location of the AGV robot.

[0116] The first type of instruction sent by the AGV robot dispatch center includes:

[0117] AGV robots transport workpieces from the processing area to the processing station corresponding to the first processing step, and finally to the finished product area. The transport path for each transport is added to the workpiece's transport tag, allowing for full tracking of the entire process. By continuously optimizing the transport route and updating the transport tags during the AGV robot's transport process, valuable analysis of the entire system's production capabilities is provided, thereby improving system efficiency.

[0118] Furthermore, during the processing of the workpiece according to the processing steps, from the time the workpiece is transported by the AGV robot from the processing area to the processing station corresponding to the first processing step to the final transport to the finished product area, the transport path of each transport is added to the transport tag of the workpiece. This includes the first transport path and the second transport path in the obstacle avoidance local self-searching path. The transport tag is updated, and finally a complete transport tag is formed. The entire transport process is tracked.

[0119] The second type of instruction sent by the AGV robot dispatch center includes:

[0120] The system locates idle AGV robots around the processing station. Based on the request time and the location of available AGV robots, it compares the distance between the processing station and the idle AGV robots, and assigns a AGV robot to handle tasks based on the distance. AGV robots that are already in operation do not accept task assignments. By locating idle AGV robots around the processing station, the system can schedule AGV robots based on proximity, saving scheduling time and improving system production efficiency.

[0121] Furthermore, the AGV robot scheduling center, based on the application time of the handling instructions from the production scheduling center and the processing station, and combined with the location of the idle robots near the processing station that are not working, compares the distance of the idle robots near the processing station for processing stations that request handling at similar times, and allocates AGV robots for handling based on distance priority.

[0122] Furthermore, the AGV robot scheduling center will assign tasks to this AGV robot, and the AGV robot will no longer accept new task assignments.

[0123] The entire processing of the workpieces by AGV robots, up to and including the transfer of the workpieces to the finished product area, includes:

[0124] The handling path for all processing steps of the AGV robot handling the workpiece includes:

[0125] When the first transport path is unobstructed, the AGV robot transports the workpiece according to the transport tag sent by the AGV robot scheduling center. After completing the transport, it sends a transport completion status information to the production scheduling center, and then enters a waiting-for-task-assignment inactive state. During the transport of the workpiece, the AGV robot passes smoothly without encountering any obstacles, completing the transport quickly and improving transport speed.

[0126] After receiving the globally optimal path, the AGV robot begins to transport materials. Depending on whether it encounters obstacles, the transport path can be either the globally optimal path or a locally self-optimized path that incorporates obstacle avoidance scanning by the AGV robot.

[0127] The first transport path includes: the AGV robot runs to the assigned processing station or waiting area, scans the processing label on the workpiece to obtain the completed processing steps and the next processing step of the workpiece, and obtains the processing station for transporting the workpiece; the AGV robot interacts with the AGV robot scheduling center, which obtains the first transport path based on the AGV robot's position and the processing station for transporting the workpiece, and sends the first transport path to the AGV robot; the AGV robot completes the transport of the workpiece and updates the first path and time information to the transport label of the workpiece.

[0128] Furthermore, after the AGV robot arrives at its assigned task processing station or waiting area, it scans the processing label on the workpiece to be processed, queries the completed processes and the next process, obtains the destination of the transport, i.e., the processing station of the workpiece, and interacts with the AGV robot scheduling center to obtain the optimal path for this transport. Specifically, the AGV robot scheduling center queries the globally optimal path based on the AGV robot's task position and transport destination, and sends the found optimal path to the robot.

[0129] Furthermore, the AGV robot receives the globally optimal path sent by the AGV robot scheduling center, adds a transport tag to the optimal path for this transport task, and begins transporting.

[0130] Furthermore, the AGV robot interacts with the AGV robot scheduling center to obtain the globally optimal path for this transport, i.e., the first transport path, and adds a transport tag. The transport tag for the i-th transport of the workpiece to be processed is shown in Table 3 below:

[0131] R(i) a(i)(1) ... a(i)(j) ... a(i)(M) T(i) F(i) END

[0132] Table 3

[0133] Where R(i) represents the robot label of this handling task; a(i)(j) is the coordinate of the j-th point passed through by the i-th path; a(i)(M) is the endpoint of the i-th path, i.e. the coordinate of the destination workstation of this handling task, i = 1...N, which is determined by the processing steps of the workpiece; j = 1...M, where M is determined by the path length; T(i) represents the completion time of this handling task; F(i) is a flag indicating whether an obstacle is encountered, which takes the value 1 or 0. The initial value of this bit is 0, indicating that the handling is carried out according to the planned global optimal path.

[0134] If the value is 1, it indicates that the AGV autonomously scans the map during the transport process and detects obstacles on the pre-planned path. The AGV then employs obstacle avoidance and local self-optimization, implementing a second transport path. Simultaneously, the transport path will be updated with the new locally optimized path. During transport, when there are no obstacles, the AGV robot transports goods according to the globally optimal path (the first transport path) updated by the AGV robot scheduling center.

[0135] When obstacles appear on the second transport path, the AGV robot scans the surrounding environment and uses transport tags to avoid them, acquiring a self-guided path. Following this path, the AGV robot transports the workpiece to be processed. After completion, it sends a completion status message to the production scheduling center and enters a waiting-for-task-assignment state. If the AGV robot encounters an obstacle while transporting the workpiece, it automatically changes its transport path to achieve the optimal path, saving transport time and increasing transport speed.

[0136] The second transport path includes: when the AGV robot avoids obstacles, that is, when it is performing self-pathfinding, during the local self-optimization process, the AGV robot scans the environment around the obstacle to obtain a map of the environment around the obstacle, and uses a path cost function to obtain the number of turns and the length of the path run by the AGV robot based on the position point of the AGV robot to obtain the second transport path; using the second transport path, the AGV robot passes through the obstacle, and the second transport path and time information are updated to the transport label of the workpiece to be processed.

[0137] Furthermore, let the current point be a(i)(j), and the optimal path planning point obtained by optimization to bypass the obstacle and return is a(i)(m). The locally optimal path for this segment obtained by self-optimization is shown in Table 4 below:

[0138] a(i)(j) c(i)(j + 1) c(i)(j + 2) ... a(i)(m)

[0139] Table 4

[0140] After obtaining the locally optimized path, insert the locally optimized path into a(i)(j) and a(i)(m), and update the self-optimized path flag F(i) to 1. The AGV robot completes the transportation according to the locally optimized path. At this time, the robot's transportation path becomes as shown in Table 5 below:

[0141] R(i) a(i)(1) ... a(i)(j) c(i)(j + 1) c(i)(j + 2) ... a(i)(m) ... a(i)(M) T(i) F(i) END

[0142] Table 5

[0143] Please refer to Figure 4Furthermore, the transport labels for the globally optimal transport path (i.e., the second transport path) obtained through interaction between the AGV robot and the AGV robot scheduling center are shown in Table 6 below:

[0144] R(i) (3，10) (7，10) (7，14) T(i) F(i) END

[0145] Table 6

[0146] The implementation example uses a method of recording inflection points. The current point is (3, 10), and the optimal path planning point is (7, 14). For the globally optimal path planning for this segment, please refer to [reference needed]. Figure 4 The black path in the image; when encountering obstacles, please refer to... Figure 7 The black circle at (4.5, 10) represents an obstacle. To avoid the obstacle, the locally optimal path for this segment, determined by self-optimization, is shown in Table 7 below:

[0147] R(i) (3，10) (3，11) (7，10) (7，11) (7，14) T(i) F(i) END

[0148] Table 7

[0149] Starting from the current position (3, 10), a self-finding path is adopted. When the path reaches (7, 11), it returns to the global optimal path. The red path is the self-finding path, and the black path is a part of the original global optimal path. While achieving obstacle avoidance, the path can be corrected in time and returned to the global optimal path, which is the second transport path.

[0150] Please refer to Figure 5 Furthermore, the globally optimal path labels for this transfer between processing stations 1 and 4, obtained through interaction between the AGV robot and the AGV robot scheduling center, are shown in Table 8 below:

[0151] R(i) (7，14) (7，9) (15，9) (15，7) T(i) F(i) END

[0152] Table 8

[0153] The current position is (7, 14), and the optimal path planning point is (15, 7). The globally optimal path for this segment is as follows: Figure 5 As shown in the black path; when moving materials between workstations 1 and 4, if an obstacle is encountered, such as... Figure 8 As shown, the black circle at (13, 9) represents an obstacle. To avoid the obstacle, the locally optimal path for this segment, which is determined by self-optimization, is shown in Table 9 below:

[0154] R(i) (7，14) (7，9) (12，9) (12，8) (15，8) (15，7) T(i) F(i) END

[0155] Table 9

[0156] Starting from (12, 9), a self-finding path is adopted. When the path reaches (15, 8), it returns to the globally optimal path. The red path is the self-finding path, and the black path is part of the original globally optimal path. While achieving obstacle avoidance, the path can be corrected in time and return to the globally optimal path.

[0157] Please refer to Figure 6 Furthermore, the global optimal path labels for processing station 4 and the finished product area, obtained through interaction between the AGV robot and the AGV robot scheduling center, are shown in Table 10 below:

[0158] R(i) (15，7) (15，10) (22，10) T(i) F(i) END

[0159] Table 10

[0160] The current point is (15, 7), and the optimal path planning point is (22, 10). The globally optimal path for this segment is as follows: Figure 6 As shown in the black path; when moving materials between workstation 4 and the finished product area, if obstacles are encountered, such as... Figure 9 As shown, the black circle at (15, 10) represents an obstacle. To avoid the obstacle, the locally optimal path for this segment, which is determined by self-optimization, is shown in Table 11 below:

[0161] R(i) (15，7) (15，9) (16，9) (16，10) (22，10) T(i) F(i) END

[0162] Table 11

[0163] Starting from (15, 9), a self-finding path is adopted. When the path reaches (16, 10), it returns to the globally optimal path. The red path is the self-finding path, and the black path is part of the original globally optimal path. While achieving obstacle avoidance, the path can be corrected in time and returned to the globally optimal path, which is the second transport path.

[0164] Furthermore, during the transport process, if the robot encounters an obstacle, the AGV robot will autonomously scan the workshop environment and use the transport tags to perform obstacle avoidance local path optimization to obtain a locally optimized path, i.e., a second transport path. The transport will then proceed according to the globally optimal path incorporating the locally optimized path. In the obstacle avoidance local self-optimization process after the AGV robot encounters an obstacle, a path cost function is used, similar to the globally optimal path planning. During the obstacle avoidance local self-optimization process, the number of turns 'a' and the path length 'b' are no longer fixed from the starting point to the ending point, but rather represent the number of turns and the path length from the current point back to the globally optimal path planning point. Here, the globally optimal path planning point can be any globally optimal path planning point after the current point.

[0165] After the AGV robot bypasses the obstacle, it inserts this locally optimized path between the current point and the obtained globally optimal path point, and updates the planned globally optimal path.

[0166] Furthermore, after completing the handling task assigned by the AGV robot scheduling center, the AGV robot sends a completion status message to the AGV robot scheduling center, and then enters an idle state waiting for task assignment.

[0167] Storing processing tags and handling tags in the data resource pool includes:

[0168] Once the parts to be processed are finished, they enter the finished product area. By scanning the processing label and handling label of the parts to be processed, the processing information and handling information of the parts to be processed are obtained, and the processing information and handling information are uploaded to the production scheduling center, which then uploads them to the data resource pool.

[0169] Furthermore, the transport path for each transfer is added to the workpiece's transport label, resulting in the complete transport label as shown in Table 12 below:

[0170] R(1) a(1)(1) ... a(1)(j) ... a(1)(M) T(1) F(1) END ... ... ... ... ... ... ... R(i) a(i)(1) ... a(i)(j) ... a(i)(M) T(i) F(i) END R(N) a(N)(1) ... a(N)(j) ... a(N)(M) T(N) F(N) END

[0171] Table 12

[0172] Furthermore, after the workpiece is processed, the finished product area scans the processing label and handling label of the workpiece to obtain the processing information and handling information of the workpiece, and uploads the information to the production scheduling center. The production scheduling center then uploads it to the data resource pool. Finally, the data uploaded to the data resource pool includes the processing steps, processing stations and processing time in the processing label, as well as the handling labels updated according to the steps.

[0173] Furthermore, the processing tracking information stored in the data resource pool is used for subsequent production process analysis. By analyzing the actual processing time of each processing step and the transportation time between different processing steps, the processing and transportation processes can be optimized, thereby further improving the production efficiency of the manufacturing system.

[0174] It will be readily understood by those skilled in the art that the aforementioned advantageous methods can be freely combined and superimposed without conflict.

Claims

1. A method for improving the processing efficiency of an intelligent manufacturing system based on tags, characterized in that, The intelligent manufacturing system includes a data resource pool, a production scheduling center, an AGV robot scheduling center, AGV robots, processing stations, a waiting area, and a finished product area. The data resource pool is equipped with tags. The labels include processing labels and handling labels. The processing labels include the processing sequence, processing steps, processing stations, and processing time for each part to be processed. They are used to track the processing of each step of the part to be processed at different processing stations. After the part to be processed is labeled with a processing label, it enters the processing area. The transport tags are used by AGV robots to transport materials between different locations in the raw material area, processing area, and finished product area according to the planned path and the self-finding path. The processing labels and handling labels are continuously updated based on the processing and handling of the parts to be processed, to obtain information on the processing and handling process of the parts to be processed; Processing labels and handling labels are used throughout the entire processing and handling process of each workpiece, ensuring that the workpiece is processed according to the preferred procedure and transported along the preferred path; The method includes: Retrieve order information from the data resource pool; The production scheduling center obtains the processing steps and processing time of the parts to be processed based on the order information, sorts them according to the processing steps and processing time at the processing stations in the workshop, obtains the processing steps and processing time of the parts to be processed, assigns a processing sequence number to the parts to be processed, and processes them according to the processing sequence number. The processing sequence, processing steps, and processing stations in the processing label are fixed values, while the processing time is continuously updated based on the processing time of each processing station. The processing label is scanned at the processing station to obtain the incomplete processing steps of the workpiece; Based on the workshop environment map stored in the AGV robot scheduling center, obtain the handling tags of the parts to be processed; Based on the handling tags, the AGV robot scheduling center obtains the instructions for the AGV robot to handle the workpieces to be processed, and the workpieces to be processed are handled according to the processing steps. The entire processing of the workpiece is carried out by AGV robots until it is moved to the finished product area. Processing labels and handling labels are stored in a data resource pool; The processing tracking information stored in the data resource pool is used for later production process analysis. By analyzing the actual processing time of each processing step and the transportation time between different processing steps, the processing and transportation processes can be optimized to further improve the production efficiency of the manufacturing system. Based on the handling tags, the instructions from the AGV robot scheduling center to dispatch AGV robots to handle workpieces include: The AGV robot scheduling center accepts two types of requests: the first type is a handling request sent by the production scheduling center according to the processing steps of the workpiece to be processed and the planned processing start time; the second type is a handling request issued based on the completion of processing at the processing station. The AGV robot scheduling center queues the handling requests and schedules the AGV robots to handle the tasks according to the request time and the location of the AGV robots. The entire processing of the workpieces using AGV robots, up to and including the transfer of the workpieces to the finished product area, includes: The handling path for all processing steps of the AGV robot handling the workpiece includes: When the first transport path is unobstructed, the AGV robot transports the workpiece according to the transport tag sent by the AGV robot scheduling center. After the transport is completed, it sends the transport completion status information to the production scheduling center and enters the non-working state of waiting for task assignment. Furthermore, to optimize the globally optimal paths between each processing station, between the waiting area and each processing station, and between the finished product area and each processing station, a grid model is established for the workshop environment scanned by the AGV robot. The coordinates of each grid are labeled, and transport labels for the transport paths are defined. The waiting area is defined as W, the processing station as S(i), and the processing station numbers are i = 1, 2, 3, ..., n. The finished product area is defined as P. In the optimal path planning, the path cost is the objective of path optimization, where the path cost value is: ； in, Value for the path To determine the weighting of the number of turns, The weights are for path length. The number of turns from the starting point to the end point. This represents the path length from the starting point to the ending point. When there are obstacles in the second transport path, the AGV robot scans the environment around the obstacle and uses the transport tag to avoid the obstacle and obtain a self-searching path. According to the self-searching path, the AGV robot completes the transport of the workpiece to be processed. After the transport is completed, it sends the transport completion status information to the production scheduling center and enters the non-working state of waiting for task assignment. The second transport path includes: When the AGV robot avoids obstacles, it scans the surrounding environment of the obstacle to obtain a map of the surrounding environment. Using a path cost function, based on the position of the AGV robot, it obtains the number of turns and the length of the path traveled by the AGV robot to obtain a second transport path. Using the second transport path, the AGV robot passes through obstacles and updates the second transport path to the transport label of the workpiece to be processed; After the AGV robot bypasses the obstacle, it inserts this locally optimized path between the current point and the obtained globally optimal path point, and updates the planned globally optimal path. Each transport path is added to the transport label of the workpiece to be processed, the transport label is updated, and finally a complete transport label is formed, and the entire transport process is tracked.

2. The method for improving the processing efficiency of an intelligent manufacturing system based on tags according to claim 1, characterized in that, By scanning the processing label at the processing station, the incomplete processing steps of the workpiece can be identified, including: The workpiece to be processed is moved from the waiting area to the processing station. The processing time of the workpiece is obtained by scanning the processing label, and it is determined whether the processing step is completed. If processing time exists, the processing procedure at this workstation is completed. If no processing time exists, the processing procedure at this workstation is completed according to the processing sequence. After processing is completed, the processing time of the processing procedure at this workstation is added to the processing label, and the processing procedure of the workpiece at this workstation is sent to the AGV robot scheduling center.

3. The method for improving the processing efficiency of an intelligent manufacturing system based on tags according to claim 1, characterized in that, Based on the workshop environment map stored in the AGV robot scheduling center, the handling tags of the parts to be processed are obtained, including: Based on the AGV robot scanning the workshop environment, the location information of all processing stations, waiting areas, finished product areas and obstacles in the workshop is obtained; Based on the acquired location information, a workshop environment map is created, and all processing stations, waiting areas, finished product areas, and obstacles are marked on the map; Based on the workshop environment map, the paths between processing stations, between the waiting area and the work station, and between the finished product area and the processing station are updated to obtain the actual handling paths, which are then stored in the AGV robot scheduling center. Based on the actual handling path obtained, a grid model is established by scanning the workshop environment with an AGV robot, the coordinates of each grid are marked, and the path cost function is obtained.

4. The method for improving the processing efficiency of an intelligent manufacturing system based on tags according to claim 1, characterized in that, The first type of instruction sent by the AGV robot dispatch center includes: AGV robots are used to transport the workpieces from the processing area to the processing station corresponding to the first processing step until the workpieces are transported to the finished product area. The transport path for each transport is added to the transport tag of the workpiece, allowing for full tracking of the entire transport process. The second type of instruction sent by the AGV robot dispatch center includes: Locate any idle AGV robots around the processing station. Based on the application time and the location of available AGV robots, compare the distance between the processing station and the idle AGV robots. Assign AGV robots to handle tasks based on the distance. AGV robots that are in operation do not accept task assignments.

5. The method for improving the processing efficiency of an intelligent manufacturing system based on tags according to claim 1, characterized in that, The AGV robot runs to the assigned processing station or waiting area, scans the processing label on the workpiece to obtain the completed processing steps and the next processing step of the workpiece, and obtains the processing station for transporting the workpiece. The AGV robot interacts with the AGV robot scheduling center. The AGV robot scheduling center obtains the first transport path based on the AGV robot's position and the processing station where the workpiece is to be transported, and sends the first transport path to the AGV robot. The AGV robot completes the transport of the workpiece and updates the first path and time information to the transport tag of the workpiece.

6. The method for improving the processing efficiency of an intelligent manufacturing system based on tags according to claim 1, characterized in that, Storing processing tags and handling tags in the data resource pool includes: after the workpiece is processed and enters the finished product area, the processing and handling information of the workpiece is obtained by scanning the processing and handling tags of the workpiece, and the processing and handling information is uploaded to the production scheduling center, which then uploads it to the data resource pool.

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