Dynamic scheduling method, device, equipment and storage medium for robot path optimization

Abstract

This invention discloses a method, apparatus, device, and storage medium for dynamic scheduling of robotic arm path optimization. The method calculates the product gap data of the current production line in real time; determines the picking and delivery priority of the robotic arm based on the product gap data; controls the robotic arm to pick and deliver products to the optimized equipment based on the picking and delivery priority; when an equipment abnormality is detected in the current production line, the abnormality status is reported to the human-machine interface (HMI); after receiving a confirmation instruction, equipment repair and gap compensation operations are performed; and after receiving a dynamic scheduling instruction, the current production line is dynamically scheduled. This method can maximize equipment utilization and output while minimizing production costs, reducing the production cost of the production line, avoiding frequent back-and-forth movements of the robotic arm between processes, improving the efficiency of the robotic arm, effectively controlling the production line rhythm, and eliminating the need for manual intervention when equipment is abnormal, thereby improving the production speed and efficiency of the products.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing technology, and in particular to a method, apparatus, equipment, and storage medium for dynamic scheduling of robot path optimization. Background Technology

[0002] Traditional production line product manufacturing solutions typically employ one of the following two approaches:

[0003] 1. Fixed process flow processing: The robot arm completes all the processing steps of the product before starting the production of the next piece.

[0004] 2. Partial Priority Response: The first process is preset to have the highest priority, and subsequent processes are handled according to the principle of proximity.

[0005] It has the following defects:

[0006] ① Automated production lines are expensive and lack professional maintenance personnel.

[0007] ② The robotic arm frequently turns back and forth between processes, travels long distances, is inefficient, and has path redundancy.

[0008] ③ The production ratio of primary and secondary products is prone to deviate from the preset target, and the production rhythm is difficult to synchronize.

[0009] ④ Manual intervention is required when the equipment malfunctions, resulting in long downtime. Summary of the Invention

[0010] The main objective of this invention is to provide a method, device, equipment, and storage medium for dynamic scheduling of robot path optimization, aiming to solve the technical problems in existing automated production lines for product manufacturing, such as high cost, lack of professional maintenance personnel, frequent back-and-forth movement of robots between different processes, long travel distances, low efficiency, path redundancy, easy deviation of the master-slave product production ratio from the predetermined target, difficulty in controlling the production rhythm, and long downtime when equipment malfunctions.

[0011] In a first aspect, the present invention provides a dynamic scheduling method for optimizing the path of a robotic arm, the method comprising the following steps:

[0012] Real-time calculation of product shortage data for the current production line;

[0013] The robot's picking and delivering priority is determined based on the product gap data, and the robot is controlled to pick and deliver master and slave products to the layout-optimized equipment based on the picking and delivering priority.

[0014] When an equipment malfunction is detected on the current production line, the abnormal status is reported to the human-machine interface (HMI). After receiving a confirmation instruction, equipment repair and gap compensation operations are performed. After receiving a dynamic scheduling instruction, the current production line is dynamically scheduled.

[0015] Optionally, the real-time calculation of the current production line's product shortage data includes:

[0016] Obtain the count data of the product counter, calculate the deviation of the master and slave products on the current production line based on the count data, and determine the product gap data based on the deviation.

[0017] Optionally, the step of acquiring the count data of the product counter, calculating the deviation between the master and slave products on the current production line based on the count data, and determining the equipment gap data based on the deviation includes:

[0018] Obtain the count data from the product counter, and from the count data, obtain the number of main products completed and the number of slave products completed on the current production line;

[0019] The deviation between the master and slave products is calculated using the following formula based on the number of master products completed, the number of slave products completed, and the preset master-slave target ratio:

[0020]

[0021] in, This is the deviation amount. Number of main products completed. To calculate the number of products completed, To preset the master-slave target ratio, , Target number for main products To determine the target number of products;

[0022] The deviation is used as the product shortage data for the current production line.

[0023] Optionally, determining the robot's pick-up and delivery priority based on the product gap data, and controlling the robot to perform master-slave product pick-up and delivery on the layout-optimized equipment according to the pick-up and delivery priority, includes:

[0024] Obtain the equipment status signal of the current production line, generate each task to be processed corresponding to the equipment status signal according to the preset task generator, and generate a task pool to be processed.

[0025] The product ratio is determined based on the product gap data, and the path efficiency and blocking factor of each task in the task pool to be processed are obtained.

[0026] The priority score of each task is determined based on the product ratio, the path efficiency, and the congestion factor.

[0027] The priority of the robot arm is determined based on the priority score, and the robot arm is controlled to perform master-slave product retrieval and delivery on the optimized equipment based on the retrieval and delivery priority.

[0028] Optionally, determining the priority score of each task based on the product ratio, the path efficiency, and the congestion factor includes:

[0029] The priority score for each task is determined based on the product ratio, the path efficiency, and the congestion factor using the following formula:

[0030]

[0031] in, Priority score, For product proportion weight, For product proportions, For path efficiency weights, For path efficiency, For the blocking factor weight, It is a blocking factor.

[0032] Optionally, determining the robot's pick-up and delivery priority based on the priority score, and controlling the robot to perform master-slave product pick-up and delivery on the layout-optimized equipment according to the pick-up and delivery priority, includes:

[0033] Based on the priority score, each task in the task pool is sorted in descending order of its score. For tasks with the same priority score, those with the closest path are given priority, and a pick-up and delivery priority is generated.

[0034] Obtain the main product production process and the slave product production process of the current production line, and determine the equipment to be configured based on the main product production process and the slave product production process;

[0035] Optimize the layout of equipment of the same size and matching production processes in each configuration, and move the corresponding equipment according to the optimized layout to form a layout-paired equipment group. The equipment in the layout-paired equipment group is divided into idle equipment to be placed, equipment in the process, and equipment to be picked up.

[0036] According to the pick-up and delivery priority, the corresponding robot is selected from the robot execution queue to place the master and slave products to be processed on the idle waiting placement device, and the processed master and slave products are taken away from the completed waiting retrieval device. The pick-up and delivery priority is updated in real time after the task is completed.

[0037] Optionally, when an equipment malfunction is detected on the current production line, the abnormal status is reported to the human-machine interface (HMI). After receiving a confirmation instruction, equipment repair and gap compensation operations are performed. Upon receiving a dynamic scheduling instruction, the current production line is dynamically scheduled, including:

[0038] When an equipment malfunction is detected on the current production line, the abnormal status is reported to the human-machine interface (HMI).

[0039] Upon receiving the confirmation command, the maintenance mode is activated, the faulty equipment is controlled to be removed from the workstation along the maintenance track, and the equipment matching gap is calculated in real time. The picking and delivery priority is updated according to the matching gap, and the gap is compensated according to the updated picking and delivery priority.

[0040] Upon receiving a dynamic scheduling instruction, the production plan for the current production line is regenerated according to the dynamic scheduling instruction, and the product shortage data is recalculated.

[0041] When the preset production time is detected, the feeding of raw materials is stopped, and the robotic arm is controlled to complete the transfer of all products on the current production line.

[0042] Secondly, to achieve the above objectives, the present invention also proposes a dynamic scheduling device for optimizing the path of a robotic arm, the dynamic scheduling device for optimizing the path of a robotic arm comprising:

[0043] The data calculation module is used to calculate the product shortage data of the current production line in real time;

[0044] The product retrieval module is used to determine the retrieval priority of the robot arm based on the product gap data, and control the robot arm to perform master-slave product retrieval and delivery on the layout-optimized equipment according to the retrieval priority;

[0045] The maintenance compensation module is used to report the abnormal status to the human-machine interface (HMI) when an equipment abnormality is detected in the current production line. After receiving a confirmation instruction, it performs equipment maintenance and gap compensation operations, and after receiving a dynamic scheduling instruction, it performs dynamic scheduling of the current production line.

[0046] Thirdly, to achieve the above objectives, the present invention also proposes a dynamic scheduling device for optimizing robot paths. The dynamic scheduling device for optimizing robot paths includes: a memory, a processor, and a dynamic scheduling program for optimizing robot paths stored in the memory and executable on the processor. The dynamic scheduling program for optimizing robot paths is configured to implement the steps of the dynamic scheduling method for optimizing robot paths as described above.

[0047] Fourthly, to achieve the above objectives, the present invention also proposes a storage medium storing a dynamic scheduling program for manipulator path optimization, wherein the dynamic scheduling program for manipulator path optimization, when executed by a processor, implements the steps of the dynamic scheduling method for manipulator path optimization as described above.

[0048] The proposed robotic arm path optimization dynamic scheduling method calculates the product gap data of the current production line in real time; determines the robotic arm's pick-up and delivery priority based on the product gap data; controls the robotic arm to pick up and deliver master and slave products to the optimized equipment according to the pick-up and delivery priority; when an equipment abnormality is detected in the current production line, the abnormality status is reported to the human-machine interface (HMI); after receiving a confirmation instruction, equipment repair and gap compensation operations are performed; and after receiving a dynamic scheduling instruction, the current production line is dynamically scheduled. By optimizing equipment layout, using a real-time dynamic scheduling algorithm, and a fault adaptive mechanism, this method maximizes equipment utilization and output while minimizing production costs, reduces the production cost of the production line, avoids frequent back-and-forth between processes, improves the efficiency of the robotic arm, effectively controls the production line's production rhythm, eliminates the need for manual intervention when equipment is abnormal, improves the utilization rate of production line equipment, realizes the production of master and slave products in a balanced manner, reduces the technical cost and system complexity of the automation line, and improves the production speed and efficiency of products. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of the present invention;

[0050] Figure 2 This is a flowchart illustrating the first embodiment of the dynamic scheduling method for manipulator path optimization of the present invention;

[0051] Figure 3 This is a flowchart illustrating the second embodiment of the dynamic scheduling method for manipulator path optimization of the present invention;

[0052] Figure 4 This is a flowchart illustrating the third embodiment of the dynamic scheduling method for manipulator path optimization of the present invention;

[0053] Figure 5 This is a schematic diagram of the dynamic scheduling algorithm logic in the dynamic scheduling method for manipulator path optimization of the present invention;

[0054] Figure 6 This is a schematic diagram of the robot arm action sequence in the robot arm path optimization dynamic scheduling method of the present invention;

[0055] Figure 7 This is a schematic diagram of the proportional control logic in the dynamic scheduling method for manipulator path optimization of the present invention;

[0056] Figure 8 This is a schematic diagram of a stable production process in the dynamic scheduling method for manipulator path optimization of the present invention;

[0057] Figure 9 This is a schematic diagram of equipment state transitions in the dynamic scheduling method for manipulator path optimization of the present invention;

[0058] Figure 10 This is a flowchart illustrating the fourth embodiment of the dynamic scheduling method for manipulator path optimization of the present invention;

[0059] Figure 11 This is a schematic diagram of the fault recovery mechanism in the dynamic scheduling method for manipulator path optimization of the present invention;

[0060] Figure 12 This is a schematic diagram of the human-machine interface in the dynamic scheduling method for manipulator path optimization of the present invention;

[0061] Figure 13 This is a functional block diagram of the first embodiment of the dynamic scheduling device for optimizing the path of the robotic arm according to the present invention.

[0062] 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

[0063] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0064] The solution of this invention mainly involves: calculating the product gap data of the current production line in real time; determining the picking and delivery priority of the robotic arm based on the product gap data; controlling the robotic arm to pick and deliver products to the optimized equipment based on the picking and delivery priority; when an equipment abnormality is detected on the current production line, reporting the abnormality to the human-machine interface (HMI); performing equipment repair and gap compensation operations after receiving a confirmation instruction; and dynamically scheduling the current production line after receiving a dynamic scheduling instruction. Through optimized equipment layout, real-time dynamic scheduling algorithms, and fault adaptive mechanisms, this approach maximizes equipment utilization and output while minimizing production costs. Miniaturization reduces production costs on the production line, avoids frequent back-and-forth movements of robotic arms between processes, improves robotic arm efficiency, effectively controls production line rhythm, eliminates the need for manual intervention in case of equipment malfunction, enhances production line equipment utilization, enables the production of master and slave products in a proportional manner, reduces the technical cost and system complexity of automated lines, and improves product production speed and efficiency. It solves the technical problems of existing automated production lines, such as high cost, lack of professional maintenance personnel, frequent back-and-forth movements of robotic arms between different processes, long travel distances, low efficiency, path redundancy, easy deviation of master and slave product production ratio from the predetermined target, difficulty in controlling production rhythm, and long downtime when equipment malfunctions.

[0065] Reference Figure 1 , Figure 1 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of the present invention.

[0066] like Figure 1As shown, the device may include: a processor 1001, such as a CPU; a communication bus 1002; a user interface 1003; a network interface 1004; and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0067] Those skilled in the art will understand that Figure 1 The device structure shown does not constitute a limitation on the device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0068] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating device, a network communication module, a user interface module, and a dynamic scheduling program for optimizing the robot's path.

[0069] The device of this invention calls the robotic arm path optimization dynamic scheduling program stored in the memory 1005 through the processor 1001, and performs the following operations:

[0070] Real-time calculation of product shortage data for the current production line;

[0071] The robot's picking and delivering priority is determined based on the product gap data, and the robot is controlled to pick and deliver master and slave products to the layout-optimized equipment based on the picking and delivering priority.

[0072] When an equipment malfunction is detected on the current production line, the abnormal status is reported to the human-machine interface (HMI). After receiving a confirmation instruction, equipment repair and gap compensation operations are performed. After receiving a dynamic scheduling instruction, the current production line is dynamically scheduled.

[0073] The device of the present invention, through processor 1001 calling the robotic arm path optimization dynamic scheduling program stored in memory 1005, also performs the following operations:

[0074] Obtain the count data of the product counter, calculate the deviation of the master and slave products on the current production line based on the count data, and determine the product gap data based on the deviation.

[0075] The device of the present invention, through processor 1001 calling the robotic arm path optimization dynamic scheduling program stored in memory 1005, also performs the following operations:

[0076] Obtain the count data from the product counter, and from the count data, obtain the number of main products completed and the number of slave products completed on the current production line;

[0077] The deviation between the master and slave products is calculated using the following formula based on the number of master products completed, the number of slave products completed, and the preset master-slave target ratio:

[0078]

[0079] in, This is the deviation amount. Number of main products completed. To calculate the number of products completed, To preset the master-slave target ratio, , Target number for main products To determine the target number of products;

[0080] The deviation is used as the product shortage data for the current production line.

[0081] The device of the present invention, through processor 1001 calling the robotic arm path optimization dynamic scheduling program stored in memory 1005, also performs the following operations:

[0082] Obtain the equipment status signal of the current production line, generate each task to be processed corresponding to the equipment status signal according to the preset task generator, and generate a task pool to be processed.

[0083] The product ratio is determined based on the product gap data, and the path efficiency and blocking factor of each task in the task pool to be processed are obtained.

[0084] The priority score of each task is determined based on the product ratio, the path efficiency, and the congestion factor.

[0085] The priority of the robot arm is determined based on the priority score, and the robot arm is controlled to perform master-slave product retrieval and delivery on the optimized equipment based on the retrieval and delivery priority.

[0086] The device of the present invention, through processor 1001 calling the robotic arm path optimization dynamic scheduling program stored in memory 1005, also performs the following operations:

[0087] The priority score for each task is determined based on the product ratio, the path efficiency, and the congestion factor using the following formula:

[0088]

[0089] in, Priority score, For product proportion weight, For product proportions, For path efficiency weights, For path efficiency, For the blocking factor weight, It is a blocking factor.

[0090] The device of the present invention, through processor 1001 calling the robotic arm path optimization dynamic scheduling program stored in memory 1005, also performs the following operations:

[0091] Based on the priority score, each task in the task pool is sorted in descending order of its score. For tasks with the same priority score, those with the closest path are given priority, and a pick-up and delivery priority is generated.

[0092] Obtain the main product production process and the slave product production process of the current production line, and determine the equipment to be configured based on the main product production process and the slave product production process;

[0093] Optimize the layout of equipment of the same size and matching production processes in each configuration, and move the corresponding equipment according to the optimized layout to form a layout-paired equipment group. The equipment in the layout-paired equipment group is divided into idle equipment to be placed, equipment in the process, and equipment to be picked up.

[0094] According to the pick-up and delivery priority, the corresponding robot is selected from the robot execution queue to place the master and slave products to be processed on the idle waiting placement device, and the processed master and slave products are taken away from the completed waiting retrieval device. The pick-up and delivery priority is updated in real time after the task is completed.

[0095] The device of the present invention, through processor 1001 calling the robotic arm path optimization dynamic scheduling program stored in memory 1005, also performs the following operations:

[0096] When an equipment malfunction is detected on the current production line, the abnormal status is reported to the human-machine interface (HMI).

[0097] Upon receiving the confirmation command, the maintenance mode is activated, the faulty equipment is controlled to be removed from the workstation along the maintenance track, and the equipment matching gap is calculated in real time. The picking and delivery priority is updated according to the matching gap, and the gap is compensated according to the updated picking and delivery priority.

[0098] Upon receiving a dynamic scheduling instruction, the production plan for the current production line is regenerated according to the dynamic scheduling instruction, and the product shortage data is recalculated.

[0099] When the preset production time is detected, the feeding of raw materials is stopped, and the robotic arm is controlled to complete the transfer of all products on the current production line.

[0100] This embodiment, through the above-described scheme, calculates the product gap data of the current production line in real time; determines the picking and delivery priority of the robot arm based on the product gap data, and controls the robot arm to pick and deliver master and slave products to the optimized equipment according to the picking and delivery priority; when an equipment abnormality is detected in the current production line, the abnormal status is reported to the human-machine interface (HMI); after receiving a confirmation instruction, equipment repair and gap compensation operations are performed; and after receiving a dynamic scheduling instruction, the current production line is dynamically scheduled. By optimizing equipment layout, using real-time dynamic scheduling algorithms, and fault adaptive mechanisms, the system maximizes equipment utilization and output while minimizing production costs, reduces the production cost of the production line, avoids frequent back-and-forth movements of the robot arm between processes, improves the efficiency of the robot arm, effectively controls the production line rhythm, eliminates the need for manual intervention when equipment is abnormal, improves the utilization rate of production line equipment, realizes the production of master and slave products in proportion, reduces the technical cost and system complexity of the automation line, and improves the production speed and efficiency of products.

[0101] Based on the above hardware structure, an embodiment of the dynamic scheduling method for manipulator path optimization of the present invention is proposed.

[0102] Reference Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the dynamic scheduling method for manipulator path optimization of the present invention.

[0103] In the first embodiment, the dynamic scheduling method for optimizing the robot path includes the following steps:

[0104] Step S10: Calculate the product shortage data of the current production line in real time.

[0105] It should be noted that the gap data of the products required on the current production line can be obtained through calculation, that is, the gap between the number of completed products and the established target.

[0106] Step S20: Determine the picking and delivery priority of the robot arm based on the product gap data, and control the robot arm to pick and deliver master and slave products to the layout-optimized equipment according to the picking and delivery priority.

[0107] It should be understood that the priority of the robotic arm in picking up and placing products is determined based on the product gap data, and the robotic arm can be controlled to perform main product and slave product picking operations on the optimized equipment based on the picking and placing priority.

[0108] Step S30: When an equipment malfunction is detected in the current production line, the abnormal status is reported to the human-machine interface (HMI). After receiving the confirmation instruction, equipment repair and gap compensation operations are performed. After receiving the dynamic scheduling instruction, the current production line is dynamically scheduled.

[0109] It is understandable that when an equipment malfunction is detected on the current production line, the malfunction status can be reported to the Human-Machine Interface (HMI). After receiving a confirmation instruction generated by the user through the HMI, the equipment repair and gap compensation operations are performed according to the confirmation instruction. After receiving a dynamic scheduling instruction, the current production line is dynamically scheduled according to the dynamic scheduling instruction.

[0110] This embodiment, through the above-described scheme, calculates the product gap data of the current production line in real time; determines the picking and delivery priority of the robot arm based on the product gap data, and controls the robot arm to pick and deliver master and slave products to the optimized equipment according to the picking and delivery priority; when an equipment abnormality is detected in the current production line, the abnormal status is reported to the human-machine interface (HMI); after receiving a confirmation instruction, equipment repair and gap compensation operations are performed; and after receiving a dynamic scheduling instruction, the current production line is dynamically scheduled. By optimizing equipment layout, using real-time dynamic scheduling algorithms, and fault adaptive mechanisms, the system maximizes equipment utilization and output while minimizing production costs, reduces the production cost of the production line, avoids frequent back-and-forth movements of the robot arm between processes, improves the efficiency of the robot arm, effectively controls the production line rhythm, eliminates the need for manual intervention when equipment is abnormal, improves the utilization rate of production line equipment, realizes the production of master and slave products in proportion, reduces the technical cost and system complexity of the automation line, and improves the production speed and efficiency of products.

[0111] Furthermore, Figure 3 This is a flowchart illustrating the second embodiment of the dynamic scheduling method for manipulator path optimization of the present invention, as shown below. Figure 3 As shown, based on the first embodiment, a second embodiment of the dynamic scheduling method for manipulator path optimization of the present invention is proposed. In this embodiment, step S10 specifically includes the following steps:

[0112] Step S11: Obtain the counting data of the product counter, calculate the deviation of the master and slave products of the current production line based on the counting data, and determine the product gap data based on the deviation.

[0113] It should be noted that the product counter is a counting device for counting the number of products completed. After obtaining the counting data of the product counter, the deviation between the master and slave products of the current production line can be calculated based on the counting data, that is, the deviation between the current completed quantity of the master and slave products and the predetermined target quantity. The product gap data can be determined based on the deviation.

[0114] Furthermore, step S11 specifically includes the following steps:

[0115] Obtain the count data from the product counter, and from the count data, obtain the number of main products completed and the number of slave products completed on the current production line;

[0116] The deviation between the master and slave products is calculated using the following formula based on the number of master products completed, the number of slave products completed, and the preset master-slave target ratio:

[0117]

[0118] in, This is the deviation amount. Number of main products completed. To calculate the number of products completed, To preset the master-slave target ratio, , Target number for main products To determine the target number of products;

[0119] The deviation is used as the product shortage data for the current production line.

[0120] It is understood that the number of main products completed and the number of slave products completed on the current production line are obtained from the counting data; the deviation between the main and slave products can be calculated using the above formula based on the number of main products completed, the number of slave products completed, and the preset master-slave target ratio, and then the deviation is used as the product gap data of the current production line.

[0121] This embodiment, through the above-described scheme, obtains the counting data of the product counter, calculates the deviation between the master and slave products on the current production line based on the counting data, and determines the product gap data based on the deviation data. This enables accurate acquisition of product gap data, facilitating subsequent dynamic scheduling and improving the speed and efficiency of dynamic scheduling for robot path optimization.

[0122] Furthermore, Figure 4 This is a flowchart illustrating the third embodiment of the dynamic scheduling method for manipulator path optimization of the present invention, as shown below. Figure 4 As shown, based on the first embodiment, a third embodiment of the dynamic scheduling method for manipulator path optimization of the present invention is proposed. In this embodiment, step S20 specifically includes the following steps:

[0123] Step S21: Obtain the equipment status signal of the current production line, generate each task to be processed corresponding to the equipment status signal according to the preset task generator, and generate a task pool to be processed.

[0124] It should be noted that after obtaining the equipment status signal of the current production line, various tasks to be processed corresponding to the equipment status signal can be generated according to the preset task generator, and then a task pool to be processed can be generated according to each task to be processed.

[0125] Step S22: Determine the product ratio based on the product gap data, and obtain the path efficiency and blocking factor of each task in the task pool to be processed.

[0126] It is understood that the product ratio can be determined based on the product gap data, and the path efficiency and blocking factor of each task in the task pool to be processed can be obtained, that is, the efficiency of the path taken by the robot in executing each task and the factors corresponding to the relevant factors that may be blocked.

[0127] Step S23: Determine the priority score of each task based on the product ratio, the path efficiency, and the blocking factor.

[0128] It should be understood that the priority score of each task can be determined based on the product ratio, the path efficiency, and the blocking factor.

[0129] Furthermore, step S23 specifically includes the following steps:

[0130] The priority score for each task is determined based on the product ratio, the path efficiency, and the congestion factor using the following formula:

[0131]

[0132] in, Priority score, For product proportion weight, For product proportion, For path efficiency weights, For path efficiency, For the blocking factor weight, It is a blocking factor.

[0133] In practical implementation, when multiple tasks compete, a scheduling algorithm can be used to calculate priorities. =0.4, =0.4, For example, =0.2:

[0134]

[0135] Product ratio When the output of the main product lags behind =2, from the time of product surplus =0.5; Path efficiency Tasks within the same equipment group = 10 points, tasks in adjacent equipment groups = 8 points, tasks in equipment groups separated by one group = 6 points, blocking factor The target device is "idle and waiting to be placed" γ=2 points; the target device is "processing" (remaining time <30 seconds) γ=1 point; of course, other values ​​are also possible, and this embodiment does not limit them.

[0136] Step S24: Determine the picking and delivery priority of the robot arm according to the priority score, and control the robot arm to pick and deliver master and slave products to the layout-optimized equipment according to the picking and delivery priority.

[0137] It is understood that the priority score can be used to determine the picking and delivery priority of the robot arm, and the robot arm can be controlled to pick up and deliver main products and slave products to the layout-optimized equipment according to the picking and delivery priority.

[0138] Furthermore, step S24 specifically includes the following steps:

[0139] Based on the priority score, each task in the task pool is sorted in descending order of its score. For tasks with the same priority score, those with the closest path are given priority, and a pick-up and delivery priority is generated.

[0140] Obtain the main product production process and the slave product production process of the current production line, and determine the equipment to be configured based on the main product production process and the slave product production process;

[0141] Optimize the layout of equipment of the same size and matching production processes in each configuration, and move the corresponding equipment according to the optimized layout to form a layout-paired equipment group. The equipment in the layout-paired equipment group is divided into idle equipment to be placed, equipment in the process, and equipment to be picked up.

[0142] According to the pick-up and delivery priority, the corresponding robot is selected from the robot execution queue to place the master and slave products to be processed on the idle waiting placement device, and the processed master and slave products are taken away from the completed waiting retrieval device. The pick-up and delivery priority is updated in real time after the task is completed.

[0143] It should be noted that, as Figure 5 As shown, Figure 5 This is a schematic diagram of the dynamic scheduling algorithm logic in the robot path optimization dynamic scheduling method of the present invention. See [link / reference]. Figure 5After receiving the equipment status signal, a task pool to be processed can be generated through the task generator. The task generation rule is that when the equipment status is "complete and ready to be picked up", a pick task is generated; when the equipment status is "idle and ready to be placed", a place task is generated. The deviation can be calculated through the production counter. Deviation = |Master completed number - (Slave completed number / target ratio)|. When the master product lags behind, it can be marked for acceleration; when the slave product is excessive, it can be marked for deceleration. The task sorting principle is to arrange them in descending order of priority score. When the scores are the same, the closer the path, the higher the priority. The tasks are reordered after each task is completed. The robot arm executes the relevant tasks according to the robot arm execution queue. The dynamic adjustment mechanism of the ratio weight can ensure that the master and slave product completion numbers approach the target ratio.

[0144] In practice, a production line requires at least one robotic arm to produce both master and slave products. For example, the master product completes six processes (A, B, C, D, E, F) sequentially, while the slave product completes five processes (U, V, W, X, Y) sequentially. The process flow for the master and slave products cannot be skipped and must be completed in sequence.

[0145] The robotic arm can be an ABB robotic arm, or of course a KUKA robotic arm, FANUC robotic arm, YASKAWA robotic arm, SIASUN robotic arm, etc. This embodiment does not limit this. The robotic arm is used to grasp products and put them into the equipment of each process. A moving track is provided under the robotic arm.

[0146] The equipment configuration process can be as follows: two A devices and two U devices, which are the same size and placed together;

[0147] Two B and two V devices, which are the same size and are placed together;

[0148] Two C and two W devices, which are the same size and are placed together;

[0149] Two units each of devices D and X, and the devices are the same size and are placed together;

[0150] Two units each of devices E and Y, and the devices are the same size and are placed together;

[0151] There are two units of each type of equipment, and the equipment is the same size and placed together. Equipment F is only used for the main product.

[0152] like Figure 6 As shown, Figure 6 This is a schematic diagram of the robot arm action sequence in the dynamic scheduling method for robot arm path optimization of the present invention. See [link / reference]. Figure 6The robotic arm executes a continuous "pick-up" and "delivery" action according to priority. The robotic arm operation is divided into two stages. Stage 1: Picking up process. After receiving the task assigned by the control system to pick up B1 and deliver it to C1, the robotic arm first moves to the waiting position of group B (movement time T), falls to the position T2, grabs the finished product to device B1, and changes the status from completed to idle to ready for placement. The robotic arm is raised, which generally takes 2 seconds. Of course, the raising time of different robotic arms is different, and it can also be adjusted according to the actual application scenario. This embodiment does not impose any restrictions on this. Stage 2: Delivery process. The robotic arm moves to the waiting position of group C (movement time T), falls to the position T2, and places the new product to C1. The status of C1 changes from idle to in processing. The robotic arm is raised (2 seconds), the task is completed and confirmed, and the control system updates the equipment status.

[0153] like Figure 7 As shown, Figure 7 This is a schematic diagram of the proportional control logic in the dynamic scheduling method for manipulator path optimization of the present invention. See [link / reference]. Figure 7 After obtaining production data in real time, the deviation between the actual and expected ratio can be calculated, and then the deviation can be judged. If the main product is lagging behind, the priority of the main product's main task will be increased. If the secondary product is lagging behind, the frequency of secondary product deployment will be reduced. After the robot executes the new queue, the equipment status will be updated.

[0154] The movement process of the robotic arm is as follows: There is a waiting position directly above each process of the robotic arm. After the process equipment completes the processing, the robotic arm grabs the product and lifts it to the waiting position. Then, the robotic arm moves to the waiting position of the next process equipment via the ground rail, and then falls into the waiting process equipment, and the equipment begins to process the product. The robotic arm is then lifted to the waiting position again to process other process equipment responses, following the same steps.

[0155] For example, the descent time is 2 seconds, the ascent time is 2 seconds, the robot arm's movement time between adjacent processes is 2 seconds, and the intervals are accumulated.

[0156] like Figure 8 As shown, Figure 8 This is a schematic diagram of the stable production process in the dynamic scheduling method for robot path optimization of the present invention. See [link / reference] Figure 8Once the equipment finishes processing, its status changes to "Completed and Pending Pickup." After "Completed and Pending Pickup," a pickup task is generated. After the pickup task is executed, the equipment becomes idle and ready for placement. When the equipment is idle and ready for placement, a placement task is generated. After the placement task is executed, the equipment changes to "Processing." When a product is finished processing on the equipment, the robot arm doesn't immediately pick it up; the product remains in its original position, but the equipment status changes to "Completed and Pending Pickup" and sends a signal to the control system. The control system adds this task to the robot arm's task queue, and the robot arm picks up the products sequentially according to task priority (usually based on completion time) and path optimization principles. After the robot arm picks up the product, the equipment status changes to "Idle," and the equipment waits for the robot arm to place the product before starting processing. Note: When the equipment is in the "Completed and Pending Pickup" state, it cannot be used to process new products.

[0157] "Maximize initial production deployment at workstations A and U": When the system starts up, products should be deployed to all idle equipment as soon as possible; "Parallel processing": The robotic arm can continuously deploy multiple products to different equipment.

[0158] The first step is to get new products from raw materials; subsequent incoming materials come from the previous step.

[0159] After the equipment completes processing, it must wait for the robotic arm to remove the finished product before it can accept new products.

[0160] like Figure 9 As shown, Figure 9 This is a schematic diagram of the device state transition in the dynamic scheduling method for manipulator path optimization of the present invention. See [link / reference]. Figure 9 :

[0161] The device state cycle changes to:

[0162] Idle and ready to be placed → In processing → Completed and ready to be picked up → Idle and ready to be placed.

[0163] Equipment status cycle: Idle and waiting to be placed: New products can be received (after the robot arm places the product → in processing).

[0164] Processing in progress: After processing is completed → Ready to be picked up (send signal to the system).

[0165] Completed for pickup: After the robotic arm takes away the finished product, it becomes idle and ready to be put back in. The equipment status is transmitted to the signal system in real time.

[0166] Note: The equipment cannot process new products when it is in the "Completed and Ready to Pick Up" state. You must wait for the robotic arm to pick up the finished product.

[0167] This embodiment, through the above-described scheme, acquires the equipment status signal of the current production line, generates tasks corresponding to the equipment status signal according to a preset task generator, and creates a task pool; determines the product ratio based on the product gap data, and obtains the path efficiency and blocking factor of each task in the task pool; determines the priority score of each task based on the product ratio, the path efficiency, and the blocking factor; determines the robot's pick-up and delivery priority based on the priority score, and controls the robot to perform master-slave product pick-up and delivery on the optimized equipment based on the pick-up and delivery priority; by optimizing equipment layout, implementing a real-time dynamic scheduling algorithm, and a fault adaptive mechanism, it maximizes equipment utilization and output while minimizing production costs, reducing production line product costs and improving product production speed and efficiency.

[0168] Furthermore, Figure 10 This is a flowchart illustrating the fourth embodiment of the dynamic scheduling method for manipulator path optimization of the present invention, as shown below. Figure 10 As shown, based on the first embodiment, a fourth embodiment of the dynamic scheduling method for manipulator path optimization of the present invention is proposed. In this embodiment, step S30 specifically includes the following steps:

[0169] Step S31: When an equipment malfunction is detected in the current production line, the abnormal status is reported to the human-machine interface (HMI).

[0170] It should be noted that when an equipment malfunction is detected on the current production line, corresponding information can be generated and the abnormal status can be reported to the human-machine interface (HMI).

[0171] Step S32: After receiving the confirmation instruction, start the maintenance mode, control the faulty equipment to be removed from the workstation along the maintenance track, calculate the equipment matching gap in real time, update the pick-up and delivery priority according to the matching gap, and realize gap compensation according to the updated pick-up and delivery priority.

[0172] It is understandable that after receiving a confirmation command generated by the user through the HMI interface, the maintenance mode can be activated, thereby controlling the faulty equipment to be removed from the workstation along the maintenance track. The equipment matching gap can be calculated in real time, the picking and delivery priority can be updated according to the matching gap, and the gap compensation can be achieved according to the updated picking and delivery priority.

[0173] In specific implementations, such as Figure 11 As shown, Figure 11 This is a schematic diagram of the fault recovery mechanism in the dynamic scheduling method for manipulator path optimization of the present invention. (See attached diagram) Figure 11After equipment failure, confirmation is made on the HMI interface, a confirmation command is generated, maintenance mode is initiated, and the equipment is removed from the maintenance station. The faulty equipment can be controlled to be removed from the workstation along the maintenance track, and relevant production data is dynamically routed to the backup equipment. The equipment matching gap is calculated in real time. The gap can be calculated as |Main Completed Count - Slave Completed Count / 2|. Of course, other calculation methods can also be set. This embodiment does not limit this. After increasing the task priority weight, the robot can speed up the processing. The fault recovery effect, i.e., historical gap compensation, can control the deviation to ≤5%. Hot-swapping of equipment can achieve maintenance time ≤ expected.

[0174] When equipment malfunctions, the HMI interface turns red to indicate an alarm. After confirmation on the HMI interface, a confirmation command is generated to start the system maintenance mode. Once maintenance begins, the faulty equipment can be pushed out along the lateral maintenance track. Gap compensation calculates the matching gap amount in real time and increases the priority of tasks for lagging products.

[0175] Step S33: Upon receiving a dynamic scheduling instruction, regenerate the production plan for the current production line according to the dynamic scheduling instruction, and recalculate the product shortage data.

[0176] It should be understood that upon receiving a dynamic scheduling instruction, the production plan for the current production line can be regenerated based on the dynamic scheduling instruction, and the product shortage data can be recalculated.

[0177] Step S34: When the current time is detected to have reached the preset production time, stop feeding product raw materials and control the robot arm to complete the transfer of all products on the current production line.

[0178] Understandably, when the preset production time is detected, the feeding of product raw materials can be stopped, and the robotic arm can be controlled to complete the transfer of all products on the current production line.

[0179] In specific implementations, such as Figure 12 As shown, Figure 12 This is a schematic diagram of the human-machine interface in the dynamic scheduling method for manipulator path optimization of the present invention. See also: Figure 12 Human-Computer Interaction (HMI) can be divided into:

[0180] Partition 1: Device Status Matrix

[0181] Section 2: Production Monitoring Panel

[0182] Section 3: Robotic Arm Monitoring Area

[0183] Partition 4: Control Panel

[0184] Accordingly, the device status matrix can be set to a 6*2 grid, or other grids, and this embodiment does not limit this.

[0185] Color coding: Green = in process, Yellow = completed and ready for pickup, Blue = idle and ready for placement, Red = fault; other color coding can also be used, and this embodiment does not limit this.

[0186] You can also set a thumbnail option to view details by clicking.

[0187] Accordingly, the production monitoring panel may include: a master product counter, a slave product counter, a proportional dynamic bar, and a deviation over-limit warning. Of course, more or fewer modules can be set according to the actual application scenario, and this embodiment does not limit this.

[0188] Accordingly, the control panel may include: a ratio setting module, a device shielding checkbox, a scheme generation button, and a maintenance mode switch. Of course, more or fewer modules can be set according to the actual application scenario, and this embodiment does not limit this.

[0189] After clicking "Generate Solution" in the control panel, a raw material suggestion will pop up.

[0190] Accordingly, the robotic arm monitoring area may include: a ground track positioning map, a current task display, and a queue list (the first 3 tasks, but other numbers of tasks can also be set, which is not limited in this embodiment). Of course, more or fewer modules can be set according to the actual application scenario, which is not limited in this embodiment.

[0191] The four-zone HMI design enables the production line to achieve closed-loop control of "monitoring-decision-execution".

[0192] Understandably, the HMI interface allows setting target ratios (e.g., master:slave = 1:2), production time, equipment shielding status, etc.; clicking the "Solution Generation" button → the system simulates the production process → outputs raw material suggestions and capacity forecast reports; after confirming the work plan → the system begins initialization, clearing unprocessed products from the existing equipment production line → the status of all equipment on the production line is updated to "idle and ready to be put in".

[0193] In practice, during the raw material feeding stage, the robot can grab the main product from the raw material area and move it to the waiting position W1 in group 1; the robot drops into the A1 device and the A1 status changes to "processing". The operation is repeated to fill the A2, U1, and U2 devices, and the production line is in the initial start-up state. The raw material equipment is fed in, and according to the predetermined process flow, the process A / U device groups are filled first.

[0194] It should be noted that the HMI interface can continuously display the following: real-time status of all equipment (color-coded), target production / actual production deviation, and real-time position of the robotic arm; the dynamic optimization process can be such that if the deviation of the proportion of processed products exceeds the target threshold, the system will automatically increase the weight of relevant tasks. Of course, production parameters can also be modified at any time according to the production requirements issued by the workshop, and the production plan can be regenerated.

[0195] It should be understood that when the equipment reaches the set production time, the feeding of new raw materials can be stopped; the robotic arm completes the transfer of all products on the production line, and the system generates today's production report: the report includes the actual output of the main products, as well as the product ratio, production line equipment utilization rate, and fault handling statistics, thereby achieving effect evaluation.

[0196] This embodiment, through the above-described scheme, reports the abnormal status to the human-machine interface (HMI) when an equipment malfunction is detected on the current production line; upon receiving a confirmation command, it initiates a maintenance mode, controls the faulty equipment to be removed from the workstation along the maintenance track, and calculates the equipment matching shortage in real time, updates the pick-up and delivery priority based on the matching shortage, and compensates for the shortage based on the updated pick-up and delivery priority; upon receiving a dynamic scheduling command, it regenerates the production plan for the current production line according to the dynamic scheduling command and recalculates the product shortage data; when the current time reaches the preset production time, it stops feeding product raw materials and controls the robot arm to complete the transfer of all products on the current production line; by optimizing equipment layout, implementing a real-time dynamic scheduling algorithm, and a fault adaptive mechanism, it maximizes equipment utilization and output while minimizing production costs, reduces the production cost of the production line, avoids frequent back-and-forth movements of the robot arm between processes, improves the efficiency of the robot arm, effectively controls the production line's production rhythm, eliminates the need for manual intervention when equipment malfunctions, improves the utilization rate of the production line equipment, realizes the production of main products from product proportions, reduces the technical cost and system complexity of the automation line, and improves the production speed and efficiency of the products.

[0197] Accordingly, the present invention further provides a dynamic scheduling device for optimizing the path of a robotic arm.

[0198] Reference Figure 13 , Figure 13 This is a functional block diagram of the first embodiment of the dynamic scheduling device for optimizing the path of the robotic arm according to the present invention.

[0199] In a first embodiment of the robotic arm path optimization dynamic scheduling device of the present invention, the robotic arm path optimization dynamic scheduling device includes:

[0200] The data calculation module 10 is used to calculate the product shortage data of the current production line in real time.

[0201] The product retrieval module 20 is used to determine the retrieval priority of the robot arm based on the product gap data, and control the robot arm to perform master-slave product retrieval and delivery on the layout-optimized equipment according to the retrieval priority.

[0202] The maintenance compensation module 30 is used to report the abnormal status to the human-machine interface (HMI) when an equipment abnormality is detected in the current production line. After receiving a confirmation instruction, it performs equipment maintenance and gap compensation operations. After receiving a dynamic scheduling instruction, it performs dynamic scheduling of the current production line.

[0203] The data calculation module 10 is also used to acquire the counting data of the product counter, calculate the deviation of the master and slave products of the current production line based on the counting data, and determine the product gap data based on the deviation.

[0204] The data calculation module 10 is also used to acquire the counting data of the product counter, and obtain the number of main products completed and the number of slave products completed on the current production line from the counting data;

[0205] The deviation between the master and slave products is calculated using the following formula based on the number of master products completed, the number of slave products completed, and the preset master-slave target ratio:

[0206]

[0207] in, This is the deviation amount. Number of main products completed. To calculate the number of products completed, To preset the master-slave target ratio, , Target number for main products To determine the target number of products;

[0208] The deviation is used as the product shortage data for the current production line.

[0209] The product pick-up and delivery module 20 is further configured to acquire the equipment status signal of the current production line, generate each task to be processed corresponding to the equipment status signal according to a preset task generator, and generate a task pool to be processed; determine the product ratio according to the product gap data, acquire the path efficiency and blocking factor of each task in the task pool to be processed; determine the priority score of each task according to the product ratio, the path efficiency and the blocking factor; determine the pick-up and delivery priority of the robot arm according to the priority score, and control the robot arm to perform master-slave product pick-up and delivery on the layout-optimized equipment according to the pick-up and delivery priority.

[0210] The product delivery module 20 is further configured to determine the priority score of each task based on the product ratio, the path efficiency, and the congestion factor using the following formula:

[0211]

[0212] in, Priority score, For product proportion weight, For product proportions, For path efficiency weights, For path efficiency, For the blocking factor weight, It is a blocking factor.

[0213] The product retrieval module 20 is further configured to: sort the tasks in the task pool in descending order of priority score, prioritize tasks with the same priority score based on their proximity, and generate a retrieval priority; obtain the main product production process and slave product production process of the current production line, and determine the required configuration equipment based on the main product production process and slave product production process; optimize the layout of equipment of the same size and matching production processes, move the corresponding equipment according to the optimized layout to form a layout-paired equipment group, wherein the equipment in the layout-paired equipment group is divided into idle waiting-to-be-placed equipment, processing equipment, and completed waiting-to-be-retrieved equipment; select the corresponding robot from the robot execution queue according to the retrieval priority to place the main and slave products to be processed on the idle waiting-to-be-placed equipment, and retrieve the processed main and slave products on the completed waiting-to-be-retrieved equipment, and update the retrieval priority in real time after the task is completed.

[0214] The maintenance compensation module 30 is also used to report the abnormal status to the human-machine interface (HMI) when an equipment abnormality is detected on the current production line; after receiving a confirmation instruction, it starts the maintenance mode, controls the faulty equipment to be removed from the workstation along the maintenance track, and calculates the equipment matching shortage in real time, updates the pick-up and delivery priority according to the matching shortage, and realizes the shortage compensation according to the updated pick-up and delivery priority; when receiving a dynamic scheduling instruction, it regenerates the production plan of the current production line according to the dynamic scheduling instruction and recalculates the product shortage data; when it detects that the current time has reached the preset production time, it stops feeding product raw materials and controls the robot to complete the transfer of all products on the current production line.

[0215] The steps for implementing each functional module of the robot path optimization dynamic scheduling device can be referred to in the various embodiments of the robot path optimization dynamic scheduling method of the present invention, and will not be repeated here.

[0216] Furthermore, this embodiment of the invention also proposes a storage medium storing a dynamic scheduling program for manipulator path optimization. When the dynamic scheduling program for manipulator path optimization is executed by a processor, it implements the operations described in the above embodiments of the dynamic scheduling method for manipulator path optimization.

[0217] Those skilled in the art will understand that all or part of the steps in the methods described above can be implemented by a program instructing related hardware. The program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium is a computer-readable storage medium, including: USB flash drive, mobile hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and other media that can store program code.

[0218] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0219] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0220] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A dynamic scheduling method for optimizing the path of a robotic arm, characterized in that, The dynamic scheduling method for optimizing the robot path includes: Real-time calculation of product shortage data for the current production line; The robot's picking and delivering priority is determined based on the product gap data, and the robot is controlled to pick and deliver master and slave products to the layout-optimized equipment based on the picking and delivering priority. When an equipment malfunction is detected on the current production line, the abnormal status is reported to the human-machine interface (HMI). After receiving a confirmation instruction, equipment repair and gap compensation operations are performed. After receiving a dynamic scheduling instruction, the current production line is dynamically scheduled. The real-time calculation of the current production line's product shortage data includes: Obtain the count data of the product counter, calculate the deviation of the master and slave products on the current production line based on the count data, and determine the product gap data based on the deviation. The step of determining the robot's pick-up and delivery priority based on the product gap data, and controlling the robot to perform master-slave product pick-up and delivery on the optimized equipment based on the pick-up and delivery priority, includes: Obtain the equipment status signal of the current production line, generate each task to be processed corresponding to the equipment status signal according to the preset task generator, and generate a task pool to be processed. The product ratio is determined based on the product gap data, and the path efficiency and blocking factor of each task in the task pool to be processed are obtained. The priority score of each task is determined based on the product ratio, the path efficiency, and the congestion factor. The priority of the robot arm is determined based on the priority score, and the robot arm is controlled to perform master-slave product retrieval and delivery on the layout-optimized equipment based on the retrieval and delivery priority. The step of determining the robot's pick-up and delivery priority based on the priority score, and controlling the robot to perform master-slave product pick-up and delivery on the layout-optimized equipment according to the pick-up and delivery priority, includes: Based on the priority score, each task in the task pool is sorted in descending order of its score. For tasks with the same priority score, those with the closest path are given priority, and a pick-up and delivery priority is generated. Obtain the main product production process and the slave product production process of the current production line, and determine the equipment to be configured based on the main product production process and the slave product production process; Optimize the layout of equipment of the same size and matching production processes in each configuration, and move the corresponding equipment according to the optimized layout to form a layout-paired equipment group. The equipment in the layout-paired equipment group is divided into idle equipment to be placed, equipment in the process, and equipment to be picked up. According to the pick-up and delivery priority, the corresponding robot is selected from the robot execution queue to place the master and slave products to be processed on the idle waiting-to-place device, and the processed master and slave products are taken away from the completed waiting-to-retrieve device. The pick-up and delivery priority is updated in real time after the task is completed.

2. The dynamic scheduling method for optimizing robot paths as described in claim 1, characterized in that, The process of acquiring product counter count data, calculating the deviation between master and slave products on the current production line based on the count data, and determining product gap data based on the deviation includes: Obtain the count data from the product counter, and from the count data, obtain the number of main products completed and the number of slave products completed on the current production line; The deviation between the master and slave products is calculated using the following formula based on the number of master products completed, the number of slave products completed, and the preset master-slave target ratio: in, This is the deviation amount. Number of main products completed. To calculate the number of products completed, To preset the master-slave target ratio, , Target number for main products To determine the target number of products; The deviation is used as the product shortage data for the current production line.

3. The dynamic scheduling method for optimizing robot paths as described in claim 1, characterized in that, The step of determining the priority score of each task based on the product ratio, the path efficiency, and the congestion factor includes: The priority score for each task is determined based on the product ratio, the path efficiency, and the congestion factor using the following formula: in, Priority score, For product proportion weight, For product proportion, For path efficiency weights, For path efficiency, For the blocking factor weight, It is a blocking factor.

4. The dynamic scheduling method for optimizing robot paths as described in claim 1, characterized in that, When an equipment malfunction is detected on the current production line, the abnormal status is reported to the Human-Machine Interface (HMI). Upon receiving a confirmation instruction, equipment repair and gap compensation operations are performed. Upon receiving a dynamic scheduling instruction, the current production line is dynamically scheduled, including: When an equipment malfunction is detected on the current production line, the abnormal status is reported to the human-machine interface (HMI). Upon receiving the confirmation command, the maintenance mode is activated, the faulty equipment is controlled to be removed from the workstation along the maintenance track, and the equipment matching gap is calculated in real time. The picking and delivery priority is updated according to the matching gap, and the gap is compensated according to the updated picking and delivery priority. Upon receiving a dynamic scheduling instruction, the production plan for the current production line is regenerated according to the dynamic scheduling instruction, and the product shortage data is recalculated. When the preset production time is detected, the feeding of raw materials is stopped, and the robotic arm is controlled to complete the transfer of all products on the current production line.

5. A dynamic scheduling device for optimizing the path of a robotic arm, characterized in that, The robotic arm path optimization dynamic scheduling device includes: The data calculation module is used to calculate the product shortage data of the current production line in real time; The product retrieval module is used to determine the retrieval priority of the robot arm based on the product gap data, and control the robot arm to perform master-slave product retrieval and delivery on the layout-optimized equipment according to the retrieval priority; The maintenance compensation module is used to report the abnormal status to the human-machine interface (HMI) when an equipment abnormality is detected in the current production line. After receiving the confirmation instruction, it performs equipment maintenance and gap compensation operations, and after receiving the dynamic scheduling instruction, it performs dynamic scheduling of the current production line. The data calculation module is also used to acquire the counting data of the product counter, calculate the deviation of the master and slave products of the current production line based on the counting data, and determine the product gap data based on the deviation. The data calculation module is further configured to acquire the equipment status signal of the current production line, generate each task to be processed corresponding to the equipment status signal according to the preset task generator, and generate a task pool to be processed; determine the product ratio according to the product gap data, acquire the path efficiency and blocking factor of each task in the task pool to be processed; determine the priority score of each task according to the product ratio, the path efficiency and the blocking factor; determine the picking and delivery priority of the robot arm according to the priority score, and control the robot arm to pick and deliver master and slave products to the equipment after layout optimization according to the picking and delivery priority; The data calculation module is further configured to sort the tasks in the task pool in descending order of priority score, and prioritize tasks with the same priority score based on their proximity in the path, thereby generating a pick-up and delivery priority; obtain the main product production process and slave product production process of the current production line, and determine the configuration equipment to be configured based on the main product production process and the slave product production process; optimize the layout of the equipment of the same size and matching production processes in each configuration equipment, and move the corresponding equipment according to the optimized layout to form a layout-paired equipment group, wherein the equipment in the layout-paired equipment group is divided into idle waiting-to-be-placed equipment, equipment in processing, and completed waiting-to-be-retrieved equipment; select the corresponding robot from the robot execution queue according to the pick-up and delivery priority, place the main and slave products to be processed on the idle waiting-to-be-placed equipment, and take away the processed main and slave products on the completed waiting-to-be-retrieved equipment, and update the pick-up and delivery priority in real time after the task is completed.

6. A dynamic scheduling device for optimizing the path of a robotic arm, characterized in that, The robotic arm path optimization dynamic scheduling device includes: a memory, a processor, and a robotic arm path optimization dynamic scheduling program stored in the memory and executable on the processor, wherein the robotic arm path optimization dynamic scheduling program is configured to implement the steps of the robotic arm path optimization dynamic scheduling method as described in any one of claims 1 to 4.

7. A storage medium, characterized in that, The storage medium stores a dynamic scheduling program for optimizing the robot path, which, when executed by a processor, implements the steps of the dynamic scheduling method for optimizing the robot path as described in any one of claims 1 to 4.

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