Path planning method and device based on magnetic drive conveying system, equipment and medium

Abstract

This application provides a path planning method, apparatus, device, and medium based on a magnetic drive conveyor system. The method includes: obtaining an initial conveying task for the magnetic drive conveyor system; obtaining a first image, where conveying units in the magnetic drive conveyor system are designated as first nodes, and transfer devices between conveying units are designated as first connecting edges; decomposing the initial conveying task based on the first image to obtain sub-conveying tasks for each conveying unit; obtaining a second image, where processing points in each conveying unit are designated as second nodes, and conveying tracks connecting the processing points are designated as second connecting edges; performing path planning on the sub-conveying tasks based on the second image to obtain an initial conveying path; and generating a target conveying path based on the initial conveying path. This application embodiment can improve the operating efficiency of the magnetic drive conveyor system.

Description

Technical Field

[0001] This application relates to the field of path planning technology, and in particular to a path planning method, apparatus, equipment and medium based on a magnetic drive conveyor system. Background Technology

[0002] Magnetic drive conveyor systems are automated systems that use magnetic levitation technology to transfer materials. They drive carriers through electromagnetic force for contactless movement and offer advantages such as low friction, high precision, and dynamically adjustable paths. Magnetic drive conveyor systems require dynamically generating optimal movement paths for the carriers. For example, in smart manufacturing scenarios, process sequences may be dynamically adjusted due to order insertions, equipment failures, or process changes, necessitating dynamic path planning based on these adjustments.

[0003] In related technologies, the shortest path between the starting point and the destination of transportation is calculated using the A* algorithm. However, this requires traversing every path in the magnetic drive conveyor system. The computational load of path planning increases dramatically with the complexity of the path, affecting the operating efficiency of the magnetic drive conveyor system. Summary of the Invention

[0004] The main objective of this application is to propose a path planning method, apparatus, equipment, and medium based on a magnetic drive conveyor system, aiming to improve the operating efficiency of the magnetic drive conveyor system.

[0005] To achieve the above objectives, a first aspect of this application proposes a path planning method based on a magnetic drive conveyor system, the method comprising:

[0006] Obtain the initial transport task of the magnetic drive conveyor system;

[0007] Obtain the first diagram, in which the conveying unit in the magnetic drive conveying system is taken as the first node, and the transfer device between the conveying units is taken as the first connecting edge.

[0008] Based on the first diagram, the initial transport task is decomposed to obtain the sub-transport tasks of the transport unit;

[0009] Obtain the second diagram, in which the processing points in the conveying unit are taken as the second nodes, and the conveying tracks connecting the processing points are taken as the second connecting edges;

[0010] Based on the second graph, path planning is performed on the sub-transportation tasks to obtain the initial transport path;

[0011] Generate the target transport path based on the initial transport path.

[0012] To achieve the above objectives, a second aspect of this application provides a path planning device based on a magnetic drive conveyor system, the device comprising:

[0013] The initial conveying task acquisition module is used to acquire the initial conveying task of the magnetic drive conveyor system;

[0014] The first image acquisition module is used to acquire the first image, in which the conveying unit in the magnetic drive conveying system is taken as the first node and the transfer device between the conveying units is taken as the first connecting edge.

[0015] The task decomposition module is used to decompose the initial delivery task based on the first graph to obtain the sub-delivery tasks of the delivery unit.

[0016] The second image acquisition module is used to acquire the second image, in which the processing points in the conveying unit are taken as the second nodes, and the conveying tracks connecting the processing points are taken as the second connecting edges.

[0017] The path planning module is used to plan the path for the sub-transportation task based on the second graph to obtain the initial transport path;

[0018] The target conveying path generation module is used to generate the target conveying path based on the initial conveying path.

[0019] To achieve the above objectives, a third aspect of the present application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method of the first aspect described above.

[0020] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method of the first aspect described above.

[0021] The path planning method, apparatus, equipment, and medium proposed in this application for a magnetic drive conveyor system divides the magnetic drive conveyor system into multiple conveying units and sets up a first diagram accordingly. In the first diagram, the conveying units in the magnetic drive conveyor system are designated as first nodes, and the transfer devices between the conveying units are designated as first connecting edges. Using the first diagram, the initial conveying task of the magnetic drive conveyor system is decomposed into sub-conveying tasks of the conveying units, simplifying the number of paths involved in path planning. Then, a second diagram is constructed for each conveying unit. In the second diagram, the processing points in the conveying unit are designated as second nodes, and the conveying tracks connecting the processing points are designated as second connecting edges. Using the second diagram, path planning is performed on the sub-conveying tasks, enabling accurate path planning within the conveying unit and obtaining a locally optimal path as the initial conveying path. Based on the initial conveying path, a target conveying path is generated, which can effectively reduce the amount of computation and improve the operating efficiency of the magnetic drive conveyor system. Attached Figure Description

[0022] Figure 1 This is a flowchart of the method provided in the embodiments of this application;

[0023] Figure 2 yes Figure 1 The flowchart of step S103 in the process;

[0024] Figure 3 This is another flowchart of the method provided in the embodiments of this application;

[0025] Figure 4 yes Figure 1 Another flowchart of step S103 in the process;

[0026] Figure 5 yes Figure 1 Another flowchart of step S103 in the process;

[0027] Figure 6 yes Figure 1 Another flowchart of step S105 in the process;

[0028] Figure 7 yes Figure 1 The flowchart of step S106 in the process;

[0029] Figure 8 This is a schematic diagram of the structure of the device provided in the embodiments of this application;

[0030] Figure 9 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0032] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0034] First, let's analyze some of the terms used in this application:

[0035] Magnetic Drive Conveyance System: This system utilizes magnetic levitation technology for efficient transport of goods, offering lower friction and resistance, resulting in higher transport speeds and lower energy consumption. The system comprises a transport track, movers, a control unit, a feedback scale assembly, a communication module, a transfer device, and a path planning unit. The transport track provides the physical path for the movers, allowing them to travel along a preset route. The movers, moving on the track, carry and transport goods. The control unit receives control commands and controls the movers' movement according to the target transport path specified in the commands. The feedback scale assembly, deployed on the transport track, detects the movers' position, status, and changes in the surrounding environment. The communication module facilitates data exchange between the control system, the movers, and other system components. The transfer device connects movers between different transport tracks, moving between tracks and docking with the end sections of different tracks. The path planning unit performs path planning, mover scheduling, and mover monitoring.

[0036] Path planning is a multidisciplinary field that studies and develops theories, methods, technologies, and application systems for finding optimal or feasible paths from a starting point to a destination for mobile entities (such as robots, vehicles, and drones) in complex environments. Path planning is an important component of robotics, autonomous driving, and logistics, providing efficient, safe, and environmentally adaptable navigation strategies for mobile entities through mathematical modeling and algorithm design. Research in this field includes environmental modeling, search algorithms, obstacle avoidance strategies, and multi-objective optimization. Path planning can analyze and process spatial and temporal information in complex environments, providing decision support for mobile entities.

[0037] In related technologies, the shuttle device in the magnetic drive conveyor system is treated as a special node for path planning. However, this method still requires traversing every path in the magnetic drive conveyor system to obtain the path containing the special node. The computational load of path planning is large, which affects the operating efficiency of the magnetic drive conveyor system.

[0038] Based on this, embodiments of this application provide a path planning method, apparatus, equipment, and medium based on a magnetic drive conveyor system, aiming to improve the operating efficiency of the magnetic drive conveyor system.

[0039] The path planning method, apparatus, equipment, and medium based on a magnetic drive conveying system provided in this application are specifically illustrated through the following embodiments. First, one method in this application embodiment is described.

[0040] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0041] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0042] This application provides a path planning method based on a magnetic drive conveyor system, relating to the field of path planning technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the method, but is not limited to the above forms.

[0043] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0044] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirects to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data for the normal operation of embodiments of this application acquired. Software tools or components not belonging to this company appearing in the embodiments of this application are merely illustrative examples and do not represent actual use.

[0045] Figure 1 This is an optional flowchart of the method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S106.

[0046] Step S101: Obtain the initial conveying task of the magnetic drive conveyor system;

[0047] Step S102: Obtain the first diagram, in which the conveying unit in the magnetic drive conveying system is taken as the first node, and the transfer device between the conveying units is taken as the first connecting edge.

[0048] Step S103: Based on the first diagram, the initial conveying task is decomposed to obtain the sub-conveying tasks of the conveying unit;

[0049] Step S104: Obtain the second diagram. In the second diagram, the processing points in the conveying unit are taken as the second nodes, and the conveying tracks connecting the processing points are taken as the second connecting edges.

[0050] Step S105: Based on the second graph, perform path planning for the sub-transportation task to obtain the initial transport path;

[0051] Step S106: Generate the target conveying path based on the initial conveying path.

[0052] In simple terms, the initial transport task refers to the transport objective that the magnetic drive conveyor system needs to accomplish, such as transporting materials from point A to point B.

[0053] In simple terms, a magnetic drive conveyor system includes multiple conveyor tracks. A mover moves along these tracks, and a transfer device can move between different tracks and physically dock with them to transfer the mover between tracks. Each conveyor track includes a conveying section and a transfer section. The transfer device includes a vertical docking module and a horizontal docking module. The vertical docking module contains a vertical drive assembly and a vertical guide structure. The vertical drive assembly typically uses a hydraulic scissor lift mechanism or a direct drive motor to drive the vertical lifting of the transfer section. The vertical guide structure restricts the vertical position of the transfer section, ensuring docking with the conveyor section. The horizontal docking module contains a horizontal drive assembly and a horizontal guide structure. The horizontal drive assembly typically uses a linear motor or an electric screw module to drive the horizontal movement of the transfer section. The horizontal guide structure restricts the horizontal position of the transfer section, ensuring docking with the conveyor section.

[0054] Understandably, the embodiments of this application divide the magnetic drive conveying system into several conveying units, each conveying unit including several conveying tracks. Specifically, the conveying units can be divided according to the processing type of the processing points on the conveying tracks, so that the processing types of each processing point in the same conveying unit are the same; or the conveying tracks upstream and downstream of the same process flow can be divided into one conveying unit; or the conveying tracks on the same horizontal plane can be divided into one conveying unit; and it is not limited to these. Based on this, a first diagram is constructed, which is a topological structure diagram of the magnetic drive conveying system, including a first node and a first connecting edge; in the first diagram, the conveying unit is taken as the first node, and the ferry device is taken as the first connecting edge.

[0055] In simple terms, task decomposition refers to breaking down an initial transport task into multiple sub-transport tasks, each completed by a transport unit. Specifically, based on the initial transport task, a first transport unit and a second transport unit can be determined, containing the transport start and end points respectively. An intermediate transport unit between the first and second transport units is then found in the first graph using a shortest path algorithm, thus generating sub-transport tasks for the first, second, and intermediate transport units. Alternatively, edge weights can be set on the first graph based on the real-time utilization rate of the shuttle device. Path costs are calculated based on these edge weights, and an intermediate transport unit between the first and second transport units is found in the first graph using a minimum cost path algorithm, thus generating sub-transport tasks for the first, second, and intermediate transport units.

[0056] The second figure is a topology diagram of the conveying unit. In the second figure, the processing point (such as the processing station or the detection point) is used as the second node, and the conveying track (physical path or logical connection) between the processing points is used as the second connecting edge.

[0057] In simple terms, path planning refers to planning specific paths for sub-transportation tasks in the second diagram, and finally generating the target transport path by integrating the sub-paths.

[0058] Steps S101 to S106 as shown in the embodiments of this application involve dividing the magnetic drive conveying system into multiple conveying units and correspondingly setting a first diagram. In the first diagram, the conveying units in the magnetic drive conveying system are designated as first nodes, and the transfer devices between the conveying units are designated as first connecting edges. Using the first diagram, the initial conveying task of the magnetic drive conveying system is decomposed into sub-conveying tasks of the conveying units, simplifying the number of paths involved in path planning. Then, a second diagram is constructed for each conveying unit. In the second diagram, the processing points in the conveying unit are designated as second nodes, and the conveying tracks connecting the processing points are designated as second connecting edges. Using the second diagram, path planning is performed on the sub-conveying tasks, enabling accurate path planning within the conveying unit and obtaining a locally optimal path as the initial conveying path. Based on the initial conveying path, a target conveying path is generated, which effectively reduces the amount of computation and improves the operating efficiency of the magnetic drive conveying system.

[0059] In step S101 of some embodiments, the initial conveying task can be a set of parameters describing the conveying target that the magnetic drive conveying system needs to complete, such as the starting processing point, the target processing point, and the transportation time. The initial conveying task can be obtained through an operation interface input by the user or a higher-level control system, and parameters such as material type, transportation volume, and time constraints can be obtained; alternatively, the initial conveying task can be generated by an automated demand forecasting system, and is not limited to these methods.

[0060] For example, in the production of new energy batteries, processes such as electrode coating, slitting, and winding need to be completed. The magnetic drive conveying system is divided into conveying unit A (with coating processing points), conveying unit B (with slitting processing points), and conveying unit C (with winding processing points). Conveying unit A includes three parallel conveying tracks, conveying unit B includes two circular tracks, and conveying unit C includes a horizontal track and an arc track. A vertical connection module is provided between conveying unit A and conveying unit B, and a horizontal connection module is provided between conveying unit B and conveying unit C. Accordingly, a first diagram is constructed, which includes first node A, first node B, and first node C. The initial conveying task is obtained to convey the electrode from the inlet of conveying unit A to the outlet of conveying unit C. The shortest path is found by performing a shortest path search on the first graph. The shortest path is found to be first node A → first node B → first node C. Thus, the sub-transport tasks of transport unit A are generated as follows: transporting the electrode sheet from the entrance of transport unit A to the transfer section A; transport unit B's sub-transport task is to transport the electrode sheet from transfer section A' to transfer section B; and transport unit C's sub-transport task is to transport the electrode sheet from transfer section B' to the exit of transport unit C.

[0061] In step S102 of some embodiments, the first diagram can be automatically generated by scanning the hardware configuration of the magnetic drive conveyor system, or it can be obtained by manual configuration, and is not limited thereto.

[0062] In step S103 of some embodiments, task decomposition is the process of breaking down the initial transport task into sub-transport tasks to be performed by each transport unit. Based on the topological relationship established in the first graph, the combination of transport units that need to work together can be determined.

[0063] Please see Figure 2 In some embodiments, step S103 includes, but is not limited to, steps S201 to S202:

[0064] Step S201: Configure the weight of the first connecting edge according to the real-time utilization rate of the ferry device to obtain the edge weight;

[0065] Step S202: Based on the edge weights and the first graph, the initial transport task is decomposed to obtain the sub-transport tasks of the transport unit.

[0066] In simple terms, the real-time utilization rate of a shuttle bus refers to the task processing load of the shuttle bus per unit of time, such as the length of the task queue and the percentage of working time. For example, if we count whether there are any vehicles passing through the shuttle bus in the past hour, and if there are vehicles passing through the shuttle bus for 30 minutes in that time, then the real-time utilization rate is calculated to be 80%.

[0067] In step S201 of some embodiments, the usage rate can be normalized to obtain the edge weight, or the usage rate threshold can be set to set the edge weight of the edge exceeding the usage rate threshold to 1, and the edge weight of the edge not exceeding the usage rate threshold to 0, and so on.

[0068] In step S202 of some embodiments, the path with the lowest total weight can be selected as a sub-transport task using a shortest path algorithm, and each sub-transport task corresponds to a different transport unit in the path. For example, the path with the lowest total weight selected from the first figure is: node X - node Y - node Z, corresponding to transport unit X - transport unit Y - transport unit Z.

[0069] In steps S201 to S202 of the embodiments of this application, the weight of the first connecting edge is dynamically configured according to the real-time utilization rate of the shuttle device, and the edge corresponding to the high utilization rate shuttle device is assigned a high weight; based on the edge weight and the first graph, the initial transportation task is decomposed, which can automatically avoid high weight edges (i.e., high load shuttle devices) and prioritize low weight edges (i.e., idle or low load shuttle devices), so as to achieve dynamic balanced distribution of task load, reduce the risk of local congestion, and optimize the overall transportation efficiency.

[0070] Please see Figure 3 In some embodiments, the path planning method based on the magnetic drive conveyor system further includes, but is not limited to, steps S301 to S302:

[0071] Step S301: Obtain the preset edge weight threshold;

[0072] Step S302: Based on the edge weight threshold and edge weight, merge the first node to update the first graph.

[0073] In simple terms, node merging refers to combining two or more adjacent first nodes into a single virtual node in the first graph. Node merging simplifies the topology, reduces the number of first nodes and first connecting edges, thereby reducing the computational complexity of subsequent task decomposition. Specifically, if the edge weight of a first connecting edge is lower than the edge weight threshold, it indicates that the load of the shuttle device between the adjacent transport units it connects is too low. Therefore, the cost of the mover traveling on the shuttle device is considered similar to the cost of the mover traveling on the transport track. These two transport units are then merged into a single logical node, and the connecting edge between them is removed from the updated first graph. For example, if node A and node B in the original first graph are connected by edge AB, and the weight of edge AB is lower than the edge weight threshold, they are merged into a virtual node [AB], which is then treated as a single node in subsequent path planning.

[0074] In step S301 of some embodiments, the edge weight threshold refers to a preset critical value used to determine the feasibility of node merging. For example, in the dynamic scheduling scenario of a magnetic drive conveyor system, this threshold may be set to 1.2, indicating that when the weight (i.e., passage cost) of the first connecting edge is lower than this value, the corresponding conveyor unit can be considered as efficiently connected. The edge weight threshold can be obtained through a default parameter library or based on historical operating data statistics, and is not limited to these methods.

[0075] In step S302 of some embodiments, when the utilization rate of the ferry device contained in the merged virtual node increases and exceeds the edge weight threshold, the merge can be terminated and the original node relationship can be restored. This ensures the flexibility of path planning for the magnetic drive conveyor system under high load scenarios, takes into account both the optimization of computing efficiency under low load and the fine-grained scheduling of resources under high load, and improves the responsiveness and resource utilization of the magnetic drive conveyor system under load fluctuation scenarios.

[0076] Steps S301 to S302 in this embodiment of the application update the first graph by setting an edge weight threshold and merging low-weight nodes, thereby simplifying the topology of the first graph and reducing the computational complexity of subsequent task decomposition.

[0077] Please see Figure 4 In some embodiments, step S103 includes, but is not limited to, steps S401 to S403:

[0078] Step S401: Configure the node type of the first node according to the congestion rate of the conveying unit to obtain the first node type;

[0079] Step S402: Based on the first node type, select the first target node from the first nodes;

[0080] Step S403: Based on the first target node, the initial transport task is decomposed to obtain the sub-transport tasks of the transport unit.

[0081] In step S401 of some embodiments, the congestion rate of the conveying unit refers to an indicator used to quantify the current task load status of the conveying unit, such as calculated using parameters like task queue length, processing delay time, or resource utilization rate. For example, the congestion rate can be assessed by real-time monitoring of the number of tasks to be processed by a certain conveying unit (e.g., 5 tasks currently backed up in the queue) and their average processing time (e.g., each task takes 2 minutes); if the processing delay of the conveying unit exceeds a preset threshold (e.g., delay exceeds 10 seconds), it is determined to be a high congestion rate state. The congestion rate can also be defined in other ways, such as by dynamically weighting the calculation based on hardware resource utilization (e.g., motor load rate, magnetic drive module energy consumption) or historical task completion trends, and is not limited to these methods.

[0082] In step S402 of some embodiments, the first target node refers to an idle or low-congestion node selected from the first nodes that is suitable for undertaking the sub-transportation task. For example, based on the first node type configured in step S401 (such as an idle node or congestion node label), transport units marked as idle are preferentially selected as target nodes; specifically, a candidate target node list can be generated by traversing the type labels of all nodes in the first graph, excluding nodes with congestion rates exceeding a threshold. In addition, the selection process can be further optimized by combining additional conditions such as the physical location of the first node and task type compatibility (such as some transport units only supporting specific materials), and is not limited to a single congestion rate indicator.

[0083] In step S403 of some embodiments, decomposing the initial conveying task based on the first target node is a process of breaking down the initial conveying task into sub-conveying tasks executed by each conveying unit corresponding to the first target node. For example, if the initial conveying task needs to transport materials from the starting point A to the ending point B, the shortest path can be selected from the first target nodes as the sub-conveying task using a shortest path algorithm.

[0084] Steps S401 to S403 as shown in the embodiments of this application, by calculating the congestion rate of the transport unit in real time and marking the node type (such as idle or congested), the first target node with low load is selected and the first target node with low load is selected as the sub-transport task, which effectively reduces the risk of overload and improves the balance of task allocation and system resource utilization.

[0085] Please see Figure 5 In some embodiments, step S103 includes, but is not limited to, steps S501 to S503:

[0086] Step S501: Based on the initial transportation task, determine the first transportation unit and the second transportation unit; wherein, the first transportation unit is the transportation starting unit and the second transportation unit is the transportation ending unit.

[0087] Step S502: Based on the first diagram, locate the conveying unit connecting the first conveying unit and the second conveying unit to obtain the intermediate conveying unit;

[0088] Step S503: Generate sub-transport tasks based on intermediate transport units.

[0089] In step S502 of some embodiments, the intermediate conveying unit refers to the conveying unit connecting the first conveying unit and the second conveying unit. For example, based on the first graph, the conveying unit Zone-B connecting conveying unit Zone-A and conveying unit Zone-C is found to be the intermediate conveying unit using a path search algorithm (such as the shortest path algorithm or breadth-first search). In specific implementations, the optimal intermediate path can be selected by combining edge weights, for example, prioritizing the intermediate conveying unit corresponding to the path with a lower load on the ferry device.

[0090] Steps S201 to S202 as shown in the embodiments of this application involve obtaining the first and second transport units where the starting point and the ending point are located, and dynamically searching for the intermediate transport unit connecting the two based on the first diagram. This decomposes the initial transport task into multiple sub-transport tasks of the first network, intermediate network, and second network, making the connection of cross-network transport paths clearer and more schedulable.

[0091] In step S104 of some embodiments, the processing point refers to the specific workstation where the material needs to be manipulated, such as an assembly station or an inspection station; the conveyor track includes track components such as straight tracks, curves, and branch tracks. For example, in a semiconductor manufacturing scenario, a processing point may correspond to different process stations such as wafer cleaning, photolithography, and etching. A second drawing can be constructed by laser scanning or conversion of CAD drawings, and is not limited to these methods.

[0092] Please see Figure 6 In some embodiments, step S105 includes, but is not limited to, steps S601 to S603:

[0093] Step S601: Obtain the processing type of the processing point, configure the node type of the second node based on the processing type, and obtain the second node type;

[0094] Step S602: Based on the processing task and the second node type, select the second target node from the second nodes;

[0095] Step S603: Based on the second target node, perform path planning for the sub-transportation task to obtain the initial transport path.

[0096] In step S601 of some embodiments, the processing type of a processing point refers to a classification label used to define the functional attributes of the processing point, such as a specific process type like drilling, welding, spraying, or inspection. For example, the processing type can be determined by parsing the processing point's equipment configuration parameters (such as equipment model, process parameter library) or historical task records (such as the types of tasks the processing point has performed in the past). For example, if a processing point is configured with a laser welding machine and all its historical tasks are welding operations, then the processing type is marked as welding. The processing type can also be defined in other ways, such as based on user manual configuration or real-time sensor feedback (such as the current working status of the processing module), and is not limited to these. The configuration of the second node type refers to assigning a type identifier (such as welding node or drilling node) to the second node (i.e., the processing point) according to the processing type to distinguish its functional characteristics.

[0097] In step S602 of some embodiments, the second target node refers to the processing point selected from the second nodes that matches the processing task requirements. For example, if the sub-transport task requires welding operations, then based on the second node type (such as welding node label), nodes marked as welding are selected from all the second nodes as the second target node.

[0098] In step S603 of some embodiments, path planning based on the second target node refers to generating a specific path for the sub-transport task within the transport unit based on the selected processing points. For example, if the sub-transport task needs to transport materials from the entrance of the transport unit, through the second target node (such as a welding station), to the exit of the transport unit, an initial transport path including the second target node is generated by a path planning algorithm. The initial transport path includes the transport unit entrance, the second target node, and the transport unit exit.

[0099] Steps S601 to S603 as shown in the embodiments of this application configure the second node type of the processing point (such as welding, drilling, etc.) and filter the second target node based on the processing task requirements to ensure that the initial delivery task includes processing points with matching functions.

[0100] Please see Figure 7 In some embodiments, step S106 includes, but is not limited to, steps S701 to S702:

[0101] Step S701: Obtain the motion trajectory of the transfer device between intermediate conveying units as the intermediate path;

[0102] Step S702: The intermediate path and the initial conveying path are spliced ​​together to obtain the target conveying path.

[0103] In step S702 of some embodiments, path splicing refers to integrating the intermediate path with the initial conveying path (i.e., the planned path within a single conveying unit) into a complete target conveying path. For example, the initial conveying path includes the internal paths A1-A2-A3 of conveying unit Zone-A and C1-C2-C3 of conveying unit Zone-C, while the intermediate path is the shuttle device B1 from Zone-A to Zone-C. Then, the target path generated after splicing is A1-A2-A3-B1-C1-C2-C3.

[0104] Steps S701 to S702, as shown in the embodiments of this application, obtain the motion trajectory of the transfer device between intermediate conveying units as an intermediate path, and splice it with the initial conveying path inside each conveying unit to ensure the global continuity of the cross-regional transportation path.

[0105] Please see Figure 8 This application also provides a path planning device based on a magnetic drive conveyor system, which can implement the above method. The device includes:

[0106] The initial conveying task acquisition module is used to acquire the initial conveying task of the magnetic drive conveyor system;

[0107] The first image acquisition module is used to acquire the first image, in which the conveying unit in the magnetic drive conveying system is taken as the first node and the transfer device between the conveying units is taken as the first connecting edge.

[0108] The task decomposition module is used to decompose the initial delivery task based on the first graph to obtain the sub-delivery tasks of the delivery unit.

[0109] The second image acquisition module is used to acquire the second image, in which the processing points in the conveying unit are taken as the second nodes, and the conveying tracks connecting the processing points are taken as the second connecting edges.

[0110] The path planning module is used to plan the path for the sub-transportation task based on the second graph to obtain the initial transport path;

[0111] The target conveying path generation module is used to generate the target conveying path based on the initial conveying path.

[0112] The specific implementation of this device is basically the same as the specific embodiments of the above-described method, and will not be repeated here.

[0113] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0114] Please see Figure 9 , Figure 9 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0115] The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0116] The memory 902 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901.

[0117] The input / output interface 903 is used to implement information input and output;

[0118] The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0119] Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904);

[0120] The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0121] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0122] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0123] The path planning method, apparatus, electronic device, and storage medium based on a magnetic drive conveyor system provided in this application divide the magnetic drive conveyor system into multiple conveying units and correspondingly set up a first diagram. In the first diagram, the conveying units in the magnetic drive conveyor system are designated as first nodes, and the transfer devices between the conveying units are designated as first connecting edges. Using the first diagram, the initial conveying task of the magnetic drive conveyor system is decomposed into sub-conveying tasks of the conveying units, simplifying the number of paths involved in path planning. Then, a second diagram is constructed for each conveying unit. In the second diagram, the processing points in the conveying unit are designated as second nodes, and the conveying tracks connecting the processing points are designated as second connecting edges. Using the second diagram, path planning is performed on the sub-conveying tasks, enabling accurate path planning within the conveying unit and obtaining a locally optimal path as the initial conveying path. Based on the initial conveying path, a target conveying path is generated, which can effectively reduce the amount of computation and improve the operating efficiency of the magnetic drive conveyor system.

[0124] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0125] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0126] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0127] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0128] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0129] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0130] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0131] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0132] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0133] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0134] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A path planning method based on a magnetic drive conveyor system, characterized in that, The method includes: Obtain the initial transport task of the magnetic drive conveyor system; Obtain the first diagram, in which the conveying unit in the magnetic drive conveying system is taken as the first node, and the transfer device between the conveying units is taken as the first connecting edge. Based on the first figure, the initial transport task is decomposed to obtain the sub-transport tasks of the transport unit; Obtain the second diagram, in which the processing points in the conveying unit are taken as the second nodes, and the conveying tracks connecting the processing points are taken as the second connecting edges; Based on the second diagram, path planning is performed on the sub-transportation task to obtain the initial transport path; Generate a target conveying path based on the initial conveying path; The initial transport task includes a processing task; the step of performing path planning on the sub-transport tasks based on the second graph to obtain the initial transport path includes: Obtain the processing type of the processing point, configure the node type of the second node based on the processing type, and obtain the second node type; Based on the processing task and the second node type, a second target node is selected from the second node; Based on the second target node, path planning is performed on the sub-transportation task to obtain the initial transport path.

2. The method according to claim 1, characterized in that, Based on the first diagram, the initial transport task is decomposed to obtain the sub-transport tasks of the transport unit, including: Based on the real-time utilization rate of the ferry device, the weight of the first connecting edge is configured to obtain the edge weight; Based on the edge weights and the first graph, the initial transport task is decomposed to obtain the sub-transport tasks of the transport unit.

3. The method according to claim 2, characterized in that, It also includes updating the first image, specifically including: Obtain the preset edge weight threshold; Based on the edge weight threshold and the edge weight, the first node is merged to update the first graph.

4. The method according to claim 1, characterized in that, Based on the first diagram, the initial transport task is decomposed to obtain the sub-transport tasks of the transport unit, including: Based on the congestion rate of the conveying unit, the node type of the first node is configured to obtain the first node type; Based on the first node type, a first target node is selected from the first nodes; Based on the first target node, the initial transport task is decomposed to obtain the sub-transport tasks of the transport unit.

5. The method according to claim 1, characterized in that, The initial transport task includes a transport start point and a transport destination; based on the first diagram, the initial transport task is decomposed to obtain the sub-transport tasks of the transport unit, including: Based on the initial transport task, a first transport unit and a second transport unit are determined; wherein, the first transport unit is the transport unit where the transport starting point is located, and the second transport unit is the transport ending point; Based on the first diagram, the intermediate conveying unit is obtained by locating the conveying unit that connects the first conveying unit and the second conveying unit. The sub-transport task is generated based on the intermediate transport unit.

6. The method according to claim 5, characterized in that, The step of generating a target conveying path based on the initial conveying path includes: The motion trajectory of the shuttle device between the intermediate conveying units is obtained as the intermediate path; The intermediate path and the initial transport path are spliced ​​together to obtain the target transport path.

7. A path planning device based on a magnetic drive conveyor system, characterized in that, The device includes: The initial conveying task acquisition module is used to acquire the initial conveying task of the magnetic drive conveyor system; The first image acquisition module is used to acquire a first image, in which the conveying unit in the magnetic drive conveying system is taken as the first node and the transfer device between the conveying units is taken as the first connecting edge. The task decomposition module is used to decompose the initial delivery task based on the first diagram to obtain the sub-delivery tasks of the delivery unit. The second image acquisition module is used to acquire a second image, in which the processing points in the conveying unit are taken as second nodes and the conveying tracks connecting the processing points are taken as second connecting edges. The path planning module is used to perform path planning for the sub-transportation task based on the second graph to obtain an initial transport path; The target conveying path generation module is used to generate a target conveying path based on the initial conveying path; The initial transport task includes a processing task; the step of performing path planning on the sub-transport tasks based on the second graph to obtain the initial transport path includes: Obtain the processing type of the processing point, configure the node type of the second node based on the processing type, and obtain the second node type; Based on the processing task and the second node type, a second target node is selected from the second node; Based on the second target node, path planning is performed on the sub-transportation task to obtain the initial transport path.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the path planning method based on the magnetic drive conveying system as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the path planning method based on the magnetic drive conveyor system as described in any one of claims 1 to 6.

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