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

Abstract

The embodiment of the invention provides a path planning method and device based on a magnetic drive conveying system, equipment and a medium. The method comprises the steps that an initial conveying task of the magnetic drive conveying system is obtained; a first graph is obtained, in the first graph, conveying units in the magnetic drive conveying system serve as first nodes, and ferry devices between the conveying units serve as first connecting edges; based on the first graph, performing task decomposition on the initial conveying task to obtain sub-conveying tasks of the conveying unit; a second graph is obtained, in the second graph, the machining points in the conveying units serve as second nodes, and conveying rails connected between the machining points serve as second connecting edges; based on the second graph, performing path planning on the sub-conveying tasks to obtain an initial conveying path; and generating a target conveying path according to the initial conveying path. According to the embodiment of the invention, the operation efficiency of the magnetic drive conveying system can be improved.

Description

Technical Field

[0001] The present application relates to the field of path planning technology, and in particular to a path planning method and device, equipment and medium based on a magnetic drive conveying system. Background Art

[0002] A magnetic conveying system is an automated material transfer system based on magnetic levitation technology. It uses electromagnetic force to drive the carrier for contactless movement, offering advantages such as low friction, high precision, and dynamically adjustable paths. This system requires dynamically generating the optimal movement path for the carrier. For example, in smart manufacturing scenarios, process timing may need to be adjusted dynamically due to order insertions, equipment failures, or process changes. Dynamic path planning is required based on these process adjustments.

[0003] In related technologies, the A-star algorithm is used to calculate the shortest path between the transport starting point and the transport end point. However, it is necessary to traverse every path in the magnetic drive conveying system. The calculation amount of path planning increases sharply with the complexity of the path, affecting the operating efficiency of the magnetic drive conveying system. Summary of the Invention

[0004] The main purpose of the embodiments of the present application is to propose a path planning method and device, equipment and medium based on a magnetic drive conveying system, aiming to improve the operating efficiency of the magnetic drive conveying system.

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

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

[0007] Obtain a first graph, in which the conveying units in the magnetic drive conveying system are used as first nodes, and the ferry devices between the conveying units are used as first connecting edges;

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

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

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

[0011] Based on the initial transport path, a target transport path is generated.

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

[0013] An initial transport task acquisition module is used to acquire the initial transport task of the magnetic drive transport system;

[0014] A first graph acquisition module is used to acquire a first graph, in which the conveying units in the magnetic drive conveying system are used as first nodes, and the ferry devices between the conveying units are used as first connecting edges;

[0015] A task decomposition module, configured to decompose the initial transport task based on the first graph to obtain sub-transportation tasks of the transport unit;

[0016] A second graph acquisition module, configured to acquire a second graph, wherein the processing points in the conveying unit are used as second nodes, and the conveying tracks connecting the processing points are used as second connecting edges;

[0017] A path planning module, configured to plan a path for the sub-transportation task based on the second graph to obtain an initial transport path;

[0018] The target conveying path generating module is used to generate the target conveying path according to the initial conveying path.

[0019] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, the memory stores a computer program, and the processor implements the method of the above-mentioned first aspect when executing the computer program.

[0020] To achieve the above-mentioned purpose, the fourth aspect of the embodiments of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the method of the above-mentioned first aspect.

[0021] The path planning method, device, equipment and medium based on the magnetic drive conveying system proposed in the present application divide the magnetic drive conveying system into multiple conveying units and set a first graph accordingly. In the first graph, the conveying units in the magnetic drive conveying system are used as first nodes, and the ferry devices between the conveying units are used as first connecting edges; using the first graph, the initial conveying task of the magnetic drive conveying system is disassembled into sub-conveyance tasks of the conveying units, which simplifies the number of paths involved in path planning; then a second graph is constructed for each conveying unit, in which the processing points in the conveying unit are used as second nodes, and the conveying tracks connecting the processing points are used as second connecting edges; using the second graph, path planning is performed on the sub-conveyance tasks, which can achieve accurate path planning within the conveying unit and obtain the local optimal path as the initial conveying path; based on the initial conveying path, the target conveying path is generated, which can effectively reduce the amount of calculation and improve the operating efficiency of the magnetic drive conveying system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart of the method provided in an embodiment of the present application;

[0023] Figure 2 yes Figure 1 Flowchart of step S103 in FIG.

[0024] Figure 3 is another flow chart of the method provided in an embodiment of the present application;

[0025] Figure 4 yes Figure 1 Another flowchart of step S103 in FIG.

[0026] Figure 5 yes Figure 1 Another flowchart of step S103 in FIG.

[0027] Figure 6 yes Figure 1 Another flowchart of step S105 in FIG.

[0028] Figure 7 yes Figure 1 Flowchart of step S106 in FIG.

[0029] Figure 8 is a schematic structural diagram of the device provided in an embodiment of the present application;

[0030] Figure 9 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0032] It should be noted that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. The terms "first," "second," and so on, in the specification, claims, and drawings, are used to distinguish similar items and are not necessarily used to describe a specific sequence or precedence.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

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

[0035] Magnetic Drive Conveyance System: The magnetic drive conveyance system uses magnetic levitation technology to achieve efficient conveying of items, with lower friction and resistance, and can achieve higher conveying speeds and lower energy consumption. The magnetic drive conveying equipment includes a conveyor track, a mover, a control unit, a feedback scale assembly, a communication module, a ferry device, and a path planning unit. Among them, the conveyor track is used to provide a physical path for the mover to move, allowing the mover to move along a preset route; the mover moves on the conveyor track to carry and transport items; the control unit is used to receive control instructions to control the movement of the mover according to the target conveying path in the control instructions; the feedback scale assembly is deployed on the conveyor track to detect the position, status, and changes in the surrounding environment of the mover; the communication module is used to realize data exchange between the control system and the mover and other system components; the ferry device is used to connect the movers between different conveyor tracks and can move between each conveyor track and dock with the end sections of different conveyor tracks; the path planning unit is used to perform path planning, mover scheduling, and mover monitoring.

[0036] Path Planning: It is a multidisciplinary field that studies and develops theories, methods, technologies and application systems for finding the optimal or feasible path from the starting point to the target point for mobile entities (such as robots, vehicles, drones, etc.) in complex environments. Path planning is an important part of robotics, autonomous driving, logistics and distribution, and other fields. Through mathematical modeling and algorithm design, it provides mobile entities with efficient, safe and environmentally adaptable navigation strategies. Research in this field includes environmental modeling, search algorithms, obstacle avoidance strategies, multi-objective optimization, etc. Path planning can analyze and process spatial and temporal information in complex environments to provide decision support for mobile entities.

[0037] In related technologies, the ferry device in a magnetic drive conveying system is treated as a special node for path planning. However, this method still requires traversing every path in the magnetic drive conveying system to obtain a path that includes the special node. This path planning requires a large amount of computation, which affects the operating efficiency of the magnetic drive conveying system.

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

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

[0040] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to achieve optimal results.

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

[0042] The embodiment of the present application provides a path planning method based on a magnetic drive conveying system, which relates to the field of path planning technology. The embodiment of the present application provides a path planning method based on a magnetic drive conveying system, which can be applied to a terminal, a server side, or software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements a method, etc., but is not limited to the above forms.

[0043] The present application can be used in many general or special computer system environments or configurations. For example: 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, distributed computing environments including any of the above systems or devices, and the like. The present 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, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0044] It should be noted that in each specific embodiment of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained. The non-Company's software tools or components that appear in the embodiments of the present application are merely examples and do not represent actual use.

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

[0046] Step S101, obtaining the initial conveying task of the magnetic drive conveying system;

[0047] Step S102: Obtain a first graph, in which the conveying units in the magnetic drive conveying system are used as first nodes, and the ferry devices between the conveying units are used as first connecting edges;

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

[0049] Step S104: Obtain a second graph, in which the processing points in the conveying unit are used as second nodes, and the conveying tracks connecting the processing points are used as second connecting edges;

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

[0051] Step S106: generating a target conveying path according to the initial conveying path.

[0052] It is easy to understand that the initial conveying task refers to the transportation goal that the magnetic drive conveying system needs to complete, such as transporting materials from point A to point B.

[0053] It is easy to understand that the magnetic drive conveying system includes multiple conveying tracks, the mover moves along the conveying tracks, and the ferry device can move between different conveying tracks and physically dock with the conveying tracks to complete the transfer of the mover between the conveying tracks. The conveying track includes a conveying section and a ferry section. The ferry device includes a vertical docking module and a horizontal docking module. The vertical docking module includes a vertical drive component and a vertical guide structure. The vertical drive component usually adopts a hydraulic scissors mechanism or a direct drive motor to drive the ferry section to rise and fall vertically. The vertical guide structure is used to limit the vertical position of the ferry section so that the ferry section can dock with the conveying section. The horizontal docking module includes a horizontal drive component and a horizontal guide structure. The horizontal drive component usually adopts a linear motor or an electric screw module to drive the ferry section to move horizontally. The horizontal guide structure is used to limit the horizontal position of the ferry section so that the ferry section can dock with the conveying section.

[0054] It can be understood that the embodiment of the present application divides the magnetic drive conveying system into several conveying units, and each conveying unit includes 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 type of each processing point in the same conveying unit is the same; the conveying tracks upstream and downstream of the same process flow can also be divided into one conveying unit; the conveying tracks on the same horizontal plane can also be divided into one conveying unit; it is not limited to this. Based on this, a first figure is constructed, which is a topological structure diagram of the magnetic drive conveying system, including a first node and a first connecting edge; the conveying unit is used as the first node and the ferry device is used as the first connecting edge in the first figure.

[0055] It is easy to understand that task decomposition refers to breaking down the initial transport task into multiple sub-transport tasks, each of which is completed by a transport unit. Specifically, the first transport unit and the second transport unit where the transport starting point and the transport end point are located can be determined based on the initial transport task, and the intermediate transport unit between the first transport unit and the second transport unit can be found in the first graph through the shortest path algorithm, thereby generating sub-transport tasks of the first transport unit, the second transport unit, and the intermediate transport unit. It is also possible to set edge weights for the first graph based on the real-time utilization rate of the ferry device, calculate the path cost based on the edge weights, and find the intermediate transport unit between the first transport unit and the second transport unit in the first graph through the minimum cost path algorithm, thereby generating sub-transport tasks of the first transport unit, the second transport unit, and the intermediate transport unit.

[0056] It is easy to understand that the second figure is a topological structure diagram of the conveying unit. In the second figure, the processing point (such as a processing station or a 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] It is easy to understand that path planning refers to planning specific paths for the sub-transportation tasks in the second figure, and finally generating the target transportation path by integrating the sub-paths.

[0058] In steps S101 to S106 shown in the embodiment of the present application, the magnetic drive conveying system is divided into multiple conveying units, and a first graph is set accordingly. In the first graph, the conveying units in the magnetic drive conveying system are used as first nodes, and the ferry devices between the conveying units are used as first connecting edges; using the first graph, the initial conveying task of the magnetic drive conveying system is decomposed into sub-conveying tasks of the conveying units, which simplifies the number of paths involved in path planning; then a second graph is constructed for each conveying unit, in which the processing points in the conveying units are used as second nodes, and the conveying tracks connecting the processing points are used as second connecting edges; using the second graph, path planning is performed on the sub-conveying tasks, which can achieve accurate path planning within the conveying unit and obtain the local optimal path as the initial conveying path; based on the initial conveying path, the target conveying path is generated, which can effectively reduce the amount of calculation and improve the operating efficiency of the magnetic drive conveying system.

[0059] In step S101 of some embodiments, the initial transport task may be a set of parameters describing the transport objectives to be accomplished by the magnetic drive transport system, such as the starting processing point, target processing point, transport time, etc. The initial transport task may be obtained through an operation interface input by a user or a higher-level control system to obtain parameters such as material type, transport volume, and time constraints; the initial transport task may also be generated by an automated demand forecasting system, without limitation.

[0060] For example, in the production scenario of new energy batteries, it is necessary to complete processes such as electrode coating, slitting, and winding. The magnetic drive conveying system is divided into conveying unit A (with a coating processing point), conveying unit B (with a slitting processing point), and conveying unit C (with a winding processing point). Conveying unit A contains 3 parallel conveying tracks, conveying unit B contains 2 circular tracks, and conveying unit C contains horizontal tracks and arc tracks. A vertical docking module is provided between conveying unit A and conveying unit B, and a horizontal docking module is provided between conveying unit B and conveying unit C. Accordingly, a first graph is constructed, and the first graph contains the first node A, the first node B, and the first node C. Obtain the initial conveying task to transport the electrode from the entrance of conveying unit A to the exit of conveying unit C. The shortest path search is performed on the first graph, and the shortest path obtained is the first node A→first node B→first node C, thereby generating the sub-conveying task of conveying unit A to convey the pole piece from the entrance of conveying unit A to ferry section A, the sub-conveying task of conveying unit B to convey the pole piece from ferry section A' to ferry section B, and the sub-conveying task of conveying unit C to convey the pole piece from ferry section B' to the exit of conveying unit C.

[0061] In step S102 of some embodiments, the first image may be automatically generated by scanning the hardware configuration of the magnetic drive conveying system, or may be obtained through manual configuration, without limitation 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, a combination of transport units that need to work together can be determined.

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

[0064] Step S201: configuring the weight of the first connecting edge according to the real-time usage 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 sub-transportation tasks of the transport unit.

[0066] Simply put, the real-time utilization rate of a ferry device refers to the task processing load of the ferry device per unit time, such as the task queue length and the proportion of working time. For example, if the number of motors passing through the ferry device in the past hour is counted, and if there were motors passing through the ferry device within 30 minutes, 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 a usage rate threshold can be set, and the edge weight of the edge exceeding the usage rate threshold is set to 1, and the edge weight of the edge not exceeding the usage rate threshold is set to 0, without limitation thereto.

[0068] In some embodiments, in step S202, a shortest path algorithm may be used to select the path with the lowest total weight as a sub-transportation task, with each sub-transportation task corresponding to a different transport unit in the path. For example, the path with the lowest total weight selected from the first graph is: node X - node Y - node Z, corresponding to transport unit X - transport unit Y - transport unit Z.

[0069] In steps S201 to S202 shown in the embodiment of the present application, the weight of the first connecting edge is dynamically configured according to the real-time utilization rate of the ferry device, and the edge corresponding to the ferry device with high utilization rate is assigned a high weight; based on the edge weight and the first graph, the initial transportation task is decomposed, and high-weight edges (i.e., high-load ferry devices) can be automatically avoided, and low-weight edges (i.e., idle or low-load ferry devices) can be given priority to achieve dynamic and balanced distribution of task loads, reduce local congestion risks, and optimize global transportation efficiency.

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

[0071] Step S301, obtaining a preset edge weight threshold;

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

[0073] It is easy to understand that node merging refers to merging two or more adjacent first nodes into a virtual node in the first graph. Node merging can simplify the topological structure, reduce the number of first nodes and first connecting edges, and thus reduce the computational complexity of subsequent task decomposition. Specifically, if the edge weight of a first connecting edge is lower than the edge weight threshold, indicating that the load of the ferry device between the adjacent conveying units connected to it is too low, it is considered that the passage cost of the mover in the ferry device is similar to the passage cost of the mover in the conveying track, and the two conveying units are merged into one logical node, and the connection edge of the two conveying units is removed in the updated first graph. For example, node A and node B in the original first graph are connected by edge AB. If the weight of edge AB is lower than the edge weight threshold, they are merged into a virtual node [AB], and subsequent path planning regards it as a single node.

[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 a dynamic scheduling scenario of a magnetic drive conveying system, this threshold may be set to 1.2, indicating that when the weight (i.e., the travel cost) of the first connecting edge is lower than this value, the corresponding conveying unit can be considered to be efficiently connected. The edge weight threshold can be obtained from a default parameter library or based on historical operation data statistics, without limitation.

[0075] In step S302 of some embodiments, when the utilization rate of the ferry device included in the merged virtual node increases and exceeds the edge weight threshold, the merger can be canceled and the original node relationship can be restored to ensure the path planning flexibility of the magnetic drive conveying system in high-load scenarios, taking into account both the optimization of computing efficiency at low load and the refined scheduling of resources at high load, thereby improving the responsiveness and resource utilization of the magnetic drive conveying system in load fluctuation scenarios.

[0076] In steps S301 to S302 of the embodiment of the present application, by setting edge weight thresholds, low-weight nodes are merged to update the first graph, thereby simplifying the topological structure of the first graph and reducing the computational complexity of subsequent task decomposition.

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

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

[0079] Step S402: based on the first node type, filter out a first target node from the first node;

[0080] Step S403: Based on the first target node, the initial transport task is decomposed to obtain sub-transportation 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 by parameters such as task queue length, processing delay time or resource occupancy rate. For example, the congestion rate can be evaluated by real-time monitoring of the number of tasks to be processed by a certain conveying unit (such as there are 5 tasks backlogged in the current queue) and its average processing time (such as each task takes 2 minutes); if the processing delay of the conveying unit exceeds a preset threshold (such as a delay of more than 10 seconds), it is determined to be in a high congestion rate state. The congestion rate can also be defined in other ways, such as by combining hardware resource utilization (such as motor load rate, magnetic drive module energy consumption) or historical task completion trends for dynamic weighted calculation, but is not limited to this.

[0082] In step S402 of some embodiments, the first target node refers to an idle or low-congestion node that is selected from the first node and 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 congested node label), the transport unit marked as idle is preferentially selected as the target node; specifically, the type labels of all nodes in the first graph can be traversed to exclude nodes with a congestion rate exceeding a threshold, and a list of candidate target nodes can be generated. In addition, the screening process can also be combined with additional conditions such as the physical location of the first node, task type compatibility (such as some transport units only support specific materials), etc. to further optimize the selection, and is not limited to a single congestion rate indicator.

[0083] In some embodiments, step S403 of the present invention involves decomposing the initial transport task based on the first target node into sub-transport tasks to be performed by the transport units corresponding to the first target node. For example, if the initial transport task requires transporting materials from starting point A to end point B, a shortest path algorithm can be used to select the shortest path from the first target node as a sub-transport task.

[0084] In steps S401 to S403 shown in the embodiment of the present application, the congestion rate of the transport unit is calculated in real time to mark the node type (such as idle or congested), the first target node with low load is screened out, and the first target node with low load is preferentially selected as the sub-transportation task, thereby effectively reducing the overload risk and improving the balance of task allocation and system resource utilization.

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

[0086] Step S501: Based on the initial transport task, determine a first transport unit and a second transport unit; wherein the first transport unit is the transport unit at the transport starting point, and the second transport unit is the transport unit at the transport end point;

[0087] Step S502: Based on the first graph, search for a transport unit connecting the first transport unit and the second transport unit to obtain an intermediate transport unit;

[0088] Step S503: Generate a sub-transportation task based on the intermediate transport unit.

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

[0090] In steps S201 to S202 shown in the embodiment of the present application, the initial transport task is decomposed into multiple sub-transportation tasks of the first network-intermediate network-second network by obtaining the first transport unit and the second transport unit where the starting point and the end point are located, and dynamically searching for the intermediate transport unit connecting the two based on the first graph, so as to make the connection between the cross-network transportation paths clearer and schedulable.

[0091] In step S104 of some embodiments, a processing point refers to a specific workstation where material manipulation is required, such as an assembly station or an inspection station. A conveyor track includes track components such as straight tracks, curved tracks, and branching tracks. For example, in a semiconductor manufacturing scenario, a processing point may correspond to different process stations such as wafer cleaning, photolithography, and etching. The second image can be constructed through laser scanning or conversion from a CAD drawing, but is not limited thereto.

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

[0093] Step S601: obtaining a processing type of a processing point, configuring a node type of a second node based on the processing type, and obtaining a second node type;

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

[0095] Step S603: Based on the second target node, path planning is performed on the sub-transportation task to obtain an initial transportation 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 specific process types such as drilling, welding, spraying or testing. For example, the processing type can be determined by parsing the equipment configuration parameters of the processing point (such as equipment model, process parameter library) or historical task records (such as the type of tasks performed by the processing point in the past). For example, if a processing point is configured with a laser welding machine and all historical tasks are welding operations, the processing type is marked as welding. The processing type can also be defined in other ways, such as based on manual configuration by the user or real-time sensor feedback (such as the working status of the current processing module), but is not limited thereto. The configuration of the second node type refers to assigning a type identifier (such as a welding node or a 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 a processing point selected from the second nodes that matches the processing task requirements. For example, if the sub-transport task requires a welding operation, based on the second node type (e.g., the welding node label), the nodes marked as welding are selected from all 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-transportation task within the conveying unit based on the selected processing points. For example, if the sub-transportation task requires transporting materials from the entrance of the conveying unit to the exit of the conveying unit via the second target node (e.g., a welding station), an initial conveying path including the second target node is generated using a path planning algorithm. The initial conveying path includes the conveying unit entrance, the second target node, and the conveying unit exit.

[0099] In steps S601 to S603 shown in the embodiment of the present application, by configuring the second node type of the processing point (such as welding, drilling, etc.) and screening the second target node based on the processing task requirements, it is ensured that the initial conveying task includes functionally matching processing points.

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

[0101] Step S701, obtaining the motion trajectory of the ferry device between the intermediate conveying units as the intermediate path;

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

[0103] In step S702 of some embodiments, path splicing refers to integrating the intermediate paths with the initial transport path (i.e., the planned path within a single transport unit) into a complete target transport path. For example, if the initial transport path includes the internal path A1-A2-A3 of transport unit Zone-A and the internal path C1-C2-C3 of transport unit Zone-C, and the intermediate path is the ferry 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 shown in the embodiment of the present application obtain the motion trajectory of the ferry device between the intermediate conveying units as the 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] See also Figure 8 The present application also provides a path planning device based on a magnetic drive conveying system, which can implement the above method. The device includes:

[0106] An initial transport task acquisition module is used to acquire the initial transport task of the magnetic drive transport system;

[0107] A first graph acquisition module is used to acquire a first graph, in which the conveying units in the magnetic drive conveying system are used as first nodes, and the ferry devices between the conveying units are used as first connecting edges;

[0108] A task decomposition module, configured to decompose the initial transport task based on the first graph to obtain sub-transportation tasks of the transport unit;

[0109] A second graph acquisition module, configured to acquire a second graph, wherein the processing points in the conveying unit are used as second nodes, and the conveying tracks connecting the processing points are used as second connecting edges;

[0110] A path planning module, configured to plan a path for the sub-transportation task based on the second graph to obtain an initial transport path;

[0111] The target conveying path generating module is used to generate the target conveying path according to the initial conveying path.

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

[0113] The present application also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the above method when executing the computer program. The electronic device can be any smart terminal including a tablet computer, an in-vehicle computer, or the like.

[0114] See also Figure 9 , Figure 9 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0115] The processor 901 can be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an 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 the present application;

[0116] The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an 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 by the processor 901 to execute the methods of the embodiments of this application.

[0117] Input / output interface 903, used to implement information input and output;

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

[0119] Bus 905 , which 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 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0121] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program, and the computer program implements the above method when executed by a processor.

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

[0123] The path planning method, device, electronic device and storage medium based on the magnetic drive conveying system provided in the embodiments of the present application divide the magnetic drive conveying system into multiple conveying units and set a first graph accordingly. In the first graph, the conveying units in the magnetic drive conveying system are used as first nodes, and the ferry devices between the conveying units are used as first connecting edges; using the first graph, the initial conveying task of the magnetic drive conveying system is disassembled into sub-conveyance tasks of the conveying units, which simplifies the number of paths involved in path planning; then a second graph is constructed for each conveying unit, in which the processing points in the conveying unit are used as second nodes, and the conveying tracks connecting the processing points are used as second connecting edges; using the second graph, path planning is performed on the sub-conveyance tasks, which can achieve accurate path planning within the conveying unit and obtain a local 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 calculation and improve the operating efficiency of the magnetic drive conveying system.

[0124] The embodiments described in the embodiments of this application are intended to more clearly illustrate 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. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in 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 the present application, and may include more or fewer steps than shown in the figures, or a combination of 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, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

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

[0128] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0129] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0130] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0131] The units described above as separate components may or may not be physically separate, and 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 these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0132] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0133] If the integrated unit is implemented in the form of 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 the present application is essentially 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. The computer software product is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store programs.

[0134] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A path planning method based on a magnetic drive conveying system, characterized in that: The method comprises: Obtain the initial transport task of the magnetic drive transport system; Obtain a first graph, wherein the conveying units in the magnetic drive conveying system are used as first nodes, and the ferry devices between the conveying units are used as first connecting edges; Based on the first graph, the initial transport task is decomposed to obtain sub-transportation tasks of the transport unit; Obtain a second graph, wherein the processing points in the conveying unit are used as second nodes, and the conveying tracks connecting the processing points are used as second connecting edges; Based on the second graph, performing path planning for the sub-transportation task to obtain an initial transport path; A target conveyance route is generated based on the initial conveyance route.

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

3. The method according to claim 2, characterized in that The method further includes updating the first graph, which specifically includes: Get the preset edge weight threshold; Based on the edge weight threshold and the edge weight, a node merge is performed on the first node to update the first graph.

4. The method according to claim 1, wherein Decomposing the initial transport task based on the first graph to obtain sub-transport tasks of the transport unit includes: configuring a node type of the first node according to a congestion rate of the transport unit to obtain a first node type; Based on the first node type, filtering out a first target node from the first nodes; Based on the first target node, the initial transport task is decomposed to obtain sub-transportation tasks of the transport unit.

5. The method according to claim 1, wherein The transport task includes a processing task; and performing path planning on the sub-transport task based on the second graph to obtain an initial transport path includes: Acquire a processing type of the processing point, and configure a node type of the second node based on the processing type to obtain a second node type; Based on the processing task and the second node type, screening out a second target node from the second nodes; Based on the second target node, path planning is performed on the sub-transportation task to obtain an initial transportation path.

6. The method according to claim 1, characterized in that The initial transport task includes a transport start point and a transport end point. Based on the first graph, the initial transport task is decomposed to obtain sub-transportation tasks of the transport unit, including: Based on the initial transport task, determining a first transport unit and a second transport unit; wherein the first transport unit is the transport unit at the transport starting point, and the second transport unit is the transport unit at the transport end point; Based on the first graph, searching for a conveying unit connecting the first conveying unit and the second conveying unit to obtain an intermediate conveying unit; The sub-transportation task is generated according to the intermediate transport unit.

7. The method according to claim 6, characterized in that Generating a target conveying path according to the initial conveying path includes: Obtaining a motion trajectory of the ferry device between the intermediate conveying units as an intermediate path; The intermediate path and the initial conveying path are spliced ​​to obtain the target conveying path.

8. A path planning device based on a magnetic drive conveying system, characterized in that: The device comprises: An initial transport task acquisition module is used to acquire the initial transport task of the magnetic drive transport system; A first graph acquisition module is configured to acquire a first graph, wherein the conveying units in the magnetic drive conveying system are used as first nodes, and the ferry devices between the conveying units are used as first connecting edges; a task decomposition module, configured to decompose the initial transport task based on the first graph to obtain sub-transportation tasks of the transport unit; A second graph acquisition module, configured to acquire a second graph, wherein the processing points in the conveying unit are used as second nodes, and the conveying tracks connecting the processing points are used as second connecting edges; a path planning module, configured to perform path planning for the sub-transportation task based on the second graph to obtain an initial transport path; The target conveying path generating module is configured to generate a target conveying path according to the initial conveying path.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the path planning method based on the magnetic drive conveying system according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the path planning method based on a magnetic drive conveying system according to any one of claims 1 to 7 is implemented.

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