Automatic guided vehicle dispatching method and device, electronic equipment and storage medium
By obtaining the execution time of the current and remaining tasks of AGV, determining the global optimal execution time, and controlling the AGV execution target tasks, the problem of greedy algorithm not global optimal calculation path is solved, and the efficiency of AGV scheduling is improved.
Patent Information
- Application Number
- CN202311540777.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-11-17
- Publication Date
- 2025-05-20
AI Technical Summary
When there are multiple tasks to be executed in AGV, the optimal driving path calculated based on the greedy algorithm is not the global optimal path, which causes the AGV to execute multiple tasks to be executed longer, reducing the scheduling efficiency.
By obtaining the first execution time of each current task and the second execution time of the remaining tasks, the global optimal execution time of the AGV is determined, and the AGV is controlled to execute the target current task based on this time to ensure that the scheduling is closer to the global optimal.
The efficiency of AGV scheduling is improved, so that the time AGV performs multiple tasks to be executed is shorter, and the scheduling strategy is closer to global optimal.
Smart Images
Figure CN120020662A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of workshop scheduling, and particularly relates to an automated guided vehicle scheduling method, a scheduling device, an electronic device, and a storage medium. Background Art
[0002] With the continuous development of automation and artificial intelligence, the workshops of automated factories have gradually replaced those of traditional factories. An Automated Guided Vehicle (AGV) is an unmanned vehicle controlled by a computer in an automated workshop for transporting materials. Relevant information shows that in the rapid development of the manufacturing industry, the characteristics of simple operation, rapid response, and high efficiency demonstrated by scheduling AGVs in actual production have made AGVs favored and widely used by manufacturers in various enterprises.
[0003] Currently, during the AGV scheduling process, the optimal driving path for the current task to be executed by the AGV is calculated according to the greedy algorithm, and the AGV is scheduled according to the optimal driving path.
[0004] However, when there are multiple tasks to be executed by the AGV, the optimal driving path calculated based on the greedy algorithm is the local optimal path for the current task to be executed by the AGV, rather than the global optimal driving path during the process of the AGV executing multiple tasks to be executed, resulting in more time spent by the AGV actually executing multiple tasks to be executed and reducing the scheduling efficiency of scheduling the AGV. Summary of the Invention
[0005] In view of this, embodiments of the present application provide an automated guided vehicle scheduling method, a scheduling device, an electronic device, and a storage medium to overcome or at least partially solve the above problems in the prior art.
[0006] In a first aspect, embodiments of the present application provide an automated guided vehicle scheduling method, including:
[0007] Obtain multiple first execution durations of multiple current tasks of the automated guided vehicle at the current task node, where each current task corresponds to a first execution duration;
[0008] Obtain a second execution duration of a remaining task corresponding to each current task, where each remaining task is a task required to transport a production workpiece corresponding to each current task from the next task node of the current task node to the end task node;
[0009] Determine the global optimal execution duration of the automated guided vehicle according to the first execution duration and the second execution duration, where the global optimal execution duration is the shortest execution duration from the current task node until a production workpiece corresponding to a current task is transported to the end task node by the automated guided vehicle;
[0010] Control the automated guided vehicle to execute the corresponding target current task according to the globally optimal execution duration.
[0011] The solution provided by this application realizes controlling the automated guided vehicle to execute the target current task according to the globally optimal execution duration determined by the first execution duration of each current task and the second execution duration of the corresponding remaining tasks, ensuring that the scheduling of the automated guided vehicle is closer to the globally optimal scheduling strategy and improving the scheduling efficiency of the automated guided vehicle.
[0012] Among them, in some alternative embodiments, controlling the automated guided vehicle to execute the corresponding target current task according to the globally optimal execution duration includes:
[0013] Determine the corresponding globally optimal path according to the globally optimal execution duration;
[0014] Control the automated guided vehicle to execute the corresponding target current task according to the globally optimal path.
[0015] The solution provided by this embodiment realizes controlling the automated guided vehicle to execute the target current task according to the globally optimal path corresponding to the globally optimal execution duration, ensuring that the scheduling of the automated guided vehicle is closer to the globally optimal scheduling strategy and improving the scheduling efficiency of the automated guided vehicle.
[0016] Among them, in some alternative embodiments, before obtaining the multiple first execution durations of the multiple current tasks of the automated guided vehicle at the current task node, the automated guided vehicle scheduling method further includes:
[0017] Determine whether the current task node where the automated guided vehicle is located is a decision task node, and the decision task node corresponds to multiple tasks;
[0018] Obtaining the multiple first execution durations of the multiple current tasks of the automated guided vehicle at the current task node includes:
[0019] When it is determined that the current task node where the automated guided vehicle is located is a decision task node, obtain the multiple first execution durations of the multiple current tasks of the automated guided vehicle at the current task node.
[0020] The solution provided by this embodiment realizes, when the current task node where the automated guided vehicle is located is a decision task node, controlling the automated guided vehicle to execute the target current task according to the globally optimal path corresponding to the globally optimal execution duration, ensuring that the scheduling of the automated guided vehicle is closer to the globally optimal scheduling strategy and improving the scheduling efficiency of the automated guided vehicle.
[0021] Among them, in some alternative embodiments, the automated guided vehicle scheduling method further includes:
[0022] When it is determined that the current task node where the automated guided vehicle is located is a non - decision task node, control the automated guided vehicle to execute the current task of the current task node, and a non - decision task node corresponds to one task.
[0023] The solution provided in this embodiment realizes directly executing the unique current task at the non - decision task node, reduces the computational amount in the scheduling process of scheduling the automated guided vehicle, and further improves the scheduling efficiency of scheduling the automated guided vehicle.
[0024] Among them, in some alternative embodiments, the method for scheduling an automated guided vehicle further includes:
[0025] Construct a corresponding scheduling simulation model;
[0026] Input the current task node information into the scheduling simulation model, so that the scheduling simulation model performs scheduling simulation on the automated guided vehicle according to the current task node information and outputs the corresponding scheduling simulation result;
[0027] Receive the scheduling simulation result output by the scheduling simulation model.
[0028] The solution provided in this embodiment realizes scheduling simulation of the automated guided vehicle based on the constructed scheduling simulation model, and improves the scheduling experience of scheduling the automated guided vehicle.
[0029] Among them, in some alternative embodiments, the scheduling simulation model includes multiple scheduling simulation threads, there are multiple automated guided vehicles, each automated guided vehicle corresponds to one scheduling simulation thread, the current task node information is multiple, each automated guided vehicle corresponds to one current task node information, and the scheduling simulation results are multiple, and each scheduling simulation result corresponds to one scheduling simulation thread;
[0030] Inputting the current task node information into the scheduling simulation model, so that the scheduling simulation model performs scheduling simulation on the automated guided vehicle according to the current task node information and outputs the corresponding scheduling simulation result includes:
[0031] Input one current task node information corresponding to each automated guided vehicle into one scheduling simulation thread, so that the scheduling simulation thread performs scheduling simulation on the automated guided vehicle according to the current task node information and outputs a corresponding scheduling simulation result;
[0032] Receiving the scheduling simulation result output by the scheduling simulation model includes:
[0033] Receive one scheduling simulation result output by each scheduling simulation thread.
[0034] The solution provided in this embodiment realizes the program simulation according to multi-thread parallelism, simulates the parallel scheduling of multiple automatic guided vehicles in the actual production process, and improves the scheduling experience for scheduling automatic guided vehicles.
[0035] Among them, in some alternative embodiments, the multiple scheduling simulation threads include a first scheduling simulation thread and a second scheduling simulation thread. The automatic guided vehicle scheduling method further includes:
[0036] Obtain the first real-time simulation duration when the first scheduling simulation thread performs scheduling simulation;
[0037] Obtain the second real-time simulation duration when the second scheduling simulation thread performs scheduling simulation;
[0038] In the case where the second real-time simulation duration is greater than the first real-time simulation duration, control the second scheduling simulation thread to stop scheduling simulation until the first simulation duration is greater than or equal to the second real-time simulation duration, and then control the second scheduling simulation thread to resume scheduling simulation.
[0039] The solution provided in this embodiment ensures that the execution time of each scheduling simulation thread is unified with the simulation time of the task assignment, and improves the simulation accuracy of scheduling simulation for automatic guided vehicles.
[0040] In a second aspect, an embodiment of the present application provides an automatic guided vehicle scheduling device, including:
[0041] A first acquisition module, configured to acquire multiple first execution durations of multiple current tasks of the automatic guided vehicle at the current task node, and each current task corresponds to a first execution duration;
[0042] A second acquisition module, configured to acquire a second execution duration of a remaining task corresponding to each current task, where each remaining task is a task required to transport a production workpiece corresponding to each current task from the next task node of the current task node to the end task node;
[0043] A duration determination module, configured to determine the global optimal execution duration of the automatic guided vehicle according to the first execution duration and the second execution duration, where the global optimal execution duration is the shortest execution duration from the start of the current task node of the automatic guided vehicle to the transportation of a production workpiece corresponding to a current task to the end task node;
[0044] A target task control module, configured to control the automatic guided vehicle to execute the corresponding target current task according to the global optimal execution duration.
[0045] In a third aspect, an embodiment of the present application provides an electronic device, including:
[0046] A memory;
[0047] One or more processors, coupled to a memory;
[0048] One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, and the one or more applications are configured to execute the automatic guided vehicle scheduling method provided in the first aspect as described above.
[0049] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which program code is stored, and the program code can be called by a processor to execute the automatic guided vehicle scheduling method provided in the first aspect as described above.
[0050] In a fifth aspect, an embodiment of the present application provides a computer program product, which, when running on a computer device, causes the computer device to execute the automatic guided vehicle scheduling method provided in the first aspect as described above.
[0051] It can be understood that the beneficial effects of the second to fifth aspects as described above can be referred to the relevant descriptions in the first aspect, and will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS
[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or exemplary technical descriptions. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.
[0053] Figure 1 Shows a schematic diagram of a scenario of the automatic guided vehicle scheduling system provided by an embodiment of the present application.
[0054] Figure 2 Shows a schematic flowchart of an automatic guided vehicle scheduling method provided by an embodiment of the present application.
[0055] Figure 3 Shows another schematic flowchart of the automatic guided vehicle scheduling method provided by an embodiment of the present application.
[0056] Figure 4 Shows a structural block diagram of an automatic guided vehicle scheduling device provided by an embodiment of the present application.
[0057] Figure 5 Shows a functional block diagram of an electronic device provided by an embodiment of the present application.
[0058] Figure 6A computer-readable storage medium for storing or carrying program code for implementing an automatic guided vehicle scheduling method according to an embodiment of the present application is shown.
[0059] Figure 7 A computer program product for storing or carrying program code for implementing an automatic guided vehicle scheduling method according to an embodiment of the present application is shown. Detailed implementation manners
[0060] To make the objectives, features, and advantages of the present application more obvious and understandable, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the embodiments described below are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.
[0061] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.
[0062] It should also be understood that the terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.
[0063] It should be further understood that the term "and / or" used in the specification of the present application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.
[0064] In addition, in the description of the present application, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.
[0065] With the continuous development of automation and artificial intelligence, the workshops of automated factories have gradually replaced those of traditional factories. An Automated Guided Vehicle (AGV) is an unmanned vehicle controlled by a computer in an automated workshop for transporting materials. Relevant information shows that in the rapid development of the manufacturing industry, the characteristics of simple operation, quick response, and high efficiency demonstrated by dispatching AGVs in actual production have enabled AGVs to gain the favor of manufacturers and be widely used in various enterprises.
[0066] Currently, during the AGV scheduling process, the optimal driving path for the current task to be executed by the AGV is calculated according to the greedy algorithm, and the AGV is scheduled based on the optimal driving path.
[0067] However, when there are multiple tasks to be executed by the AGV, the optimal driving path calculated based on the greedy algorithm is the local optimal path for the current task to be executed by the AGV, rather than the global optimal driving path during the process of the AGV executing multiple tasks to be executed. This results in more time spent by the AGV in actually executing multiple tasks to be executed, reducing the scheduling efficiency of scheduling the AGV.
[0068] In response to the above problems, the automated guided vehicle scheduling method, scheduling device, electronic device, and storage medium provided in the embodiments of the present application obtain multiple first execution durations of multiple current tasks of the automated guided vehicle at the current task node, each current task corresponding to a first execution duration, and obtain a second execution duration of a remaining task corresponding to each current task. Each remaining task is a task required to transport a production workpiece corresponding to each current task from the next task node of the current task node to the end task node, and determine the global optimal execution duration of the automated guided vehicle according to the first execution duration and the second execution duration. The global optimal execution duration is the shortest execution duration from the start of the current task node by the automated guided vehicle until a production workpiece corresponding to a current task is transported to the end task node, and control the automated guided vehicle to execute the corresponding target current task according to the global optimal execution duration, realizing the control of the automated guided vehicle to execute the target current task according to the global optimal execution duration determined by the first execution duration of each current task and the second execution duration of the corresponding remaining task, ensuring that the scheduling of the automated guided vehicle is closer to the global optimal scheduling strategy and improving the scheduling efficiency of scheduling the automated guided vehicle.
[0069] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application.
[0070] Please refer to Figure 1, which shows a schematic diagram of an application scenario of the automated guided vehicle scheduling system provided by an embodiment of the present application. It may include an automated guided vehicle (AGV) 100 and a control device 200. The AGV 100 is connected to the control device 200 through a wireless network and exchanges data with the control device 200 through the wireless network.
[0071] Among them, the AGV 100 may include, but is not limited to, any one of a latent AGV, a towing AGV, a self-unloading AGV, a lifting AGV, a forklift type AGV, etc.
[0072] The wireless network may include, but is not limited to, any one of a ZigBee network, a Bluetooth (BT) network, a Wireless Fidelity (Wi-Fi) network, a Thread network, a Long Range Radio (LoRa) network, an infrared network, a Low-Power Wide-Area Network (LPWAN), a Narrow Band Internet of Things (NB-IoT), and a Wireless Personal Area Network (WPAN), etc.
[0073] The control device 200 may include, but is not limited to, any one of a server and a terminal device, etc.
[0074] The server may include, but is not limited to, any one of an independent physical server, a server cluster or a distributed system composed of multiple physical servers, and a cloud server, etc.
[0075] The terminal device may include, but is not limited to, mobile terminal devices (such as mobile phones, personal digital assistants (PDAs), tablet personal computers (Tablet PCs), laptops, smart watches, smart bracelets, etc.) and fixed terminal devices (such as desktop computers, smart panels, all-in-one computers, etc.).
[0076] Please refer to Figure 2 , which shows a flowchart of the automated guided vehicle scheduling method provided by an embodiment of the present application. In a specific embodiment, the automated guided vehicle scheduling method may be applied to the control device 200 in the automated guided vehicle scheduling system as shown in Figure 1 shown. Taking the control device 200 as an example, the following will Figure 2The following describes the process shown in detail. The automatic guided vehicle scheduling method may include the following steps S110 to S140.
[0077] Step 110: Obtain multiple first execution durations of multiple current tasks of the automatic guided vehicle at the current task node.
[0078] In an embodiment of the present application, the scheduling environment where the AGV is located may include multiple task nodes, and each task node may include at least one task. When the current task node where the AGV is located includes multiple current tasks, the control device may obtain multiple first execution durations of multiple current tasks of the AGV at the current task node. Among them, each current task corresponds to a first execution duration.
[0079] Specifically, each current task corresponds to a production workpiece. When the current task node where the AGV is located includes multiple current tasks, the control device may obtain the distances of the to-be-transported task nodes of each production workpiece at the current task node, search a preset execution duration table according to the distances of the to-be-transported task nodes, and obtain the first execution duration of each production workpiece, which is the first execution duration corresponding to each current task. Among them, the preset execution duration table can be used to represent the corresponding relationship between the distances of the to-be-transported task nodes and the first execution durations.
[0080] For example, the distances of the to-be-transported task nodes may include, but are not limited to, 3 meters (m), 5 m, 7 m, and 10 m, etc., and the first execution durations may include, but are not limited to, 6 seconds (s), 8 s, 10 s, and 12 s, etc. The corresponding relationship between the distances of the to-be-transported task nodes and the first execution durations may be as shown in Table 1, that is, the preset execution duration table. According to this corresponding relationship, the first execution duration corresponding to each current task can be obtained.
[0081] Table 1
[0082] Distance of the task node to be transported (m) First execution duration (s) 3 6 5 8 7 10 10 12
[0083] It should be noted that the corresponding relationship between the distances of the to-be-transported task nodes and the first execution durations is not limited to that shown in Table 1.
[0084] Among them, each production workpiece corresponds to a current to-be-transported task node, and the control device may calculate the corresponding distance of the to-be-transported task node according to the current task node and the current to-be-transported task node where each production workpiece is located.
[0085] In some embodiments, the control device may determine whether the current task node where the AGV is located is a decision-making task node. When it is determined that the current task node where the AGV is located is a decision-making task node, the control device may obtain multiple first execution durations of multiple current tasks of the AGV at the current task node. When the current task node where the AGV is located is a decision-making task node, the AGV is controlled to execute the target current task according to the global optimal path corresponding to the global optimal execution duration, ensuring that the scheduling of the automated guided vehicle is closer to the global optimal scheduling strategy and improving the scheduling efficiency of the automated guided vehicle.
[0086] Among them, the decision-making task node corresponds to multiple tasks. The control device may obtain the number of current tasks corresponding to the current task node and determine whether the current task node is a decision-making task node according to the number of tasks. When the number of current tasks is multiple, it is determined that the current task node is a decision-making task node; when the number of current tasks is one, it is determined that the current task node is a non-decision-making task node.
[0087] Step 120: Obtain the second execution duration of one remaining task corresponding to each current task.
[0088] In the embodiments of the present application, each current task corresponds to one remaining task, and each remaining task is the task required to transport a production workpiece corresponding to each current task from the next task node of the current task node to the end task node.
[0089] The control device may obtain the task required for a production workpiece corresponding to each current task from the next task node after the execution of each current task until the production workpiece is transported to the end task node, and determine the task as the remaining task corresponding to the current task, and may obtain the second execution duration of the remaining task.
[0090] Among them, the second execution duration may be used to represent the estimated time required to transport the corresponding production workpiece from the next task node of the corresponding current task node to the end task node.
[0091] Step 130: Determine the global optimal execution duration of the automated guided vehicle according to the first execution duration and the second execution duration.
[0092] In the embodiments of the present application, the control device may calculate the sum of the durations of each corresponding first execution duration and the second execution duration, and use the sum of the durations as the global execution duration of the AGV, and determine the smallest global execution duration as the global optimal execution duration of the AGV.
[0093] Among them, the global optimal execution duration can be the shortest execution duration from when the automatic guided vehicle starts from the current task node until it transports a production workpiece corresponding to a current task to the end task node.
[0094] In an application scenario, the first execution duration of each current task of the AGV at the current task node is denoted as g(t), and the second execution duration of a remaining task corresponding to each current task is denoted as h(t). The corresponding global execution duration f(t) can be calculated according to each corresponding first execution duration g(t) and second execution duration h(t) according to Formula 1.
[0095] Formula 1 is: f(t) = g(t) + h(t).
[0096] And sort the multiple global execution durations f(t) in ascending order, and determine the f(t) ranked first as the global optimal execution duration of the AGV.
[0097] Step 140: Control the automatic guided vehicle to execute the corresponding target current task according to the global optimal execution duration.
[0098] In the embodiment of the present application, the control device can send a first execution instruction to the AGV through a wireless network according to the global optimal execution duration. The AGV receives and responds to the first execution instruction, and executes the target current task corresponding to the global optimal duration, realizing the global optimal execution duration determined according to the first execution duration of each current task and the second execution duration of the corresponding remaining task, controlling the AGV to execute the target current task, ensuring that the scheduling of the AGV is closer to the global optimal scheduling strategy, and improving the scheduling efficiency of scheduling the AGV.
[0099] Specifically, the control device can calculate the corresponding global optimal path according to the global optimal execution duration, and send a first execution instruction carrying the global optimal path to the AGV through a wireless network. The AGV receives and responds to the first execution instruction, and executes the target current task corresponding to the global optimal path, realizing the control of the AGV to execute the target current task according to the global optimal path corresponding to the global optimal execution duration, ensuring that the scheduling of the AGV is closer to the global optimal scheduling strategy, and improving the scheduling efficiency of scheduling the AGV.
[0100] In some embodiments, when the control device determines that the current task node where the AGV is located is a non - decision task node, and the non - decision task node corresponds to one task, indicating that the current task corresponding to the current task node is one. The control device can send a second execution instruction to the AGV through a wireless network. The AGV receives and responds to the second execution instruction to execute the current task of the current task node, realizing the direct execution of the only current task at the non - decision task node, reducing the computational amount in the scheduling process of scheduling the AGV, and further improving the scheduling efficiency of scheduling the AGV.
[0101] The solution provided by this application obtains the first execution durations of multiple current tasks of the automatic guided vehicle at the current task node, where each current task corresponds to one first execution duration, and obtains the second execution duration of one remaining task corresponding to each current task. Each remaining task is the task required to transport a production workpiece corresponding to each current task from the next task node of the current task node to the end task node. According to the first execution duration and the second execution duration, the global optimal execution duration of the automatic guided vehicle is determined. The global optimal execution duration is the shortest execution duration from the current task node of the automatic guided vehicle until a production workpiece corresponding to one current task is transported to the end task node. And according to the global optimal execution duration, the control device controls the automatic guided vehicle to execute the corresponding target current task, realizing the control of the automatic guided vehicle to execute the target current task according to the global optimal execution duration determined by the first execution duration of each current task and the second execution duration of the corresponding remaining task, ensuring that the scheduling of the automatic guided vehicle is closer to the global optimal scheduling strategy and improving the scheduling efficiency of scheduling the automatic guided vehicle.
[0102] Please refer to Figure 3 , which shows a flowchart of an automatic guided vehicle scheduling method provided by another embodiment of this application. In a specific embodiment, the automatic guided vehicle scheduling method can be applied to a control device 200 in an automatic guided vehicle scheduling system as shown in Figure 1 . Taking the control device 200 as an example, the process shown in Figure 3 will be elaborated in detail below. The automatic guided vehicle scheduling method can include the following steps S210 to step S270.
[0103] Step 210: Obtain the first execution durations of multiple current tasks of the automatic guided vehicle at the current task node.
[0104] Step 220: Obtain the second execution duration of one remaining task corresponding to each current task.
[0105] Step 230: Determine the global optimal execution duration of the automatic guided vehicle according to the first execution duration and the second execution duration.
[0106] Step 240: Control the automated guided vehicle to execute the corresponding target current task according to the globally optimal execution duration.
[0107] In this embodiment, steps 210, 220, S230, and S240 may refer to the corresponding steps in the foregoing embodiments, and will not be elaborated herein.
[0108] Step 250: Construct a corresponding scheduling simulation model.
[0109] In this embodiment, the control device may input multiple task node information and multiple task information corresponding to the AGV into the Python program. The Python program receives and responds to the multiple task node information and multiple task information, and generates a corresponding scheduling simulation model.
[0110] Among them, each task node information corresponds to at least one task information. Python is a widely used high-level programming language. Python is easy to learn, read, and write, and has rich libraries and framework support. Python is widely used in the field of automation design, such as circuit design, robot control, data analysis, etc. With the help of Python and its related libraries, developers can easily implement the automation design process and improve the design efficiency.
[0111] Step 260: Input the current task node information into the scheduling simulation model, so that the scheduling simulation model performs scheduling simulation on the automated guided vehicle according to the current task node information, and outputs the corresponding scheduling simulation result.
[0112] In this embodiment, the control device may input the current task node information into the scheduling simulation model. The scheduling simulation model receives and responds to the current task node information, performs scheduling simulation on the AGV according to the current task node information, and outputs the corresponding scheduling simulation result to the control device.
[0113] In some embodiments, the scheduling simulation model may include multiple scheduling simulation threads. There may be multiple AGVs. Each AGV may correspond to a scheduling simulation thread. The current task node information may be multiple. Each AGV may correspond to a current task node information. The scheduling simulation results may be multiple. Each scheduling simulation result may correspond to a scheduling simulation thread.
[0114] The control device may input one current task node information corresponding to each AGV into one scheduling simulation thread. The scheduling simulation thread receives and responds to the current task node information, performs scheduling simulation on the AGV according to the current task node information, and outputs a corresponding scheduling simulation result to the control device, realizing the program simulation according to multi-thread parallelism, simulating the multi-AGV parallel scheduling in the actual production process, and improving the scheduling experience of scheduling the AGV.
[0115] Among them, all scheduling simulation threads share global data, and all data includes but is not limited to multiple task node information, multiple task information, etc.
[0116] Step 270: Receive the scheduling simulation result output by the scheduling simulation model.
[0117] In this embodiment, the control device can receive the scheduling simulation result output by the scheduling simulation model, realizing the scheduling simulation of the AGV based on the constructed scheduling simulation model, and improving the scheduling experience for scheduling the AGV.
[0118] In some embodiments, the scheduling simulation model may include multiple scheduling simulation threads, there may be multiple AGVs, each AGV may correspond to a scheduling simulation thread, the current task node information may be multiple, each AGV may correspond to a current task node information, and the scheduling simulation results are multiple, each scheduling simulation result corresponding to a scheduling simulation thread. The control device can receive one scheduling simulation result output by each scheduling simulation thread.
[0119] In some embodiments, the multiple scheduling simulation threads may include a first scheduling simulation thread and a second scheduling simulation thread.
[0120] The control device can obtain the first real-time simulation duration when the first scheduling simulation thread performs scheduling simulation, obtain the second real-time simulation duration when the second scheduling simulation thread performs scheduling simulation, and in the case where the second real-time simulation duration is greater than the first real-time simulation duration, control the second scheduling simulation thread to stop scheduling simulation until the first real-time simulation duration is greater than or equal to the second real-time simulation duration, and then control the second scheduling simulation thread to resume scheduling simulation, ensuring that the execution time of each scheduling simulation thread is unified with the simulation time of the task assignment, and improving the simulation accuracy of the scheduling simulation of the AGV.
[0121] The solution provided in this embodiment obtains the first execution durations of multiple current tasks of the automatic guided vehicle at the current task node, obtains the second execution duration of a remaining task corresponding to each current task, determines the global optimal execution duration of the automatic guided vehicle based on the first execution duration and the second execution duration, controls the automatic guided vehicle to execute the corresponding target current task according to the global optimal execution duration, constructs the corresponding scheduling simulation model, inputs the current task node information into the scheduling simulation model, enables the scheduling simulation model to perform scheduling simulation on the automatic guided vehicle according to the current task node information, outputs the corresponding scheduling simulation result, and receives the scheduling simulation result output by the scheduling simulation model. It realizes the global optimal execution duration determined according to the first execution duration of each current task and the second execution duration of the corresponding remaining task, controls the automatic guided vehicle to execute the target current task, ensures that the scheduling of the automatic guided vehicle is closer to the global optimal scheduling strategy, and improves the scheduling efficiency of scheduling the automatic guided vehicle.
[0122] Furthermore, scheduling simulation of the automatic guided vehicle based on the scheduling simulation model improves the scheduling experience of scheduling the AGV.
[0123] Please refer to Figure 4 , which shows the automatic guided vehicle scheduling device 300 provided in an embodiment of the present application. The automatic guided vehicle scheduling device 300 can be applied to the control device 200 in the automatic guided vehicle scheduling system as shown in Figure 1 . Taking the control device 200 as an example, the automatic guided vehicle scheduling device 300 shown in Figure 4 will be elaborated in detail below. The automatic guided vehicle scheduling device 300 can include a first acquisition module 310, a second acquisition module 320, a duration determination module 330, and a target task control module 340.
[0124] The first acquisition module 310 can be used to acquire the first execution durations of multiple current tasks of the automatic guided vehicle at the current task node, and each current task corresponds to a first execution duration; the second acquisition module 320 can be used to acquire the second execution duration of a remaining task corresponding to each current task, and each remaining task can be the task required to transport a production workpiece corresponding to each current task from the next task node of the current task node to the end task node; the duration determination module 330 can be used to determine the global optimal execution duration of the automatic guided vehicle based on the first execution duration and the second execution duration, and the global optimal execution duration can be the shortest execution duration from the start of the current task node by the automatic guided vehicle until a production workpiece corresponding to a current task is transported to the end task node; the target task control module 340 can be used to control the automatic guided vehicle to execute the corresponding target current task according to the global optimal execution duration.
[0125] In some embodiments, the target task control module 340 may include a determination unit and a control unit.
[0126] The determination unit may be configured to determine a corresponding globally optimal path according to the globally optimal execution duration; the control unit may be configured to control the automatic guided vehicle to execute the corresponding target current task according to the globally optimal path.
[0127] In some embodiments, the automatic guided vehicle scheduling device 300 may further include a node determination module.
[0128] The node determination module may be configured to determine whether the current task node where the automatic guided vehicle is located is a decision task node before the first acquisition module 310 acquires multiple first execution durations of multiple current tasks of the automatic guided vehicle at the current task node, and the decision task node corresponds to multiple tasks.
[0129] In some embodiments, the first acquisition module 310 may include an acquisition unit.
[0130] The acquisition unit may be configured to acquire multiple first execution durations of multiple current tasks of the automatic guided vehicle at the current task node when it is determined that the current task node where the automatic guided vehicle is located is a decision task node.
[0131] In some embodiments, the automatic guided vehicle scheduling device 300 may further include a current task control module.
[0132] The current task control module may be configured to control the automatic guided vehicle to execute the current task of the current task node when it is determined that the current task node where the automatic guided vehicle is located is a non-decision task node, and the non-decision task node corresponds to one task.
[0133] In some embodiments, the automatic guided vehicle scheduling device 300 may further include a construction module, an input module, and a receiving module.
[0134] The construction module may be configured to construct a corresponding scheduling simulation model; the input module may be configured to input current task node information into the scheduling simulation model, so that the scheduling simulation model performs scheduling simulation on the automatic guided vehicle according to the current task node information and outputs a corresponding scheduling simulation result; the receiving module may be configured to receive the scheduling simulation result output by the scheduling simulation model.
[0135] In some embodiments, the scheduling simulation model may include multiple scheduling simulation threads, there may be multiple automatic guided vehicles, each automatic guided vehicle may correspond to one scheduling simulation thread, the current task node information may be multiple, each automatic guided vehicle may correspond to one current task node information, the scheduling simulation results may be multiple, and each scheduling simulation result may correspond to one scheduling simulation thread; the input module may include an input unit.
[0136] The input unit can be used to input the current task node information corresponding to each automated guided vehicle into a scheduling simulation thread, so that the scheduling simulation thread performs scheduling simulation on the automated guided vehicle according to the current task node information and outputs a corresponding scheduling simulation result.
[0137] In some embodiments, the receiving module may include a receiving unit.
[0138] The receiving unit can be used to receive a scheduling simulation result output by each scheduling simulation thread.
[0139] In some embodiments, the multiple scheduling simulation threads may include a first scheduling simulation thread and a second scheduling simulation thread. The automated guided vehicle scheduling device 300 may further include a third acquisition module, a fourth acquisition module, and a simulation control module.
[0140] The third acquisition module can be used to acquire the first real-time simulation duration when the first scheduling simulation thread performs scheduling simulation; the fourth acquisition module can be used to acquire the second real-time simulation duration when the second scheduling simulation thread performs scheduling simulation; the simulation control module can be used to control the second scheduling simulation thread to stop scheduling simulation when the second real-time simulation duration is greater than the first real-time simulation duration, and control the second scheduling simulation thread to resume scheduling simulation until the first real-time simulation duration is greater than or equal to the second real-time simulation duration.
[0141] The solution provided in this embodiment obtains the multiple first execution durations of multiple current tasks of the automated guided vehicle at the current task node, each current task corresponding to a first execution duration, and obtains the second execution duration of a remaining task corresponding to each current task. Each remaining task is the task required to transport a production workpiece corresponding to each current task from the next task node of the current task node to the end task node. And according to the first execution duration and the second execution duration, the global optimal execution duration of the automated guided vehicle is determined. The global optimal execution duration is the shortest execution duration from the start of the current task node by the automated guided vehicle until a production workpiece corresponding to a current task is transported to the end task node. And according to the global optimal execution duration, the automated guided vehicle is controlled to execute the corresponding target current task, realizing the control of the automated guided vehicle to execute the target current task according to the global optimal execution duration determined by the first execution duration of each current task and the second execution duration of the corresponding remaining task, ensuring that the scheduling of the automated guided vehicle is closer to the global optimal scheduling strategy and improving the scheduling efficiency of scheduling the automated guided vehicle.
[0142] It should be noted that the embodiments in this specification are all described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other. For device embodiments, since they are basically similar to method embodiments, the description is relatively simple. For the relevant parts, reference can be made to the corresponding description in the method embodiments. For any processing method described in the method embodiments, it can be implemented by the corresponding processing module in the device embodiments, and will not be elaborated one by one in the device embodiments.
[0143] In addition, in each embodiment of the present application, the functional modules can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules.
[0144] Please refer to Figure 5 , which shows a functional block diagram of an electronic device 500 provided by an embodiment of the present application. The electronic device 500 may include one or more of the following components: a memory 510, a processor 520, and one or more application programs. One or more application programs may be stored in the memory 510 and configured to be executed by one or more processors 520. One or more application programs are configured to execute the methods described in the foregoing method embodiments.
[0145] The memory 510 may include a Random Access Memory (RAM), or may also include a Read-Only Memory. The memory 510 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 510 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as obtaining a first execution duration, obtaining a second execution duration, determining a global optimal execution duration, controlling an automated guided vehicle, executing a target current task, determining a global optimal path, determining whether a current task node is a decision task node, determining that the current task node is a decision task node, determining that the current task node is a non-decision task node, executing the current task, constructing a scheduling simulation model, inputting multiple task node information, inputting multiple task information, scheduling simulation, outputting scheduling simulation results, receiving scheduling simulation results, obtaining a first real-time simulation duration, obtaining a second real-time simulation duration, stopping scheduling simulation, and resuming scheduling simulation, etc.), instructions for implementing the following various method embodiments, etc. The data storage area may also store data created during the use of the electronic device 500 (such as an automated guided vehicle, a current task node, multiple current tasks, multiple first execution durations, remaining tasks, a second execution duration, production workpieces, a next task node, an end task node, a global optimal execution duration, a shortest execution duration, a target current task, a global optimal path, a decision task node, a non-decision task node, a current task, a scheduling simulation model, multiple task node information, multiple task information, scheduling simulation results, multiple scheduling simulation threads, multiple automated guided vehicles, a first scheduling simulation thread, a second scheduling simulation thread, a first real-time simulation duration, and a second real-time simulation duration).
[0146] The processor 520 may include one or more processing cores. The processor 520 connects various parts within the entire electronic device 500 through various interfaces and circuits. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 510, and by invoking the data stored in the memory 510, it performs various functions of the electronic device 500 and processes data. Optionally, the processor 520 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 520 may integrate a combination of one or several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the display content; the modem is used to process wireless communications. It can be understood that the above modem may not be integrated into the processor 520 and may be implemented separately through a communication chip.
[0147] Please refer to Figure 6 , which shows a structural block diagram of a computer-readable storage medium provided by an embodiment of the present application. Program code 610 is stored in the computer-readable storage medium 600, and the program code 610 can be called by a processor to execute the method described in the above method embodiment.
[0148] The computer-readable storage medium 600 may be an electronic memory such as a flash memory, EEPROM (electrically erasable programmable read-only memory), EPROM, hard disk, or ROM. Optionally, the computer-readable storage medium 600 includes a non-transitory computer-readable storage medium. The computer-readable storage medium 600 has a storage space for the program code 610 that executes any method step in the above method. These program codes may be read out from or written into one or more computer program products. The program code 610 may be compressed in an appropriate form, for example.
[0149] Please refer to Figure 7, which shows a structural block diagram of a computer program product 700 provided by an embodiment of the present application. The computer program product 700 includes computer programs / instructions 710, and the computer programs / instructions 710 are stored in a computer-readable storage medium of a computer device. When the computer program product 700 runs on the computer device, the processor of the computer device reads the computer programs / instructions 710 from the computer-readable storage medium, and the processor executes the computer programs / instructions 710, so that the computer device executes the method described in the above method embodiment.
[0150] The solution provided in this embodiment obtains, for each of a plurality of current tasks of an automated guided vehicle at a current task node, a plurality of first execution durations, where each current task corresponds to one first execution duration, and obtains a second execution duration of one remaining task corresponding to each current task, where each remaining task is a task required to transport a production workpiece corresponding to each current task from the next task node of the current task node to an end task node, and determines a globally optimal execution duration of the automated guided vehicle according to the first execution duration and the second execution duration, where the globally optimal execution duration is the shortest execution duration from when the automated guided vehicle starts from the current task node until a production workpiece corresponding to one current task is transported to the end task node, and controls the automated guided vehicle to execute a corresponding target current task according to the globally optimal execution duration, thereby realizing the control of the automated guided vehicle to execute the target current task according to the globally optimal execution duration determined by the first execution duration of each current task and the second execution duration of the corresponding remaining task, ensuring that the scheduling of the automated guided vehicle is closer to the globally optimal scheduling strategy, and improving the scheduling efficiency of scheduling the automated guided vehicle.
[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims
1. An automatic guided vehicle scheduling method, characterized in that: include: Acquire multiple first execution durations of multiple current tasks of the automatic guided vehicle at the current task node, where each current task corresponds to a first execution duration; Obtaining a second execution duration of a remaining task corresponding to each current task, where each remaining task is a task required to transport a production workpiece corresponding to each current task from a next task node of the current task node to a terminal task node; Determine the global optimal execution time of the automatic guided vehicle according to the first execution time and the second execution time, wherein the global optimal execution time is the shortest execution time of the automatic guided vehicle from the current task node to the point where a production workpiece corresponding to the current task is transported to the terminal task node; According to the global optimal execution time, the automatic guided vehicle is controlled to execute the corresponding target current task.
2. The automatic guided vehicle dispatching method according to claim 1, characterized in that: The step of controlling the automatic guided vehicle to execute the corresponding target current task according to the global optimal execution time includes: Determine a corresponding global optimal path according to the global optimal execution time; According to the global optimal path, the automatic guided vehicle is controlled to execute the corresponding target current task.
3. The automatic guided vehicle dispatching method according to claim 1, characterized in that: Before obtaining the first execution durations of the plurality of current tasks of the automatic guided vehicle at the current task node, the automatic guided vehicle scheduling method further includes: Determine whether a current task node where the automatic guided vehicle is located is a decision task node, wherein the decision task node corresponds to multiple tasks; The obtaining of a plurality of first execution durations of a plurality of current tasks of the automatic guided vehicle at the current task node comprises: When it is determined that the current task node where the automatic guided vehicle is located is a decision task node, a plurality of first execution durations of a plurality of current tasks of the automatic guided vehicle at the current task node are obtained.
4. The automatic guided vehicle dispatching method according to claim 3, characterized in that: Also includes: When it is determined that the current task node where the automatic guided vehicle is located is a non-decision task node, the automatic guided vehicle is controlled to execute the current task of the current task node, and the non-decision task node corresponds to one task.
5. The automatic guided vehicle dispatching method according to any one of claims 1 to 4, characterized in that: Also includes: Build the corresponding scheduling simulation model; Inputting current task node information into the scheduling simulation model, so that the scheduling simulation model performs scheduling simulation on the automatic guided vehicle according to the current task node information, and outputs corresponding scheduling simulation results; Receive the scheduling simulation result output by the scheduling simulation model.
6. The automatic guided vehicle dispatching method according to claim 5, characterized in that: The scheduling simulation model includes a plurality of scheduling simulation threads, the number of the automatic guided vehicles is multiple, each automatic guided vehicle corresponds to a scheduling simulation thread, the number of the current task node information is multiple, each automatic guided vehicle corresponds to a current task node information, the number of the scheduling simulation results is multiple, each scheduling simulation result corresponds to a scheduling simulation thread; The inputting of the current task node information into the scheduling simulation model so that the scheduling simulation model performs scheduling simulation on the automatic guided vehicle according to the current task node information and outputs a corresponding scheduling simulation result includes: Inputting a current task node information corresponding to each automatic guided vehicle into a scheduling simulation thread, so that the scheduling simulation thread performs scheduling simulation on the automatic guided vehicle according to the current task node information, and outputs a corresponding scheduling simulation result; The receiving the scheduling simulation result output by the scheduling simulation model includes: Receive a scheduling simulation result output by each scheduling simulation thread.
7. The automatic guided vehicle dispatching method according to claim 6, characterized in that: The multiple scheduling simulation threads include a first scheduling simulation thread and a second scheduling simulation thread, and the automatic guided vehicle scheduling method further includes: Obtaining a first real-time simulation duration when the first scheduling simulation thread performs scheduling simulation; Obtaining a second real-time simulation duration when the second scheduling simulation thread performs scheduling simulation; When the second real-time simulation duration is longer than the first real-time simulation duration, the second scheduling simulation thread is controlled to stop scheduling simulation, until the first real-time simulation duration is longer than or equal to the second real-time simulation duration, the second scheduling simulation thread is controlled to resume scheduling simulation.
8. An automatic guided vehicle dispatching device, characterized in that: include: A first acquisition module is used to acquire multiple first execution durations of multiple current tasks of the automatic guided vehicle at a current task node, each current task corresponding to a first execution duration; A second acquisition module is used to acquire a second execution duration of a remaining task corresponding to each current task, where each remaining task is a task required to transport a production workpiece corresponding to each current task from a next task node of the current task node to a terminal task node; a duration determination module, configured to determine the global optimal execution duration of the automatic guided vehicle according to the first execution duration and the second execution duration, wherein the global optimal execution duration is the shortest execution duration of the automatic guided vehicle from the current task node to the point where a production workpiece corresponding to the current task is transported to the terminal task node; The target task control module is used to control the automatic guided vehicle to execute the corresponding target current task according to the global optimal execution time.
9. An electronic device, wherein: include: Memory; One or more processors coupled to the memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by one or more processors, and the one or more applications are configured to execute the automatic guided vehicle scheduling method as described in any one of claims 1 to 7.
10. A computer-readable storage medium, wherein: The computer-readable storage medium stores program codes, and the program codes can be called by a processor to execute the automatic guided vehicle scheduling method according to any one of claims 1 to 7.