Intelligent AGV practical training teaching platform design based on digital technology

Abstract

The invention relates to the field of AGV practical training teaching, in particular to an intelligent AGV practical training teaching platform design based on a digital technology, and the platform comprises a teaching module which is used for constructing a VR-type AGV theoretical knowledge learning scene through employing a 3D modeling technology and a VR engine; the practical training module is used for building and providing an AGV hardware assembly practical operation environment, an AGV hardware control circuit connection practical operation environment, an AGV software programming debugging practical operation environment and a task operation practical operation environment; the practical training module comprises a cross-domain fusion practical training unit, and the cross-domain fusion practical training unit is used for generating VR type practical training scenes of different industries; students perform practical training operation of path planning and practical training operation of scheduling management of the AGV three-dimensional model by utilizing practical training scenes of different VR-type industries; and the multi-person online cooperation module is used for enabling different students to remotely operate the same or multiple AGV three-dimensional models in the same VR type AGV theoretical knowledge learning scene at the same time. According to the invention, students can know differences of different industries, and the practical operation ability and innovative thinking of the students are improved.

Description

Technical Field

[0001] The present invention relates to the field of AGV training teaching, and particularly to the design of an intelligent AGV training teaching platform based on digital technology. Background Art

[0002] With the continuous advancement of the concepts of Industry 4.0 and intelligent manufacturing, the application of automated guided vehicles (AGVs) in the fields of modern logistics and flexible manufacturing is becoming increasingly widespread. For the cultivation of relevant professional talents, a training teaching platform is particularly important. However, the existing AGV training teaching platforms have many deficiencies, especially in terms of form, which is relatively single.

[0003] Most traditional AGV training teaching platforms only provide simple AGV operation demonstrations. For example, PPTs are played on a computer, and students learn through AGV models. Students can only understand the operation process of AGVs by observing, lacking the opportunity for actual hands-on operation. Therefore, the emergence of an intelligent AGV training teaching platform based on digital technology solves this problem. However, in the face of the diverse needs of different industries, the existing intelligent AGV training teaching platforms based on digital technology lack flexibility and pertinence. The application characteristics and requirements of AGVs vary, and students cannot deeply understand the specific applications of AGVs in different industries, making it difficult to meet the diversified needs of future employment.

[0004] In summary, how to solve the problem that the existing intelligent AGV training teaching platforms based on digital technology lack flexibility and pertinence. The application characteristics and requirements of AGVs vary, and students cannot deeply understand the specific applications of AGVs in different industries, making it difficult to meet the diversified needs of future employment has become a difficult problem that urgently needs to be solved in this field. Therefore, it is necessary to propose the design of an intelligent AGV training teaching platform based on digital technology. Summary of the Invention

[0005] To solve the above problems, the present invention provides the design of an intelligent AGV training teaching platform based on digital technology. When students use this intelligent AGV training teaching platform, they can perform operations such as path planning and scheduling management according to the application characteristics of AGVs in specific industries, understand the differences in logistics needs in different industries, and can improve students' practical operation ability and innovative thinking.

[0006] To achieve the above object, the technical solution of the present invention is as follows: The design of an intelligent AGV training teaching platform based on digital technology, a teaching module, which is used to construct a teaching database by using an electronic database of AGV theoretical knowledge; use 3D modeling technology and a VR engine to convert the content in the teaching database into an AGV three-dimensional model and a virtual teaching scene, and use the AGV three-dimensional model and the virtual teaching scene to construct a VR-style AGV theoretical knowledge learning scene;

[0007] The training module is used to build and provide a practical operation environment for AGV hardware assembly, a practical operation environment for connecting the AGV hardware control circuit, a practical operation environment for AGV software programming and debugging, and a task operation practical environment for simulating real working scenarios;

[0008] The training module includes a cross-domain integration training unit, which is used to generate VR-based training scenarios for different industries according to the needs of different industries by using 3D modeling technology and VR engines; students use the VR-based training scenarios for different industries to conduct practical training operations for path planning and dispatching management of the AGV three-dimensional model; the VR-based training scenarios for different industries include one of automotive manufacturing, e-commerce warehousing, and pharmaceutical distribution;

[0009] The multi-person online collaboration module includes an online collaboration platform; different students log in to the online collaboration platform using their personal accounts. After logging in, different students use the online collaboration platform to remotely operate the same or multiple AGV three-dimensional models in the same VR-based training scenario for different industries at the same time.

[0010] Furthermore, the teaching module includes a basic operation teaching unit, which is used for teachers to control the basic actions of starting, stopping, moving forward, moving backward, and turning of the AGV three-dimensional model in the VR-based AGV theoretical knowledge learning scenario using the AGV three-dimensional model.

[0011] Furthermore, the teaching module also includes a path planning teaching unit, which is used for teachers to teach the switching algorithm of different navigation modes of the AGV three-dimensional model using the VR-based AGV theoretical knowledge learning scenario; different navigation modes include one of magnetic navigation, laser navigation, and visual navigation.

[0012] Furthermore, the teaching module also includes a dispatching management teaching unit, which is used for teachers to teach dispatching management strategies in the scenario of using multiple AGV three-dimensional models to work together in the VR-based AGV theoretical knowledge learning scenario.

[0013] Furthermore, the training module includes a fault simulation and troubleshooting unit, which is used to simulate various faults that occur during the operation of the AGV three-dimensional model; students perform fault troubleshooting and repair operations on the AGV three-dimensional model.

[0014] Furthermore, in the path planning teaching unit, the switching algorithm of the navigation mode includes the following steps:

[0015] S1, initialization of the AGV three-dimensional model: The AGV three-dimensional model is initialized and the initial navigation mode is loaded.

[0016] S2. The AGV 3D model selects a navigation mode: The AGV 3D model detects the environmental information of the VR-style AGV theoretical knowledge learning scenario in real time; when the AGV 3D model detects a magnetic strip, it enters the magnetic navigation mode; when the AGV 3D model detects lidar data, it enters the laser navigation mode; when the AGV 3D model detects visual features, it enters the visual navigation mode; the visual features include QR codes.

[0017] S3. The AGV 3D model constructs a path: The AGV 3D model calls the corresponding control algorithm according to the entered navigation mode; when the AGV 3D model enters the magnetic navigation mode, the AGV 3D model travels along the magnetic strip path and uses the PID control algorithm to adjust the direction; when the AGV 3D model enters laser navigation, the AGV 3D model constructs a map of the VR-style AGV theoretical knowledge learning scenario based on the SLAM algorithm and uses the environmental information, and automatically plans the shortest path from the current position of the AGV 3D model to the end point; when the AGV 3D model enters visual navigation, the AGV 3D model performs positioning based on the YOLO image recognition technology and plans the forward path.

[0018] S4. The switching of the AGV 3D model path: When the AGV 3D model is moving forward, the AGV 3D model monitors the navigation state in real time. When it encounters a lost path, the AGV 3D model switches to another navigation mode.

[0019] Further, in the scheduling management teaching unit, the scheduling management strategy includes task assignment, vehicle scheduling, and conflict avoidance.

[0020] Further, in the fault simulation and troubleshooting unit, various faults include one or more of sensor faults, motor faults, and communication faults.

[0021] Further, it also includes a competition module; the competition module is used to hold competitions for the practical operation of path planning and scheduling management of the AGV 3D model by using the training module and the multi-person online collaboration module.

[0022] Further, in the competition module, the competition includes a speed competition and a complex task challenge competition.

[0023] Adopting the above solution has the following beneficial effects:

[0024] 1. In the present invention, by using 3D modeling technology and a VR engine to construct a teaching module, the abstract AGV theoretical knowledge is transformed into an intuitive VR-style learning scenario, breaking the limitation of traditional teaching relying only on PPT presentations and model observations, enabling students to learn in an immersive manner, greatly improving students' understanding and mastery of AGV theoretical knowledge, and making the learning process more vivid and efficient.

[0025] 2. In the present invention, the training module provides a comprehensive practical environment, covering hardware assembly, circuit connection, software programming and debugging, as well as task operations simulating real working scenarios. Moreover, it is equipped with a cross-domain integration training unit, which generates VR-style training scenarios for different industries, enabling students to deeply understand the application characteristics of AGVs in different industries such as automobile manufacturing, e-commerce warehousing, and pharmaceutical distribution, and conduct targeted path planning and scheduling management training, comprehensively improving students' practical operation ability, so that they can quickly adapt to the work requirements of different industries after graduation.

[0026] 3. In the present invention, the multi-person online collaboration module and the competition module are provided. The multi-person online collaboration module cultivates students' teamwork and communication skills through an online collaboration platform. The competition module holds various competitions and cooperates with enterprises to incorporate actual project requirements, stimulating students' innovative thinking and competitive awareness, enabling students to come into contact with cutting-edge industry issues in the competition and enhancing their employment competitiveness.

[0027] Additional aspects and advantages of the present invention will be given in part in the following description, will become apparent in part from the following description, or will be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a structural diagram designed for the intelligent AGV training and teaching platform based on digital technology of the present invention.

[0029] Figure 2 It is a step diagram of the switching algorithm of the navigation mode in the design of the intelligent AGV training and teaching platform based on digital technology of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] The following is a further detailed description through specific embodiments:

[0031] As shown in the appendix Figure 1 - Figure 2Shown as follows: The design of an intelligent AGV training and teaching platform based on digital technology mainly includes a teaching module, a training module, and a multi-person online collaboration module. Among them, the teaching module is mainly used to construct a teaching database by using the electronic database of AGV theoretical knowledge. By applying 3D modeling technology and VR engine, the content in the teaching database is transformed into AGV three-dimensional models and virtual teaching scenarios, and a VR-based AGV theoretical knowledge learning scenario is constructed. The training module is mainly used to build and provide an actual operation environment for AGV hardware assembly, an actual operation environment for connecting AGV hardware control circuits, an actual operation environment for AGV software programming and debugging, and an actual operation environment for task operations simulating real working scenarios. The multi-person online collaboration module includes an online collaboration platform, which is mainly used for different students to log in with personal accounts and be able to remotely operate the same or multiple AGV three-dimensional models in the same VR-based AGV theoretical knowledge learning scenario to cultivate students' collaboration ability. The competition module is mainly used to hold a practical operation competition for path planning and scheduling management of AGV three-dimensional models by using the training module and the multi-person online collaboration module.

[0032] The functions of each module will be explained in detail as follows:

[0033] The teaching module is used to construct a teaching database by using the electronic database of AGV theoretical knowledge; by applying 3D modeling technology and VR engine, the content in the teaching database is transformed into AGV three-dimensional models and virtual teaching scenarios, and a VR-based AGV theoretical knowledge learning scenario is constructed by using the AGV three-dimensional models and virtual teaching scenarios.

[0034] The teaching module includes a basic operation teaching unit, which is used for teachers to control the basic actions of starting, stopping, moving forward, moving backward, and turning of the AGV three-dimensional model in the VR-based AGV theoretical knowledge learning scenario by using the AGV three-dimensional model; it also includes a path planning teaching unit, which is used for teachers to teach the switching algorithms of different navigation modes of the AGV three-dimensional model by using the VR-based AGV theoretical knowledge learning scenario; different navigation modes include one of magnetic navigation, laser navigation, and visual navigation; it also includes a scheduling management teaching unit, which is used for teachers to teach scheduling management strategies in the VR-based AGV theoretical knowledge learning scenario by using the scenario of multiple AGV three-dimensional models working together.

[0035] In the path planning teaching unit, the switching algorithm of the navigation mode includes the following steps:

[0036] S1, Initialization of the AGV three-dimensional model: The AGV three-dimensional model is initialized and the initial navigation mode is loaded.

[0037] S2. The AGV 3D model selects a navigation mode: The AGV 3D model continuously detects the environmental information of the VR-style AGV theoretical knowledge learning scenario; when the AGV 3D model detects a magnetic stripe, it enters the magnetic navigation mode; when the AGV 3D model detects lidar data, it enters the laser navigation mode; when the AGV 3D model detects visual features, it enters the visual navigation mode; the visual features include QR codes.

[0038] S3. The AGV 3D model constructs a path: The AGV 3D model calls the corresponding control algorithm according to the entered navigation mode; when the AGV 3D model enters the magnetic navigation mode, the AGV 3D model travels along the magnetic stripe path and uses the PID control algorithm to adjust the direction; when the AGV 3D model enters the laser navigation mode, the AGV 3D model constructs a map of the VR-style AGV theoretical knowledge learning scenario based on the SLAM algorithm using the environmental information and automatically plans the shortest path from the current position of the AGV 3D model to the end point; when the AGV 3D model enters the visual navigation mode, the AGV 3D model performs positioning based on the YOLO image recognition technology and plans the forward path.

[0039] S4. The switching of the AGV 3D model path: When the AGV 3D model is moving forward, the AGV 3D model continuously monitors the navigation state. When it encounters a lost path, the AGV 3D model switches to another navigation mode.

[0040] Specifically, first, in the intelligent manufacturing major courses of a certain college, the teacher conducts teaching in the basic operation teaching unit. For example, when explaining the AGV start operation, the teacher uses the AGV 3D model and, through VR equipment, enables students to be in a real AGV operation site. In the VR-style AGV theoretical knowledge learning scenario, the teacher clicks the start button, and students can see the AGV 3D model gradually start running from a stationary state, the details of the motor operation, and the rotation of the wheels. When explaining the AGV stop operation, also in the VR-style AGV theoretical knowledge learning scenario, the teacher demonstrates how to make the AGV stop smoothly through the operation interface, and students can intuitively observe the braking process of the AGV.

[0041] In the path planning teaching unit, this embodiment takes a logistics distribution simulation task as an example. In the VR-based AGV theoretical knowledge learning scenario, the teacher sets up a simulated warehouse environment with multiple cargo storage points and shipping outlets. When the teacher selects the magnetic navigation mode, the student sees the AGV 3D model detecting the preset magnetic strip and then driving along the magnetic strip path, using the PID control algorithm to accurately adjust the direction, avoiding obstacles in the warehouse, and transporting the goods from the storage point to the shipping outlet. The PID control algorithm is the proportional (P), integral (I), and derivative (D) control algorithm. Its control principle is based on the deviation between the set value (target direction) and the actual value (current AGV direction). The proportional link quickly responds to the deviation, the integral link eliminates the steady-state error of the system, and the derivative link predicts the change trend of the deviation and adjusts the control amount in advance to make the system quickly stable. In AGV magnetic navigation, the AGV calculates the deviation from the target path based on the detected magnetic strip position information, calculates the control amount through the mathematical expression of the PID control algorithm, and then adjusts the speed and steering of the motor to make the AGV accurately drive along the magnetic strip path. Since the mathematical expression of the PID control algorithm is prior art, this embodiment will not elaborate on it further.

[0042] When switching to the laser navigation mode, the AGV 3D model quickly constructs a warehouse map based on lidar data and automatically plans the shortest path from the current position to the shipping outlet to quickly complete the handling task. The principle of the SLAM algorithm is that in an unknown environment, the AGV moves while obtaining surrounding environment information through a lidar sensor, and at the same time uses this information to locate its own position and construct a map of the surrounding environment. Taking the SLAM algorithm based on graph optimization as an example, it takes the pose of the AGV and the observed environmental feature points as nodes in the graph, and the constraint relationship between the nodes as edges. By optimizing the positions of the nodes in the graph, the error of the entire graph is minimized, thereby obtaining the accurate position of the AGV and the environmental map. In this platform, the AGV uses lidar to scan the surrounding environment, obtains lidar data, constructs a VR-based scene map in real time through the SLAM algorithm, and plans the shortest path from the current position to the end point according to the map information and the target position.

[0043] If the visual navigation mode is selected, the AGV 3D model recognizes the QR code visual features in the warehouse, locates based on the YOLO image recognition technology, plans the forward path, and completes the cargo handling. The YOLO image recognition technology is prior art and will not be elaborated on further in this embodiment.

[0044] Through such an example, students can deeply understand the switching algorithms and application scenarios of different navigation modes.

[0045] In the scheduling management teaching unit, in this embodiment, taking the scenario of a sharp increase in orders during the peak season of e-commerce warehousing as an example, multiple AGV 3D models work together. In the VR scenario, the teacher assigns different order tasks to different AGV 3D models, demonstrating how to reasonably schedule AGV 3D models according to the urgency of orders and the location of goods, and avoid conflicts among multiple AGV 3D models during driving. Students can clearly see the application of scheduling management strategies in the actual scenario, such as the specific operation processes of task allocation, vehicle scheduling, and conflict avoidance, so as to better master scheduling management knowledge.

[0046] The training module is used to build and provide an actual operation environment for AGV hardware assembly, an actual operation environment for connecting AGV hardware control circuits, an actual operation environment for AGV software programming and debugging, and an actual operation environment for task operations simulating real working scenarios;

[0047] The training module includes a cross-domain integration training unit. The cross-domain integration training unit uses 3D modeling technology and VR engines to generate VR-style training scenarios for different industries; students use VR-style training scenarios for different industries to conduct practical training operations for path planning and scheduling management of AGV 3D models; the training scenarios for different industries include one of automobile manufacturing, e-commerce warehousing, and pharmaceutical distribution.

[0048] The training module also includes a fault simulation and troubleshooting unit, which is used to simulate various faults that occur during the operation of AGV 3D models; students perform fault troubleshooting and repair operations on AGV 3D models; various faults include one or more of sensor faults, motor faults, and communication faults.

[0049] Specifically, first, in the actual operation environment for hardware assembly, students carry out operations in the training classroom using real AGV hardware components. For example, a certain student gets components such as the frame, drive wheels, driven wheels, and motors of an AGV. According to the assembly manual, the student first installs the drive wheels and driven wheels at the specified positions on the frame, and then connects the motor to the drive wheels to complete the preliminary mechanical structure construction. Then, the student connects the power supply wire and control wire of the motor to the main control circuit board.

[0050] In the cross-domain integration training unit, in this embodiment, taking the training scenario of the automobile manufacturing industry as an example. Simulating the material distribution link of an automobile production line, a certain student manipulates an AGV 3D model in the VR-style automobile manufacturing training scenario and needs to transport specific types of automobile parts from the warehouse to the designated workstation on the production line. The student needs to plan an optimal path for the AGV 3D model that avoids other vehicles and obstacles and can quickly reach the workstation according to the real-time requirements on the automobile production line, using the practical training operation knowledge of path planning learned in the path planning teaching unit.

[0051] Meanwhile, the student also needs to coordinate the operation of multiple AGV 3D models according to the scheduling management strategy to avoid collisions and blockages in the narrow production workshop and ensure the efficient progress of material distribution.

[0052] In the fault simulation and troubleshooting unit, when simulating a sensor fault, the AGV 3D model controlled by a certain student showed a positioning deviation during operation. By observing the running state of the AGV 3D model and the system prompt information, the bachelor initially judged that it might be a laser sensor fault. The student first checked whether the connection line of the sensor was loose, and then used the detection tool provided by the platform to detect the sensor. After determining that the internal components of the sensor were damaged, a new laser sensor was replaced and recalibrated, and finally the AGV 3D model resumed normal operation. In this process, the student not only mastered the method of troubleshooting but also had a deeper understanding of the working principle of the AGV sensor.

[0053] The multi-person online collaboration module includes an online collaboration platform; different students log in to the online collaboration platform using their personal accounts. After logging in, different students use the online collaboration platform to remotely operate the same or multiple AGV 3D models in the same VR training scenarios of different industries at the same time.

[0054] Specifically, first, in the intelligent manufacturing practice course of a certain university, the teacher assigned a complex logistics warehouse simulation task. Students A, B, and C logged in to the online collaboration platform using their respective accounts and entered the VR training scenarios of different industries. In this embodiment, the e-commerce warehousing training scenario is taken as an example. There is a large virtual logistics warehouse in the e-commerce warehousing training scenario, storing various goods, which need to be transported to different shipping outlets.

[0055] Student A is responsible for the overall task planning. According to the order information and warehouse layout, the handling routes and target shipping outlets of each cargo are marked on the platform. Student B controls an AGV 3D model and starts to transport goods from Area A of the warehouse. During the transportation process, there is a conflict with the driving route of another AGV 3D model. Student B immediately informs Students A and C through the voice communication function of the platform.

[0056] Student C then operates another AGV 3D model. Originally planned to transport goods from Area B, but after receiving the conflict information, he quickly adjusted the driving route of his AGV 3D model to avoid Student B's AGV 3D model. At the same time, Student A re-optimized the task allocation and vehicle scheduling plan according to the actual situation and sent new instructions to Students B and C through the online collaboration platform.

[0057] In this process, students continuously communicated through the text chat and voice functions of the online collaboration platform, shared information in real time, jointly solved the problems encountered, and finally efficiently completed the task of moving goods in the logistics warehouse. Through such collaboration, students not only improved their proficiency in AGV operation but also realized the importance of teamwork in actual work.

[0058] It also includes a competition module; the competition module is used to hold competitions for the practical operation of path planning and dispatching management of the AGV three-dimensional model by using the training module and the multi-person online collaboration module; the competition includes a speed competition and a complex task challenge competition.

[0059] Specifically, first, in an intelligent AGV training competition held in a vocational college, in the speed competition session, the track was set up as an environment simulating an e-commerce warehouse, with various shelves and obstacles. A student manipulated the AGV three-dimensional model and competed with other contestants on the online platform built by the multi-person online collaboration module. The competition required the AGV three-dimensional model to start from the starting point of the warehouse, quickly pass through the curved passage, and accurately move the goods to the designated shipping outlet within the specified time. The student analyzed the track environment in advance by using the knowledge of the practical operation of path planning and planned a theoretically fastest driving route. After the competition started, he skillfully manipulated the AGV three-dimensional model, used the PID control algorithm to accurately turn, and completed the goods handling at the fastest speed.

[0060] In the present invention, by using 3D modeling technology and VR engine to construct the teaching module, the abstract AGV theoretical knowledge is transformed into an intuitive VR-style learning scenario, breaking the limitations of traditional teaching relying only on PPT presentations and model observations, enabling students to learn immersively, greatly improving students' understanding and mastery of AGV theoretical knowledge, and making the learning process more vivid and efficient; the training module provides a comprehensive practical environment, covering hardware assembly, circuit connection, software programming and debugging, and task operations simulating real working scenarios, and has a cross-domain integration training unit, generating VR-style training scenarios for different industries, enabling students to deeply understand the application characteristics of AGV in different industries such as automobile manufacturing, e-commerce warehousing, and pharmaceutical distribution, conducting targeted path planning and dispatching management training, and comprehensively improving students' practical operation ability, so that they can quickly adapt to the work requirements of different industries after graduation.

[0061] Obviously, the above embodiments are only examples given for clear illustration and are not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. And the obvious changes or modifications derived therefrom are still within the protection scope of the present invention.

Claims

1. Design of an intelligent AGV training and teaching platform based on digital technology, characterized in that, Including: A teaching module for constructing a teaching database by using an electronic database of AGV theoretical knowledge. Using 3D modeling technology and a VR engine, converting the content in the teaching database into a 3D model of an AGV and a virtual teaching scenario, and constructing a VR-style AGV theoretical knowledge learning scenario by using the 3D model of the AGV and the virtual teaching scenario. A training module for building and providing a practical operation environment for AGV hardware assembly, a practical operation environment for connecting AGV hardware control circuits, a practical operation environment for AGV software programming and debugging, and a practical operation environment for task operations simulating a real working scenario. The training module includes a cross-domain integration training unit. The cross-domain integration training unit is used to generate VR-style training scenarios for different industries by using 3D modeling technology and a VR engine. Students use the VR-style training scenarios for different industries to carry out practical training operations for path planning and dispatching management of the 3D model of the AGV. The VR-style training scenarios for different industries include one of automobile manufacturing, e-commerce warehousing, and pharmaceutical distribution. A multi-person online collaboration module, including an online collaboration platform. Different students log in to the online collaboration platform using their personal accounts. After logging in, different students use the online collaboration platform to remotely operate the same or multiple 3D models of AGVs in the same VR-style training scenario for different industries at the same time.

2. The design of the intelligent AGV training and teaching platform based on digital technology according to claim 1, characterized in that, The teaching module includes a basic operation teaching unit. The basic operation teaching unit is used for teachers to teach the control of basic actions such as starting, stopping, moving forward, moving backward, and turning of the 3D model of the AGV in the VR-style AGV theoretical knowledge learning scenario by using the 3D model of the AGV.

3. The design of the intelligent AGV training and teaching platform based on digital technology according to claim 2, wherein, The teaching module also includes a path planning teaching unit. The path planning teaching unit is used for teachers to teach the switching algorithm of different navigation modes of the 3D model of the AGV by using the VR-style AGV theoretical knowledge learning scenario. Different navigation modes include one of magnetic navigation, laser navigation, and visual navigation.

4. The design of the intelligent AGV training and teaching platform based on digital technology according to claim 3, characterized in that, The teaching module also includes a dispatching management teaching unit. The dispatching management teaching unit is used for teachers to teach dispatching management strategies by using the scenario of multiple 3D models of AGVs working together in the VR-style AGV theoretical knowledge learning scenario.

5. The design of the intelligent AGV training and teaching platform based on digital technology according to claim 4, characterized in that, The training module also includes a fault simulation and troubleshooting unit. The fault simulation and troubleshooting unit is used to simulate various faults that occur during the operation of the 3D model of the AGV. Students carry out fault troubleshooting and repair operations on the 3D model of the AGV.

6. The design of the intelligent AGV training and teaching platform based on digital technology according to claim 5, characterized in that, In the path planning teaching unit, the switching algorithm of the navigation mode includes the following steps: S1, Initialization of the 3D model of the AGV: Initialize the 3D model of the AGV and load the initial navigation mode. S2, Selection of the navigation mode by the 3D model of the AGV: The 3D model of the AGV continuously detects the environmental information of the VR-style AGV theoretical knowledge learning scenario. When the 3D model of the AGV detects a magnetic strip, it enters the magnetic navigation mode. When the 3D model of the AGV detects lidar data, it enters the laser navigation mode. When the 3D model of the AGV detects visual features, it enters the visual navigation mode. Visual features include QR codes. S3, AGV 3D model construction path: The AGV 3D model calls the corresponding control algorithm according to the entered navigation mode; when the AGV 3D model enters the magnetic navigation mode, the AGV 3D model travels along the magnetic strip path and uses the PID control algorithm to adjust the direction; when the AGV 3D model enters laser navigation, the AGV 3D model is based on the SLAM algorithm, uses environmental information to construct a map of the VR-style AGV theoretical knowledge learning scenario and automatically plans the shortest path from the current position of the AGV 3D model to the end point; when the AGV 3D model enters visual navigation, the AGV 3D model performs positioning based on the YOLO image recognition technology and plans the forward path. S4, Switching of the AGV 3D model path: When the AGV 3D model is moving forward, the AGV 3D model monitors the navigation state in real time. When a lost path is encountered, the AGV 3D model switches to another navigation mode.

7. The design of the intelligent AGV training and teaching platform based on digital technology according to claim 6, characterized in that, In the scheduling management teaching unit, the scheduling management strategies include task allocation, vehicle scheduling, and conflict avoidance.

8. The design of the intelligent AGV training and teaching platform based on digital technology according to claim 7, characterized in that, In the fault simulation and troubleshooting unit, various faults include one or more of sensor faults, motor faults, and communication faults.

9. The design of the intelligent AGV training and teaching platform based on digital technology according to claim 8, wherein, It also includes a competition module; the competition module is used to hold competitions for the practical operation of path planning and scheduling management of the AGV 3D model by using the training module and the multi-person online collaboration module.

10. The design of the intelligent AGV training and teaching platform based on digital technology according to claim 9, characterized in that, In the competition module, the competitions include speed competitions and complex task challenge competitions.

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