Unmanned aerial vehicle driving training method and system and electronic equipment

Abstract

The invention relates to the technical field of unmanned aerial vehicle driving training, and discloses an unmanned aerial vehicle driving training method and system, and electronic equipment, and the method comprises the steps: a trainee selects a simulated meteorological environment, generates a training view, carries out the training through the training view, generates operation information, and a teacher carries out the training guidance, and generates guidance information. The system and the electronic equipment correspond to the method. According to the application, the reality sense and the simulation effect are improved by simulating the meteorological environment, the student viewing angle, the instructor viewing angle and the channel viewing angle of the training view are displayed on the same screen to improve the mastering of the student on the driving process, and the instructor observes the student training process in real time through the communication connection of the VR display device. A technical basis is provided for real-time guidance by accessing a teacher into a training environment of the student, the channel change of the unmanned aerial vehicle is sensed in real time from a channel perspective, and training links are supplemented. And finally, optimization of unmanned aerial vehicle driving training is realized.

Description

Technical Field

[0001] The present application relates to the technical field of drone driving training, and specifically to a drone driving training method, system and electronic equipment. Background Art

[0002] With the development of low-altitude economy and UAV technology, the demand for UAV pilots is also increasing, so research on UAV driving training is necessary.

[0003] VR technology can build a virtual environment for drone driving training, which is helpful for the development of drone driving training. Therefore, it is necessary to study the application of VR technology in the field of drone driving training.

[0004] However, when the inventor studied the application of VR technology in the field of drone driving training, he found that the existing technology had the following problems:

[0005] 1. The virtual training environment constructed using VR technology has poor realism.

[0006] 2. When training is conducted in a virtual training environment constructed using VR technology, only the trainee’s main perspective is provided. The instructor cannot access the trainee’s virtual training environment, and the single perspective makes it difficult for the trainee to grasp the driving process.

[0007] 3. Trainees cannot feel the changes in the drone’s flight path in the virtual training environment, resulting in the loss of an important part of the training.

[0008] In summary, existing technologies are in urgent need of a technical solution to optimize drone driving training. Summary of the invention

[0009] The purpose of this application is to provide a drone driving training method, system and electronic equipment to solve the technical problems raised in the above background technology.

[0010] To achieve the above objectives, this application discloses the following technical solutions:

[0011] In a first aspect, the present application discloses a drone driving training method, the method comprising:

[0012] S1: The trainee wears the first VR display device and selects the simulated meteorological environment;

[0013] S2: The waterway simulation model generates a training view based on the simulated meteorological environment;

[0014] S3: The trainee performs training using the VR operating device that is communicatively connected to the first VR display device and the training view, and generates corresponding operation information;

[0015] S4: The instructor wears a second VR display device, obtains the training view in step S3, provides training guidance to the trainees and generates corresponding guidance information;

[0016] Wherein: the simulated meteorological environment is generated based on the historical real meteorological environment; the waterway simulation model is used to generate the corresponding training views based on different simulated meteorological environments; the training views include the trainee perspective, the instructor perspective and the waterway perspective; the operation information is specifically the control instructions issued by the trainee through the VR operation device during the training;

[0017] The first VR display device is communicatively connected to the second VR display device.

[0018] Preferably, the method further comprises:

[0019] A1: construct the waterway simulation model;

[0020] A2: Generate the training view using the waterway simulation model;

[0021] Among them: the training view also includes a corresponding displayed simulated waterway and simulated view background; the student perspective is the student's main perspective and the instructor's first secondary perspective during training; the instructor view is the student's first secondary perspective and the instructor's main perspective during training; the waterway view is the second secondary perspective of the student and the instructor during training.

[0022] Preferably, the construction of the waterway simulation model specifically includes:

[0023] Acquire the historical real weather environment and the disturbance that occurs when the corresponding UAV flies in the real weather environment, and capture the disturbance;

[0024] Constructing a first correspondence between the historical real meteorological environment and the disturbance, wherein the first correspondence is used to match the corresponding disturbance using the historical real meteorological environment;

[0025] Performing feature recognition on the historical real meteorological environment to obtain the simulated meteorological environment;

[0026] Using the simulated meteorological environment to update the first corresponding relationship to obtain a second corresponding relationship, wherein the second corresponding relationship is used to match the corresponding disturbance using the simulated meteorological environment;

[0027] Storing the simulated meteorological environment and the second corresponding relationship in a preset waterway simulation learning model;

[0028] The waterway simulation learning model is trained to obtain the waterway simulation model.

[0029] Preferably, the training of the waterway simulation learning model specifically includes:

[0030] Training the waterway simulation learning model performs the following steps:

[0031] B1: Acquire the simulated meteorological environment;

[0032] B2: Filling the simulated view background with the simulated meteorological environment;

[0033] B3: using the simulated meteorological environment and the second corresponding relationship to match the corresponding disturbance;

[0034] B4: Acquire the training course and the operation information of the trainee, and update the training course using the disturbance and the operation information obtained in step B3 to obtain the simulated course;

[0035] B5: generating the training view based on the simulated waterway and the simulated view background, and displaying the training view in the trainee's perspective, the instructor's perspective and the waterway perspective accordingly;

[0036] B6: Repeat steps B1 to B5 until the accuracy of the training view output by the channel simulation learning model is greater than a preset model accuracy threshold, and output the channel simulation learning model as the channel simulation model.

[0037] Preferably, the method of generating the training view by using the waterway simulation model specifically comprises the following steps:

[0038] C1: the waterway simulation model receives the simulated meteorological environment selected by the trainee;

[0039] C2: running the waterway simulation model to generate the simulated waterway and the simulated view background corresponding to the simulated meteorological environment obtained in step C1;

[0040] C3: Generate the training view using the simulated waterway and the simulated view background obtained in step C2 and display it on the first VR display device and the second VR display device;

[0041] C4: Acquire the operation information, repeat steps C2 and C3 to update the simulated waterway, and then update the training view displayed on the first VR display device and the second VR display device.

[0042] Preferably, the trainee uses the training view for training, specifically including:

[0043] The trainee obtains the training view through the first VR display device;

[0044] Using the trainee's perspective to obtain the result of the simulated UAV executing the operation information in the simulated meteorological environment;

[0045] obtaining the guidance information using the instructor's perspective;

[0046] The simulated channel of the operation information in the simulated meteorological environment is acquired by using the channel viewing angle.

[0047] Preferably, the obtaining of the training view in step S3 specifically includes:

[0048] The second VR display device worn by the instructor obtains the trainee's training view in real time through a communication connection with the first VR display device worn by the trainee, and switches between the main perspective and the secondary perspective to obtain the training view displayed in the corresponding second VR display device worn by the instructor.

[0049] Preferably, the generating corresponding guidance information specifically includes:

[0050] The instructor obtains the training view through a second VR display device;

[0051] Obtain and observe the results of the student's simulated drone executing the operation information in the simulated weather environment from the student's perspective, and generate the corresponding guidance information when guidance is needed;

[0052] inputting the guidance information using the instructor perspective;

[0053] The simulated waterway in the simulated weather environment is obtained and observed using the waterway perspective for the trainee's operation information.

[0054] In a second aspect, the present application discloses a drone driving training system, which is applicable to the drone driving training method as described above, and the system comprises:

[0055] A simulated meteorological environment acquisition module, wherein the simulated meteorological environment acquisition module is configured for the trainee to wear the first VR display device and select the simulated meteorological environment before uploading;

[0056] A training view generation module, wherein the training view generation module is configured to generate a training view based on the simulated meteorological environment by a waterway simulation model and then upload it;

[0057] A training module, wherein the training module is configured such that trainees use the training view for training and generate corresponding operation information and then upload it;

[0058] A guidance module, wherein the guidance module is configured as follows: the instructor wears a second VR display device, the second VR display device obtains the training view in the training module, provides training guidance to the trainees, generates corresponding guidance information, and then uploads it;

[0059] Wherein: the simulated meteorological environment is generated based on the historical real meteorological environment; the waterway simulation model is used to generate the corresponding training views based on different simulated meteorological environments; the training views include the trainee perspective, the instructor perspective and the waterway perspective; the operation information is specifically the control instructions issued by the trainee through the VR operation device during the training;

[0060] The first VR display device is communicatively connected to the second VR display device.

[0061] In a third aspect, the present application discloses an electronic device, comprising: at least one processor and at least one memory, wherein the memory is communicatively connected to the processor; the memory stores a computer program that can be executed by the processor, and when the computer program is executed by the processor, the drone driving training method as described above is implemented.

[0062] Beneficial effects: The drone driving training method, system and electronic equipment of the present application achieve enhanced realism and simulation effects by simulating meteorological environments, improve students' grasp of the driving process by displaying the student perspective, instructor perspective and channel perspective in the training view on the same screen, enable instructors to observe the student training process in real time through the communication connection of VR display devices, connect instructors to the student's training environment, provide a technical basis for real-time guidance, and achieve real-time perception of drone channel changes from the channel perspective, thereby completing the training links and optimizing drone driving training. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0064] Figure 1 A flowchart of the drone driving training method provided in the embodiment of the present application;

[0065] Figure 2 This is a structural block diagram of the drone driving training system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0066] The technical solutions in the embodiments of the present application are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0067] In this article, the term "comprising" is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of more restrictions, the elements defined by the sentence "comprising..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0068] In a first aspect, this embodiment discloses Figure 1 A drone driving training method is shown, which is implemented using VR technology and corresponding display equipment and operating equipment, and the method includes:

[0069] S1: The trainee wears the first VR display device and selects the simulated weather environment.

[0070] S2: The channel simulation model generates training views based on the simulated meteorological environment.

[0071] S3: Trainees use the training view for training and generate corresponding operation information.

[0072] S4: The instructor wears the second VR display device, obtains the training view in step S3, provides training guidance to the trainees and generates corresponding guidance information.

[0073] Wherein: the simulated meteorological environment is generated based on the historical real meteorological environment. The waterway simulation model is used to generate corresponding training views based on different simulated meteorological environments. The training views include the trainee perspective, the instructor perspective and the waterway perspective. The operation information is specifically the control instructions issued by the trainee through the VR operation device connected to the first VR display device during the training.

[0074] The first VR display device is communicatively connected to the second VR display device.

[0075] It should be noted that the process of generating the training view and the operation information in this embodiment adopts the imaging technology and the interaction technology in the existing VR technology.

[0076] Specifically, the method further includes:

[0077] A1: Construct a waterway simulation model.

[0078] A2: Generate training views using the waterway simulation model.

[0079] Among them: the training view also includes the corresponding simulated waterway and simulated view background. The student perspective is the primary perspective of the student and the first perspective of the instructor during training. The instructor view is the first perspective of the student and the primary perspective of the instructor during training. The waterway view is the second perspective of the student and the instructor during training.

[0080] This embodiment realizes real-time updating of the simulated waterway of trainees based on the simulated meteorological environment and operation information by constructing a waterway simulation model, thereby providing greater authenticity in training in a virtual environment.

[0081] Specifically, the waterway simulation model is constructed, including:

[0082] Obtain the historical real weather environment and the disturbance that occurs when the corresponding drone flies in the real weather environment, and capture the disturbance.

[0083] A first correspondence between the historical real meteorological environment and the disturbance is constructed, and the first correspondence is used to match the corresponding disturbance using the historical real meteorological environment. In this embodiment, the disturbance is specifically the change in the flight state of the drone caused by the meteorological environment. For example, the change in the flight state of the drone under strong wind conditions is the disturbance. By capturing and recording the disturbance, a data basis is provided for the optimization of the simulated route during the training of trainees.

[0084] The feature recognition of the historical real meteorological environment is carried out to obtain the simulated meteorological environment. It should be noted that the feature recognition technology used here is an existing feature recognition technology, which aims to realize the mapping of the real meteorological environment by refining it in the virtual environment, and provide a data basis for improving the realism of students in the virtual environment.

[0085] The first corresponding relationship is updated by using the simulated meteorological environment to obtain a second corresponding relationship, and the second corresponding relationship is used to match the corresponding disturbance by using the simulated meteorological environment.

[0086] The simulated meteorological environment and the second corresponding relationship are stored in a preset waterway simulation learning model.

[0087] The waterway simulation learning model is trained to obtain a waterway simulation model.

[0088] Specifically, the training channel simulation learning model includes:

[0089] Training the waterway simulation learning model performs the following steps:

[0090] B1: Obtain simulated meteorological environment.

[0091] B2: Filling the background of the simulated view using the simulated meteorological environment. In this embodiment, background images under different meteorological environments are preset, and the simulated view background, i.e., the background screen of the training view, is filled by matching the corresponding background image with the simulated meteorological environment. For example, if the simulated meteorological environment selected by the trainee is a sunny day, the background image of the sunny day is adaptively filled in the background image.

[0092] B3: Use the simulated meteorological environment and the second correspondence to match the corresponding disturbance.

[0093] B4: Obtain the training course and the trainee's operation information, and use the disturbance and operation information obtained in step B3 to update the training course to obtain a simulated course. It should be noted that the training course here is a preset course for training, and the training course provides a data basis for the optimization of the simulated course. The specific principle for obtaining the simulated course here is: the trainees are trained based on the training course, and under the influence of the simulated meteorological environment and operation information, the course driven by the trainees deviates from the training course, and the course driven by the trainees is the simulated course. The deviation between the simulated course and the training course is displayed in the course perspective, which helps trainees and instructors to observe the driving results in real time and improve the quality of training.

[0094] B5: Generate a training view based on the simulated waterway and simulated view background, and display it correspondingly in the student perspective, instructor perspective and waterway perspective.

[0095] B6: Repeat steps B1 to B5 until the accuracy of the training view output by the waterway simulation learning model is greater than a preset model accuracy threshold, and output the waterway simulation learning model as the waterway simulation model.

[0096] Specifically, the training view is generated using the waterway simulation model, which includes the following steps:

[0097] C1: The waterway simulation model receives the simulated meteorological environment selected by the trainees.

[0098] C2: Run the waterway simulation model to generate a simulated waterway and simulated view background corresponding to the simulated meteorological environment obtained in step C1.

[0099] C3: Generate the training view using the simulated waterway and the simulated view background obtained in step C2 and display it on the first VR display device and the second VR display device.

[0100] C4: Acquire the operation information, repeat steps C2 and C3 to update the simulated waterway, and then update the training view displayed on the first VR display device and the second VR display device.

[0101] This embodiment uses a waterway simulation model to perform real-time updates on the simulated waterway, providing a data basis for trainees to conduct training and instructors to conduct guidance, thereby achieving an improvement in the sense of reality and the completion of training links.

[0102] Specifically, trainees use the training view for training, including:

[0103] The trainee obtains the training view through the first VR display device;

[0104] Using the trainee's perspective to obtain the result of the simulated UAV executing the operation information in the simulated meteorological environment;

[0105] obtaining the guidance information using the instructor's perspective;

[0106] The simulated channel of the operation information in the simulated meteorological environment is acquired by using the channel viewing angle.

[0107] In this embodiment, the multi-perspective display provides a data basis for trainees to improve their grasp of driving and provides technical support for improving the quality of training.

[0108] Specifically, obtaining the training view in step S3 includes:

[0109] The second VR display device worn by the instructor obtains the trainee's training view in real time through a communication connection with the first VR display device worn by the trainee, and switches between the main perspective and the secondary perspective to obtain the training view displayed in the corresponding second VR display device worn by the instructor.

[0110] It should be noted that the training view in the VR display device worn by the instructor is the same as the training view in the VR display device worn by the trainee, and only the content displayed in the main perspective and the first secondary perspective has changed. By connecting the instructor to the virtual environment where the trainee is located, technical support is provided for the instructor to provide real-time guidance to the trainee. Furthermore, the communication connection between the VR display device worn by the instructor and the VR display device worn by the trainee provides technical support for realizing training in different spaces, that is, realizing remote training, which greatly improves the flexibility of trainees in training.

[0111] Specifically, the corresponding guidance information is generated, including:

[0112] The instructor obtains the training view through a second VR display device;

[0113] Use the trainee's perspective to obtain and observe the results of the trainee's simulated UAV executing operation information in the simulated weather environment, and generate corresponding guidance information when guidance is needed;

[0114] Input guidance information using the instructor's perspective. It should be noted that the guidance information here can be in the form of, but not limited to, text or audio. Trainees can obtain guidance from instructors in real time through guidance information, thereby improving the quality of training;

[0115] Use the course perspective to obtain and observe trainees’ operational information on a simulated course in a simulated weather environment.

[0116] In a second aspect, this embodiment discloses Figure 2 A drone driving training system is shown, and the system is applicable to the drone driving training method as described above, and the system includes:

[0117] The simulated meteorological environment acquisition module is configured so that the trainee wears the first VR display device and selects the simulated meteorological environment before uploading.

[0118] The training view generation module is configured to generate a training view based on the simulated meteorological environment by the waterway simulation model and then upload it.

[0119] Training module, the training module is configured as follows: trainees use the training view for training and generate corresponding operation information and then upload it.

[0120] The guidance module is configured as follows: the instructor wears a second VR display device, the second VR display device obtains the training view in the training module, provides training guidance to the trainees, generates corresponding guidance information and then uploads it.

[0121] Among them: The simulated meteorological environment is generated based on the historical real meteorological environment. The waterway simulation model is used to generate corresponding training views based on different simulated meteorological environments. The training views include the trainee perspective, the instructor perspective and the waterway perspective. The operation information is specifically the control instructions issued by the trainees through the VR operating device during training.

[0122] The first VR display device is communicatively connected to the second VR display device.

[0123] It should be noted that the drone driving training system of this embodiment corresponds to the drone driving training method mentioned above. Therefore, the contents not specifically described in the drone driving training system of this embodiment may have, but are not limited to, the same functional definition, working principle and technical effects as the drone driving training method mentioned above, and this text will not elaborate on them here.

[0124] In a third aspect, this embodiment discloses an electronic device, including: at least one processor and at least one memory, the memory being communicatively connected to the processor. The memory stores a computer program that can be executed by the processor, and when the computer program is executed by the processor, the drone driving training method described above is implemented.

[0125] In summary, the UAV driving training method, system and electronic equipment of the present embodiment achieve the improvement of realism and simulation effect by simulating the meteorological environment, improve the students' grasp of the driving process by displaying the students' perspective, instructor's perspective and channel perspective on the same screen of the training view, realize the instructor's real-time observation of the students' training process through the communication connection of the VR display device, connect the instructor to the students' training environment, provide a technical basis for real-time guidance, realize real-time perception of the UAV's channel changes from the channel perspective, complete the training links, and realize the optimization of UAV driving training.

[0126] In the embodiments provided in the present application, it should be understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code or any appropriate combination thereof. For hardware implementation, the processor can be implemented in one or more of the following units: application specific integrated circuit (ASIC), digital signal processor (DSP), digital signal processing device (DSPD), programmable logic device (PLD), field programmable gate array (FPGA), processor, controller, microcontroller, microprocessor, other electronic units designed to implement the functions described herein or their combination. For software implementation, part or all of the flow of the embodiment can be completed by instructing the relevant hardware through a computer program. When implemented, the above program can be stored in a computer-readable storage medium or transmitted as one or more instructions or codes on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein the communication medium includes any medium that is convenient for transmitting a computer program from one place to another. The storage medium can be any available medium that a computer can access. The computer-readable storage medium can include but is not limited to RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer.

[0127] Finally, it should be noted that the above is only a preferred embodiment of the present application and is not intended to limit the present application. Although the present application has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A drone driving training method, characterized in that: The method includes: S1: The trainee wears the first VR display device and selects the simulated meteorological environment; S2: The waterway simulation model generates a training view based on the simulated meteorological environment; S3: The trainee performs training using the VR operating device that is communicatively connected to the first VR display device and the training view, and generates corresponding operation information; S4: The instructor wears a second VR display device, obtains the training view in step S3, provides training guidance to the trainees and generates corresponding guidance information; Wherein: the simulated meteorological environment is generated based on the historical real meteorological environment; the waterway simulation model is used to generate the corresponding training views based on different simulated meteorological environments; the training views include the trainee perspective, the instructor perspective and the waterway perspective; the operation information is specifically the control instructions issued by the trainee through the VR operation device during the training; The first VR display device is communicatively connected to the second VR display device.

2. The drone driving training method according to claim 1, characterized in that: The method further includes: A1: construct the waterway simulation model; A2: Generate the training view using the waterway simulation model; Among them: the training view also includes a corresponding displayed simulated waterway and simulated view background; the student perspective is the student's main perspective and the instructor's first secondary perspective during training; the instructor view is the student's first secondary perspective and the instructor's main perspective during training; the waterway view is the second secondary perspective of the student and the instructor during training.

3. The drone driving training method according to claim 2, characterized in that: The construction of the waterway simulation model specifically includes: Acquire the historical real weather environment and the disturbance that occurs when the corresponding UAV flies in the real weather environment, and capture the disturbance; Constructing a first correspondence between the historical real meteorological environment and the disturbance, wherein the first correspondence is used to match the corresponding disturbance using the historical real meteorological environment; Performing feature recognition on the historical real meteorological environment to obtain the simulated meteorological environment; Using the simulated meteorological environment to update the first corresponding relationship to obtain a second corresponding relationship, wherein the second corresponding relationship is used to match the corresponding disturbance using the simulated meteorological environment; Storing the simulated meteorological environment and the second corresponding relationship in a preset waterway simulation learning model; The waterway simulation learning model is trained to obtain the waterway simulation model.

4. The drone driving training method according to claim 3, characterized in that: The training of the waterway simulation learning model specifically includes: Training the waterway simulation learning model performs the following steps: B1: Acquire the simulated meteorological environment; B2: Filling the simulated view background with the simulated meteorological environment; B3: using the simulated meteorological environment and the second corresponding relationship to match the corresponding disturbance; B4: Acquire the training course and the operation information of the trainee, and update the training course using the disturbance and the operation information obtained in step B3 to obtain the simulated course; B5: generating the training view based on the simulated waterway and the simulated view background, and displaying the training view in the trainee's perspective, the instructor's perspective and the waterway perspective accordingly; B6: Repeat steps B1 to B5 until the accuracy of the training view output by the channel simulation learning model is greater than a preset model accuracy threshold, and output the channel simulation learning model as the channel simulation model.

5. The drone driving training method according to claim 3, characterized in that: The method of generating the training view by using the waterway simulation model specifically includes the following steps: C1: the waterway simulation model receives the simulated meteorological environment selected by the trainee; C2: running the waterway simulation model to generate the simulated waterway and the simulated view background corresponding to the simulated meteorological environment obtained in step C1; C3: Generate the training view using the simulated waterway and the simulated view background obtained in step C2 and display it on the first VR display device and the second VR display device; C4: Acquire the operation information, repeat steps C2 and C3 to update the simulated waterway, and then update the training view displayed on the first VR display device and the second VR display device.

6. The drone driving training method according to claim 1, characterized in that: The trainees use the training view for training, which specifically includes: The trainee obtains the training view through the first VR display device; Using the trainee's perspective to obtain the result of the simulated UAV executing the operation information in the simulated meteorological environment; obtaining the guidance information using the instructor's perspective; The simulated channel of the operation information in the simulated meteorological environment is acquired by using the channel viewing angle.

7. The drone driving training method according to claim 1, characterized in that: The obtaining of the training view in step S3 specifically includes: The second VR display device worn by the instructor obtains the trainee's training view in real time through a communication connection with the first VR display device worn by the trainee, and switches between the main perspective and the secondary perspective to obtain the training view displayed in the corresponding second VR display device worn by the instructor.

8. The drone driving training method according to claim 1, characterized in that: The generating corresponding guidance information specifically includes: The instructor obtains the training view through a second VR display device; Obtain and observe the results of the student's simulated drone executing the operation information in the simulated weather environment from the student's perspective, and generate the corresponding guidance information when guidance is needed; inputting the guidance information using the instructor perspective; The simulated waterway in the simulated weather environment is obtained and observed using the waterway perspective for the trainee's operation information.

9. A drone driving training system, the system being applicable to the drone driving training method according to any one of claims 1 to 8, characterized in that: The system includes: A simulated meteorological environment acquisition module, wherein the simulated meteorological environment acquisition module is configured for the trainee to wear the first VR display device and select the simulated meteorological environment before uploading; A training view generation module, wherein the training view generation module is configured to generate a training view based on the simulated meteorological environment by a waterway simulation model and then upload it; A training module, wherein the training module is configured such that trainees use the training view for training and generate corresponding operation information and then upload it; A guidance module, wherein the guidance module is configured as follows: the instructor wears a second VR display device, the second VR display device obtains the training view in the training module, provides training guidance to the trainees, generates corresponding guidance information, and then uploads it; Wherein: the simulated meteorological environment is generated based on the historical real meteorological environment; the waterway simulation model is used to generate the corresponding training views based on different simulated meteorological environments; the training views include the trainee perspective, the instructor perspective and the waterway perspective; the operation information is specifically the control instructions issued by the trainee through the VR operation device during the training; The first VR display device is communicatively connected to the second VR display device.

10. An electronic device, characterized in that: include: At least one processor and at least one memory, the memory being communicatively connected to the processor; the memory storing a computer program that can be executed by the processor, and when the computer program is executed by the processor, the drone driving training method according to any one of claims 1 to 8 is implemented.

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