Full virtual simulation method and device for unmanned aerial vehicle

Abstract

The invention provides a full virtual simulation method and device for an unmanned aerial vehicle, and relates to the technical field of simulation. The method comprises the following steps: constructing an unmanned aerial vehicle system model and a servo control system model by using a modeling platform GCKontrol, constructing an image recognition system model by using Python codes on the basis of a Jupter function in a simulation platform GCAir, importing the unmanned aerial vehicle system model and the servo control system model into the simulation platform GCAir, and finally, constructing an image recognition system model by using a Python code on the basis of the Jupter function in the simulation platform GCAir. And simulating the unmanned aerial vehicle through the unmanned aerial vehicle system model, the servo control system model and the image recognition system model. Therefore, on the basis of the JUPter function in the GCKontrol, the Python code is utilized to construct the image recognition system model, the construction of the image recognition system model is realized, and the simulation platform GCAir is utilized to perform simulation, so that the full virtual simulation of the unmanned aerial vehicle is realized.

Description

Technical Field

[0001] The present application relates to the field of simulation technology, and in particular to a full virtual simulation method and device for an unmanned aerial vehicle. Background Art

[0002] Drone technology has been deeply applied in various fields, such as aerial photography and military fields. In the existing technology, it is difficult to achieve full virtual simulation of the image system and control system on the same platform. In the past, the image system used real objects, and the control system used models. The real objects were used to test and iterate the products, which was extremely costly and prone to property losses and safety issues.

[0003] In summary, how to achieve full virtual simulation of UAVs is a problem that technical personnel in this field urgently need to solve. Summary of the invention

[0004] In view of this, the present application provides a full virtual simulation method and device for a drone, aiming to achieve full virtual simulation of a drone.

[0005] In a first aspect, the present application provides a full virtual simulation method of a drone, comprising:

[0006] Use the modeling platform GCKontrol to build the UAV system model and servo control system model;

[0007] Based on the Jupyter function in the simulation platform GCAir, the image recognition system model was constructed using Python code;

[0008] Importing the drone system model and the servo control system model into the simulation platform GCAir;

[0009] The drone is simulated by using the drone system model, the servo control system model and the image recognition system model.

[0010] Optionally, simulating the drone through the drone system model, the servo control system model, and the image recognition system model includes:

[0011] Get the view provided by FlightGear software;

[0012] Inputting the visual scene into the image recognition system model to obtain the misalignment angle;

[0013] Inputting the misalignment angle into the servo control system model to obtain the line of sight angular velocity;

[0014] The line of sight angular velocity is input into the UAV system model to realize the simulation of the UAV.

[0015] Optionally, inputting the scene into the image recognition system model to obtain the misalignment angle includes:

[0016] Inputting the visual scene into the image recognition system model to obtain a target;

[0017] The target is tracked to obtain the misalignment angle.

[0018] Optionally, inputting the scene into the image recognition system model to obtain a target includes:

[0019] Inputting the visual scene into the image recognition system model;

[0020] In response to the scene being input into the image recognition system model, selecting a working state of the image recognition system model; the working state is automatic recognition or manual selection;

[0021] When the working state is automatic identification, target detection is performed on the visual scene to obtain the target;

[0022] When the working state is manual selection, target selection is performed on the scene to obtain the target.

[0023] Optionally, inputting the misalignment angle into the servo control system model to obtain the line of sight angular velocity includes:

[0024] Controlling the working state of the servo control system model to be self-stabilizing; the working state of the servo control system model to be self-stabilizing or tracking;

[0025] Inputting the misalignment angle into the servo control system model;

[0026] According to the misalignment angle, the working state of the servo control system model is switched to tracking to obtain the achieved angular velocity.

[0027] Optionally, inputting the line of sight angular velocity into a drone system model to achieve simulation of the drone includes:

[0028] The working mode of the UAV is controlled to be manual flight or route flight; the working modes of the UAV include the manual flight, the route flight and the guided flight;

[0029] inputting the line-of-sight angular velocity into a UAV system model;

[0030] In response to the line of sight angular velocity being input into the UAV system model, the working mode of the UAV is switched to the guided flight to achieve simulation of the UAV.

[0031] In a second aspect, the present application provides a fully virtual simulation device for a drone, comprising:

[0032] The first building module is used to build the UAV system model and the servo control system model using the modeling platform GCKontrol;

[0033] The second building module uses Python code to build the image recognition system model based on the Jupyter function in the simulation platform GCAir;

[0034] An import module, used for importing the UAV system model and the servo control system model into the simulation platform GCAir;

[0035] The simulation module is used to simulate the UAV through the UAV system model, the servo control system model and the image recognition system model.

[0036] Optionally, the simulation module includes:

[0037] The acquisition submodule is used to obtain the view provided by the FlightGear software;

[0038] A first input submodule, used for inputting the scene into the image recognition system model to obtain a misalignment angle;

[0039] A second input submodule, used for inputting the misalignment angle into the servo control system model to obtain the line of sight angular velocity;

[0040] The third input submodule is used to input the line of sight angular velocity into the UAV system model to realize the simulation of the UAV.

[0041] Optionally, the first input submodule includes:

[0042] A first input unit, used to input the scene into the image recognition system model to obtain a target;

[0043] The tracking unit is used to track the target and obtain the misalignment angle.

[0044] Optionally, the first input unit includes:

[0045] A first input subunit, used for inputting the visual scene into the image recognition system model;

[0046] A selection subunit, for selecting a working state of the image recognition system model in response to the scene being input into the image recognition system model; the working state is automatic recognition or manual selection;

[0047] A detection subunit, when the working state is automatic recognition, performs target detection on the visual scene to obtain the target;

[0048] The frame selection subunit is used to perform target frame selection on the scene to obtain the target when the working state is manual frame selection.

[0049] Optionally, the second input submodule includes:

[0050] A first control unit, used to control the working state of the servo control system model to be self-stabilizing; the working state of the servo control system model is self-stabilizing or tracking;

[0051] A second input unit, used for inputting the misalignment angle into the servo control system model;

[0052] The first switching unit is used to switch the working state of the servo control system model to tracking according to the misalignment angle to obtain the achieved angular velocity.

[0053] Optionally, the third input submodule includes:

[0054] A second control unit is used to control the working mode of the UAV to be manual flight or route flight; the working modes of the UAV include the manual flight, the route flight and the guided flight;

[0055] A second input unit, used to input the line of sight angular velocity into the UAV system model;

[0056] The second switching unit is used for switching the working mode of the UAV to the guided flight in response to the line of sight angular velocity being input into the UAV system model, so as to realize the simulation of the UAV.

[0057] In a third aspect, an embodiment of the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a full virtual simulation method for a drone as described in any one of the implementations in the first aspect of the embodiment of the present application is implemented.

[0058] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which instructions are stored. When the instructions are executed on a terminal device, the terminal device executes a full virtual simulation method of a drone as described in any one of the implementation methods in the first aspect of the embodiment of the present application.

[0059] The present application provides a full virtual simulation method for an unmanned aerial vehicle. When executing the method, the unmanned aerial vehicle system model and the servo control system model are first constructed using the modeling platform GCKontrol, and then based on the Jupyter function in the simulation platform GCAir, the image recognition system model is constructed using Python code. Then, the unmanned aerial vehicle system model and the servo control system model are imported into the simulation platform GCAir. Finally, the unmanned aerial vehicle is simulated by the unmanned aerial vehicle system model, the servo control system model, and the image recognition system model. In this way, by using the modeling platform GCKontrol to construct the unmanned aerial vehicle system model and the servo control system model, and then based on the Jupyter function in the simulation platform GCAir, and using Python code to construct the image recognition system model, the construction of the unmanned aerial vehicle system model, the servo control system model, and the image recognition system model is realized, and the unmanned aerial vehicle system model, the servo control system model, and the image recognition system model are simulated using the simulation platform GCAir, realizing the full virtual simulation of the unmanned aerial vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in this embodiment or 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 ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0061] Figure 1 A flowchart of a full virtual simulation method of a drone provided in an embodiment of the present application;

[0062] Figure 2 A schematic diagram of the workflow of the image recognition system model provided in the embodiment of the present application;

[0063] Figure 3 A schematic diagram of the workflow of the servo control system model provided in an embodiment of the present application;

[0064] Figure 4 A schematic diagram of the workflow of the drone system model provided in the embodiment of the present application;

[0065] Figure 5 A control system block diagram of a yaw loop provided in an embodiment of the present application;

[0066] Figure 6 A schematic diagram of the structure of a full virtual simulation device for a drone provided in an embodiment of the present application;

[0067] Figure 7 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0068] The following will be combined with the accompanying drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. The present application provides a full virtual simulation method and device for drones, which are used in the field of simulation technology. The above is only an example and does not limit the application field of the method and device provided in the present application.

[0069] Drone technology has been deeply applied in various fields, such as aerial photography and military fields. In the existing technology, it is difficult to achieve full virtual simulation of the image system and control system on the same platform. In the past, the image system used real objects, and the control system used models. The real objects were used to test and iterate the products, which was extremely costly and prone to property losses and safety issues.

[0070] After research, the inventor proposed the technical solution of this application. First, the UAV system model and servo control system model are constructed using the modeling platform GCKontrol. Then, based on the Jupyter function in the simulation platform GCAir, the image recognition system model is constructed using Python code. Then, the UAV system model and servo control system model are imported into the simulation platform GCAir. Finally, the UAV is simulated through the UAV system model, servo control system model and image recognition system model. In this way, by using the modeling platform GCKontrol to construct the UAV system model and servo control system model, and then based on the Jupyter function in the simulation platform GCAir, the image recognition system model is constructed using Python code, the construction of the UAV system model, servo control system model and image recognition system model is realized, and the UAV system model, servo control system model and image recognition system model are simulated using the simulation platform GCAir, realizing the full virtual simulation of the UAV.

[0071] In order to enable those skilled in the art to better understand the present application, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments 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 ordinary technicians in the field without making creative work are within the scope of protection of the present application. It should be noted that, for ease of description, only the parts related to the relevant invention are shown in the accompanying drawings. In the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0072] like Figure 1 As shown, Figure 1 A flowchart of a full virtual simulation method of a drone provided in an embodiment of the present application includes:

[0073] S101: Use the modeling platform GCKontrol to build the UAV system model and servo control system model.

[0074] GCKontrol is used as the modeling platform to build the UAV system model and servo control system model.

[0075] S102: Based on the Jupyter function in the simulation platform GCAir, the image recognition system model is constructed using Python code.

[0076] The image recognition system model uses Jupyter in the simulation platform GCAir as a python development tool. The python libraries required in the process include pyautogui, numpy, and opencv-python. The pyautogui library is used for real-time screen capture, the numpy library is used for real-time and efficient conversion of captured screen data, and the opencv-python library is used for target tracking, screen display, and target selection.

[0077] S103: Import the UAV system model and the servo control system model into the simulation platform GCAir.

[0078] The UAV system model, servo control system model and image recognition system model built by the modeling platform GCKontrol were imported into the simulation platform GCAir.

[0079] S104: Simulating the UAV through the UAV system model, the servo control system model and the image recognition system model.

[0080] On the simulation platform GCAir, the UAV system model, servo control system model and image recognition system model are used to perform full virtual simulation of the UAV. The specific simulation method is as follows:

[0081] First, in the embodiment provided in this application, the vision is provided by FlightGear software.

[0082] Next, the view provided by FlightGear software is input into the image recognition system model to obtain the misalignment angle, as follows: Figure 2 As shown, Figure 2The workflow diagram of the image recognition system model provided in the embodiment of the present application is as follows: first, the working mode of the image recognition system model is selected, and the working mode is automatic recognition or manual selection. When the working mode is automatic recognition, target detection is performed on the scene provided by the FlightGear software to obtain the target; when the working mode is manual selection, target selection is performed on the scene provided by the FlightGear software to obtain the target. After obtaining the target, the target is tracked and the misalignment angle is output.

[0083] The servo control system model has two working modes: self-stabilization and tracking. The self-stabilization mode is to keep the optical axis of the image recognition system model stable at the set angle and not shake with the maneuvering and swaying of the drone; the tracking mode is to keep the optical axis of the image recognition system model always pointing to the tracked target and isolate the shaking of the drone. The workflow of the servo control system model is to start by maintaining stability at the preset angle and waiting for the target data of the image recognition system model, that is, the misalignment angle. After receiving the target data, the optical axis of the image recognition system model is kept pointing to the target based on the received data. Due to the change in the relative position of the drone and the target, the yaw axis and the pitch axis are rotating all the time. The gyroscope senses the rotation information, and combines the angles of the yaw axis and the pitch axis to convert the data sensed by the gyroscope into the rotation information in the drone coordinate system. The specific process is as follows: Figure 3 As shown, Figure 3 A schematic diagram of the workflow of the servo control system model provided in an embodiment of the present application, first, the working mode of the servo control system model is controlled to be a self-stabilizing model. If target data, i.e., misalignment angle, is received from the image recognition system model, the working mode of the servo control system model is controlled to switch to a tracking mode, and the line of sight angular velocity is output.

[0084] The UAV system has three working modes: route flight, manual flight, and guided flight. When started, the UAV works in route flight or manual flight mode, waiting for the tracking data from the guidance device control system, and after receiving the tracking data, it enters the guided flight working mode. Figure 4 A schematic diagram of the workflow of the drone system model provided in an embodiment of the present application, wherein the working mode of the drone is controlled to be manual flight or route flight; the line of sight angular velocity is input into the drone system model, and in response to the line of sight angular velocity being input into the drone system model, the working mode of the drone is switched to guided flight.

[0085] In addition, the control system of the full virtual simulation system is divided into two loops, the yaw loop and the pitch loop. The following takes the yaw loop as an example. Figure 5A control system block diagram of a yaw loop provided in an embodiment of the present application, first, a view is input into an image recognition system, the image recognition system manually selects the view or automatically identifies a target, and tracks the target to output a misalignment angle, then the servo control system controls the misalignment angle to 0, outputs the line of sight angular velocity, and the UAV system controls the line of sight angular velocity to 0 for flight.

[0086] In the embodiment provided in the present application, the modeling platform GCKontrol is first used to construct the UAV system model and the servo control system model, and then the image recognition system model is constructed based on the Jupyter function in the simulation platform GCAir and Python code. Then, the UAV system model and the servo control system model are imported into the simulation platform GCAir. Finally, the UAV is simulated by the UAV system model, the servo control system model and the image recognition system model. In this way, by using the modeling platform GCKontrol to construct the UAV system model and the servo control system model, and then based on the Jupyter function in the simulation platform GCAir and Python code to construct the image recognition system model, the construction of the UAV system model, the servo control system model and the image recognition system model is realized, and the UAV system model, the servo control system model and the image recognition system model are simulated by using the simulation platform GCAir, and the full virtual simulation of the UAV is realized.

[0087] The above are some specific implementations of the full virtual simulation method of the drone provided by the embodiment of the present application. Based on this, the present application also provides a corresponding device. The device provided by the embodiment of the present application will be introduced from the perspective of functional modularization.

[0088] See also Figure 6 , Figure 6 A schematic diagram of the structure of a full virtual simulation device for a drone provided in an embodiment of the present application. The full virtual simulation device 600 for a drone includes:

[0089] The first construction module 610 is used to construct a UAV system model and a servo control system model using a modeling platform GCKontrol;

[0090] The second construction module 620 constructs the image recognition system model using Python code based on the Jupyter function in the simulation platform GCAir;

[0091] An import module 630, used to import the drone system model and the servo control system model into the simulation platform GCAir;

[0092] The simulation module 640 is used to simulate the drone through the drone system model, the servo control system model and the image recognition system model.

[0093] Optionally, the simulation module 640 includes:

[0094] The acquisition submodule is used to obtain the view provided by the FlightGear software;

[0095] A first input submodule, used for inputting the scene into the image recognition system model to obtain a misalignment angle;

[0096] A second input submodule, used for inputting the misalignment angle into the servo control system model to obtain the line of sight angular velocity;

[0097] The third input submodule is used to input the line of sight angular velocity into the UAV system model to realize the simulation of the UAV.

[0098] Optionally, the first input submodule includes:

[0099] A first input unit, used to input the scene into the image recognition system model to obtain a target;

[0100] The tracking unit is used to track the target and obtain the misalignment angle.

[0101] Optionally, the first input unit includes:

[0102] A first input subunit, used for inputting the visual scene into the image recognition system model;

[0103] A selection subunit, for selecting a working state of the image recognition system model in response to the scene being input into the image recognition system model; the working state is automatic recognition or manual selection;

[0104] A detection subunit, when the working state is automatic recognition, performs target detection on the visual scene to obtain the target;

[0105] The frame selection subunit is used to perform target frame selection on the scene to obtain the target when the working state is manual frame selection.

[0106] Optionally, the second input submodule includes:

[0107] A first control unit, used to control the working state of the servo control system model to be self-stabilizing; the working state of the servo control system model is self-stabilizing or tracking;

[0108] A second input unit, used for inputting the misalignment angle into the servo control system model;

[0109] The first switching unit is used to switch the working state of the servo control system model to tracking according to the misalignment angle to obtain the achieved angular velocity.

[0110] Optionally, the third input submodule includes:

[0111] A second control unit is used to control the working mode of the UAV to be manual flight or route flight; the working modes of the UAV include the manual flight, the route flight and the guided flight;

[0112] A second input unit, used to input the line of sight angular velocity into the UAV system model;

[0113] The second switching unit is used for switching the working mode of the UAV to the guided flight in response to the line of sight angular velocity being input into the UAV system model, so as to realize the simulation of the UAV.

[0114] The embodiments of the present application also provide corresponding devices and computer storage media for implementing the solutions provided by the embodiments of the present application.

[0115] like Figure 6 As shown, the computer device 01 is in the form of a general computing device. The components of the computer device 01 may include but are not limited to: one or more processors or processing units 03, a system memory 08, and a bus 04 connecting different system components (including the system memory 08 and the processing unit 03).

[0116] Bus 04 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnect (PCI) bus.

[0117] The computer device 01 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 01, including volatile and non-volatile media, removable and non-removable media.

[0118] The system memory 08 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 09 and / or cache memory 10. The computer device 01 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 11 may be used to read and write non-removable, non-volatile magnetic media ( Figure 6 not shown, usually called a "hard drive"). Although Figure 6 Not shown in the figure, a disk drive for reading and writing a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 04 via one or more data medium interfaces. The memory 08 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0119] A program / utility 12 having a set (at least one) of program modules 13 may be stored, for example, in the memory 08, such program modules 13 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination thereof may include an implementation of a network environment. The program modules 13 generally perform the functions and / or methods of the embodiments described herein.

[0120] The computer device 01 may also communicate with one or more external devices 02 (e.g., keyboard, pointing device, display 07, etc.), one or more devices that enable a user to interact with the computer device 01, and / or any device that enables the computer device 01 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed via input / output (I / O) interface 06. Furthermore, the computer device 01 may also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN) and / or public network, such as the Internet) via network adapter 05. Figure 6 As shown, the network adapter 05 communicates with other modules of the computer device 01 via the bus 04. It should be understood that although Figure 6 Not shown, other hardware and / or software modules may be used in conjunction with computer device 01, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0121] The processor unit 03 executes various functional applications and data processing by running the programs stored in the system memory 08, such as implementing a full virtual simulation method of a drone provided in an embodiment of the present application.

[0122] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are 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 other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0123] Through the description of the above implementation methods, it can be known that those skilled in the art can clearly understand that all or part of the steps in the above-mentioned embodiment method can be implemented by means of software plus a general hardware platform. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a storage medium, such as a read-only memory (ROM) / RAM, a magnetic disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network communication device such as a router) to execute the methods described in each embodiment of the present application or some parts of the embodiments.

[0124] Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative work.

[0125] The above description is merely an exemplary embodiment of the present application and is not intended to limit the protection scope of the present application.

Claims

1. A full virtual simulation method for an unmanned aerial vehicle, characterized in that: include: Use the modeling platform GCKontrol to build the UAV system model and servo control system model; Based on the Jupyter function in the simulation platform GCAir, the image recognition system model was constructed using Python code; Importing the drone system model and the servo control system model into the simulation platform GCAir; The drone is simulated by using the drone system model, the servo control system model and the image recognition system model.

2. The method according to claim 1, characterized in that The simulating the drone through the drone system model, the servo control system model and the image recognition system model includes: Get the view provided by FlightGear software; Inputting the visual scene into the image recognition system model to obtain the misalignment angle; Inputting the misalignment angle into the servo control system model to obtain the line of sight angular velocity; The line of sight angular velocity is input into the UAV system model to realize the simulation of the UAV.

3. The method according to claim 2, characterized in that The step of inputting the visual scene into the image recognition system model to obtain the misalignment angle comprises: Inputting the visual scene into the image recognition system model to obtain a target; The target is tracked to obtain the misalignment angle.

4. The method according to claim 3, characterized in that: The step of inputting the visual scene into the image recognition system model to obtain a target includes: Inputting the visual scene into the image recognition system model; In response to the scene being input into the image recognition system model, selecting a working state of the image recognition system model; the working state is automatic recognition or manual selection; When the working state is automatic identification, target detection is performed on the visual scene to obtain the target; When the working state is manual selection, target selection is performed on the scene to obtain the target.

5. The method according to claim 2, characterized in that: The step of inputting the misalignment angle into the servo control system model to obtain the line of sight angular velocity comprises: Controlling the working state of the servo control system model to be self-stabilizing; the working state of the servo control system model to be self-stabilizing or tracking; Inputting the misalignment angle into the servo control system model; According to the misalignment angle, the working state of the servo control system model is switched to tracking to obtain the achieved angular velocity.

6. The method according to claim 2, characterized in that The step of inputting the line of sight angular velocity into a UAV system model to achieve simulation of the UAV includes: The working mode of the UAV is controlled to be manual flight or route flight; the working modes of the UAV include the manual flight, the route flight and the guided flight; inputting the line-of-sight angular velocity into a UAV system model; In response to the line of sight angular velocity being input into the UAV system model, the working mode of the UAV is switched to the guided flight to achieve simulation of the UAV.

7. A full virtual simulation device for an unmanned aerial vehicle, characterized in that: include: The first building module is used to build the UAV system model and the servo control system model using the modeling platform GCKontrol; The second building module uses Python code to build the image recognition system model based on the Jupyter function in the simulation platform GCAir; An import module, used for importing the UAV system model and the servo control system model into the simulation platform GCAir; The simulation module is used to simulate the UAV through the UAV system model, the servo control system model and the image recognition system model.

8. The device according to claim 7, characterized in that The simulation module comprises: The acquisition submodule is used to obtain the view provided by the FlightGear software; A first input submodule, used for inputting the scene into the image recognition system model to obtain a misalignment angle; A second input submodule, used for inputting the misalignment angle into the servo control system model to obtain the line of sight angular velocity; The third input submodule is used to input the line of sight angular velocity into the UAV system model to realize the simulation of the UAV.

9. A computer device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the full virtual simulation method of the drone as described in any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed on a terminal device, the terminal device executes the full virtual simulation method for a drone as described in any one of claims 1 to 6.

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