Virtual-real interaction method and system for simulated training flight of unmanned aerial vehicle

Abstract

The invention discloses a virtual-real interaction method and system for simulated training flight of an unmanned aerial vehicle, and relates to the field of general control or regulation systems.The method comprises the steps that airflow channels are divided in a preset geographic area; performing nonlinear interpolation calculation on the real-time meteorological data according to the position of a preset detection point in each airflow channel to obtain meteorological data of each preset detection point; generating an airflow disturbance characteristic graph; based on the airflow disturbance characteristic pattern, arranging a region conversion buffer zone among the stable airflow region, the weak disturbance region and the strong disturbance region; under the condition that it is determined that the virtual unmanned aerial vehicle enters the area conversion buffer zone according to the real-time position coordinates of the virtual unmanned aerial vehicle in the virtual training environment, the pitch angle, the roll angle and the yaw angle of the virtual unmanned aerial vehicle are adjusted according to a preset time interval and a preset incremental coefficient, and angle variation is obtained; and the angle variation is converted into a force feedback signal, and the force feedback signal is fed back to an operator through a force feedback device. According to the invention, the authenticity and effectiveness of virtual unmanned aerial vehicle training flight are improved.

Description

Technical Field

[0001] This application belongs to the field of general control or regulation systems, and particularly relates to a method and system for virtual-real interaction in simulated training flights of unmanned aerial vehicles (UAVs). Background Art

[0002] With the rapid development of UAV technology, UAVs have been widely used in various fields. During the UAV flight training process, the traditional actual flight training method is not only costly but also has safety hazards. Especially during emergency rescue flight training under adverse weather conditions such as heavy rain and strong winds, beginners are prone to damage the UAV or cause accidents due to improper operation. At the same time, due to weather conditions, it is difficult to ensure the continuity and effectiveness of training.

[0003] In related technologies, virtual simulation technology driven by meteorological data can be used for UAV flight training in adverse environments. By obtaining environmental parameters such as wind speed and precipitation of a weather station in real time and combining with a fluid dynamics model to simulate the flight state of the UAV, trainees can use a control device similar to an actual UAV remote controller to conduct flight operation training in a virtual environment. This training method is not affected by the external environment and can be repeatedly operated and practiced, reducing the training cost and safety risk.

[0004] However, in complex terrain environments such as mountain rescue, when strong winds and heavy rain interact, there will be airflow disturbance characteristics under the superposition of multiple adverse weather conditions, resulting in a decrease in the accuracy of trainees' prediction of the flight state under combined weather conditions during training. For example, when conducting rescue in a valley, it is difficult for the virtual training system to simulate the combined effects of the updraft and heavy rain in the valley, making it difficult for trainees to obtain corresponding training experience during simulated training flights. As a result, trainees need more time to adapt to such complex weather conditions when actually performing tasks, reducing the effectiveness of UAV simulated training flights. Summary of the Invention

[0005] This application provides a method and system for virtual-real interaction in simulated training flights of UAVs. By establishing an airflow channel model based on terrain data and fluid dynamics, combining real-time meteorological data for non-linear interpolation calculation, generating an accurate airflow disturbance feature map, and through region division and buffer zone design, when the virtual UAV crosses different airflow regions, the angle change amount is transmitted to the operator in real time through a force feedback device, enabling training personnel to truly feel the airflow changes under complex terrain and the superposition of multiple adverse weather conditions, and improving the authenticity and effectiveness of virtual training.

[0006] In a first aspect, this application provides a method for virtual-real interaction in simulated training flights of UAVs, which divides airflow channels in a preset geographical area according to the terrain elevation data and fluid dynamics principle of the preset geographical area; Perform non - linear interpolation calculations on the real - time meteorological data according to the positions of preset detection points within each air flow channel to obtain the meteorological data of each preset detection point; Generate an air flow disturbance feature map based on the meteorological data of each preset detection point; Based on the air flow disturbance feature map, divide the preset geographical area into a stable air flow area, a weak disturbance area, and a strong disturbance area, and set regional transition buffer zones between the stable air flow area, the weak disturbance area, and the strong disturbance area respectively; When it is determined that the virtual UAV enters the regional transition buffer zone according to the real - time position coordinates of the virtual UAV in the virtual training environment, adjust the pitch angle, roll angle, and yaw angle of the virtual UAV at a preset time interval with a preset increasing coefficient to obtain an angle change amount; Convert the angle change amount into a force feedback signal and feedback it to the operator through a force feedback device.

[0007] By adopting the above - mentioned technical solution, through establishing an air flow channel and a detection point network, an accurate modeling of the air flow state within the preset geographical area is achieved. Based on the non - linear interpolation calculation of real - time meteorological data, accurate meteorological data of the detection points can be obtained, and then a disturbance feature map reflecting the regional air flow distribution characteristics can be generated. By dividing the area into different air flow disturbance levels and setting buffer zones, the system can smoothly adjust the flight attitude of the virtual UAV at a preset time interval and increasing coefficient when the virtual UAV crosses different air flow regions, avoiding a sharp increase in the control difficulty caused by sudden attitude changes. Converting the calculated angle change amount into a force feedback signal and transmitting it to the operator enables the operator to truly feel the control resistance under different air flow environments, enhancing the authenticity during the training process. This virtual - reality interaction method based on real - time meteorological data and physical models enables the UAV simulation training system to more accurately simulate the influence of air flow in the actual flight environment and helps the operator master flight skills under complex meteorological conditions.

[0008] Combined with some embodiments of the first aspect, in some embodiments, dividing the air flow channels in the preset geographical area according to the terrain elevation data of the preset geographical area and the principles of fluid mechanics specifically includes: Convert the terrain elevation data of the preset geographical area into three - dimensional grid data; Extract ridge lines and valley lines according to the three - dimensional grid data; Divide the preset geographical area into multiple sub - areas with the ridge lines as boundaries; Within each sub - area, calculate the air flow movement direction based on the principles of fluid mechanics; According to the air flow movement direction, combine adjacent grid units with the same air flow movement trend to form multiple air flow channels.

[0009] By adopting the above technical solution, by converting the terrain elevation data into a three-dimensional grid and extracting terrain features, an accurate description of the airflow movement law in complex terrain is achieved. Based on the ridgeline for regional division, combined with the principles of fluid mechanics to calculate the airflow movement direction in each sub-region, enabling the system to accurately identify and simulate the influence of terrain on airflow movement. By combining adjacent grid cells with the same airflow movement trend to form airflow channels, a complete regional airflow network model is established. This method of dividing airflow channels based on terrain features and fluid mechanics enables the system to accurately reflect the guiding effect of terrain undulation on airflow movement, improving the accuracy of airflow simulation in the virtual training environment.

[0010] In combination with some embodiments of the first aspect, in some embodiments, the pitch angle, roll angle, and yaw angle of the virtual unmanned aerial vehicle are adjusted at a preset time interval with a preset increasing coefficient to obtain the angle change amount, specifically including: Determine the difference in airflow disturbance characteristics between the two regions on both sides of the regional conversion buffer zone; Calculate the target attitude angle based on the difference in airflow disturbance characteristics. The target attitude angle includes the target pitch angle, target roll angle, and target yaw angle; Obtain the preset increasing coefficient at the current moment at a preset time interval. The value range of the preset increasing coefficient is from 0 to 1; Multiply the difference between the target attitude angle and the current attitude angle by the preset increasing coefficient to obtain the angle change amount. The angle change amount includes the pitch angle change amount, roll angle change amount, and yaw angle change amount.

[0011] By adopting the above technical solution, the target attitude angle is determined by calculating the difference in airflow disturbance characteristics between the two regions on both sides of the buffer zone, and the smooth transition of the attitude angle is achieved by using the preset increasing coefficient, avoiding the attitude mutation that may occur when switching between different airflow regions. The system calculates the required attitude adjustment amount according to the actual difference in airflow disturbance characteristics, and precisely controls the adjustment process through an increasing coefficient that varies within the range of 0 to 1, ensuring that the attitude change of the virtual unmanned aerial vehicle is continuous and natural when passing through different airflow regions. This progressive attitude adjustment method based on the actual difference in airflow characteristics not only ensures the timely response of the unmanned aerial vehicle to airflow changes but also avoids the sudden feeling caused by violent attitude changes to the operator, enhancing the realism and controllability of the virtual training process.

[0012] In combination with some embodiments of the first aspect, in some embodiments, after converting the angle change amount into a force feedback signal and feeding it back to the operator through a force feedback device, the method further includes: Regarding the virtual unmanned aerial vehicle as an airflow disturbance source, generate a moving disturbance field centered on the virtual unmanned aerial vehicle; Based on the interaction law between the moving disturbance field and the airflow channels in the preset geographical area, form a dynamic airflow environment; Calculate the feedback disturbance acting on the virtual UAV according to the dynamic airflow environment; Fuse the feedback disturbance with the angle change amount to obtain the fused angle change amount; Update the force feedback signal based on the fused angle change amount.

[0013] By adopting the above technical solution, by using the virtual UAV as an airflow disturbance source and generating a moving disturbance field, the dynamic simulation of the airflow changes caused by the UAV flight is realized. Based on the interaction law between the moving disturbance field and the existing airflow channels, the system calculates and updates the dynamic airflow environment, and then obtains the feedback disturbance acting on the virtual UAV. After fusing this feedback disturbance with the original angle change amount and updating the force feedback signal, the system can simultaneously reflect the comprehensive influence of the environmental airflow and the UAV's own movement on the flight state. This dynamic feedback mechanism considering the interaction between the UAV and the airflow environment realizes a force feedback effect closer to the real flight state, and improves the accuracy and authenticity of the aerodynamic effect simulation during the training process.

[0014] Combined with some embodiments of the first aspect, in some embodiments, based on the interaction law between the moving disturbance field and the airflow channels in the preset geographical area, a dynamic airflow environment is formed, which specifically includes: Calculate the disturbance propagation direction of the moving disturbance field in each airflow channel; Determine the disturbance intensity of the moving disturbance field on each airflow channel according to the disturbance propagation direction; Superimpose the disturbance intensity on the original airflow characteristics of each airflow channel to obtain a superimposed result, and calculate new airflow distribution data based on the superimposed result; Generate a dynamic airflow environment according to the new airflow distribution data.

[0015] By adopting the above technical solution, by calculating the disturbance propagation direction of the moving disturbance field in each airflow channel, determining the disturbance intensity on each airflow channel based on the propagation direction, and then superimposing the disturbance intensity on the original airflow characteristics to generate new airflow distribution data, and finally forming a dynamic airflow environment, the system can accurately simulate the actual influence of the UAV flight on the surrounding airflow environment. Due to considering the propagation characteristics of the disturbance in different airflow channels, the system can truly reflect the wake effect generated by the UAV and the interaction process between these wakes and the surrounding airflow. This dynamic update mechanism of the airflow environment based on physical laws enables the training personnel to experience more actual airflow changes, and improves the realism and accuracy of the simulation training.

[0016] Combined with some embodiments of the first aspect, in some embodiments, generating a dynamic airflow environment according to the new airflow distribution data specifically includes: Extract the air flow gradient between each air flow region and calculate the rate of change of the air flow gradient over time; Determine the transition characteristics between air flow regions according to the rate of change; Take the continuous air flow field generated based on the transition characteristics as the dynamic air flow environment.

[0017] By adopting the above technical solution, by extracting the air flow gradient between air flow regions and calculating its rate of change over time, the system establishes a transition characteristic model between air flow regions and generates a continuous dynamic air flow field, which can provide a more delicate and realistic air flow transition effect and improve the simulation degree of the simulation training system.

[0018] Combined with some embodiments of the first aspect, in some embodiments, calculate the feedback disturbance acting on the virtual unmanned aerial vehicle according to the dynamic air flow environment, specifically including: Obtain the air flow velocity and direction at the position of the virtual unmanned aerial vehicle in the dynamic air flow environment; Calculate the forces exerted by the air flow velocity and direction on each force-bearing surface of the virtual unmanned aerial vehicle; Calculate the resultant moment according to the forces on each force-bearing surface and the position of the action point; Take the attitude angle change amount obtained by converting the resultant moment as the feedback disturbance acting on the virtual unmanned aerial vehicle.

[0019] By adopting the above technical solution, by obtaining the air flow velocity and direction at the position of the virtual unmanned aerial vehicle, calculating the forces exerted by the air flow on each force-bearing surface, and converting the resultant moment into an attitude angle change amount, the different forces and the moment effects generated on each part of the unmanned aerial vehicle are considered, and the air flow influence on the unmanned aerial vehicle in the real flight environment can be accurately reflected. By converting the air flow action into a specific attitude angle change amount, the system can provide accurate force feedback signals for the training personnel, enabling them to intuitively feel the attitude changes of the unmanned aerial vehicle under different air flow conditions, improving the physical authenticity of the simulation training, and allowing the training personnel to accumulate actual combat experience in dealing with various air flow disturbances in a safe virtual environment.

[0020] In a second aspect, an embodiment of the present application provides a virtual-real interaction system for simulating the flight of an unmanned aerial vehicle. The virtual-real interaction system for simulating the flight of an unmanned aerial vehicle includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code. The computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer-readable storage medium including instructions that, when running on a system, cause the system to execute the method described in the first aspect and any possible implementation manner of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer program product that, when running on a system, causes the system to execute the method described in any possible implementation manner of the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The present application provides a virtual-real interaction method for unmanned aerial vehicle (UAV) simulated training flight. By establishing an air flow channel and a detection point network, an accurate model of the air flow state within a preset geographical area is achieved. Based on non-linear interpolation calculations of real-time meteorological data, accurate meteorological data for the detection points can be obtained, and then a disturbance feature map reflecting the regional air flow distribution characteristics is generated. By dividing the area into different air flow disturbance levels and setting buffer zones, the system can smoothly adjust the flight attitude of the virtual UAV at preset time intervals and increasing coefficients when the virtual UAV crosses different air flow regions, avoiding a sharp increase in the control difficulty caused by sudden attitude changes. Converting the calculated angle change amount into a force feedback signal and transmitting it to the operator enables the operator to truly feel the control resistance in different air flow environments, enhancing the authenticity during the training process. This virtual-real interaction method based on real-time meteorological data and physical models enables the UAV simulated training system to more accurately simulate the influence of air flow in the actual flight environment and helps the operator master flight skills under complex meteorological conditions.

[0024] 2. The present application provides a virtual-real interaction method for UAV simulated training flight. By using the virtual UAV as an air flow disturbance source and generating a moving disturbance field, a dynamic simulation of the air flow changes caused by the UAV flight is achieved. Based on the interaction law between the moving disturbance field and the existing air flow channel, the system calculates and updates the dynamic air flow environment, and then obtains the feedback disturbance acting on the virtual UAV. After fusing this feedback disturbance with the original angle change amount and updating the force feedback signal, the system can simultaneously reflect the comprehensive influence of the environmental air flow and the UAV's own movement on the flight state. This dynamic feedback mechanism considering the interaction between the UAV and the air flow environment achieves a force feedback effect closer to the real flight state and improves the accuracy and authenticity of the simulation of aerodynamic effects during the training process. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a flow schematic diagram of a virtual-real interaction method for UAV simulated training flight in an embodiment of the present application.

[0026] Figure 2It is a schematic flowchart of a virtual - reality interaction method considering the wake effect of an unmanned aerial vehicle (UAV) in an embodiment of the present application.

[0027] Figure 3 It is a schematic structural diagram of an entity device of a virtual - reality interaction system for UAV simulation training flight provided in an embodiment of the present application. Detailed implementation manners

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. As used in the specification and appended claims of the present application, the singular forms "a", "an", "the", "above - mentioned", "said", and "this" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations including one or more of the listed items.

[0029] Hereinafter, the terms "first" and "second" are only used for descriptive purposes, and cannot be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0030] Next, an embodiment is used in combination with Figure 1 to describe a virtual - reality interaction method for UAV simulation training flight in an embodiment of the present application: Please refer to Figure 1 which is a schematic flowchart of a virtual - reality interaction method for UAV simulation training flight in an embodiment of the present application.

[0031] S101. Divide airflow channels in a preset geographical area according to the terrain elevation data of the preset geographical area and the principles of fluid mechanics; The system divides airflow channels in a preset geographical area according to the terrain elevation data of the preset geographical area and the principles of fluid mechanics, specifically including: converting the terrain elevation data of the preset geographical area into three - dimensional grid data; extracting ridge lines and valley lines according to the three - dimensional grid data; dividing the preset geographical area into multiple sub - areas with the ridge lines as boundaries; calculating the airflow movement directions within each sub - area based on the principles of fluid mechanics; and combining adjacent grid cells with the same airflow movement trend according to the airflow movement directions to form multiple airflow channels.

[0032] In this step, the system divides the airflow channels in the preset geographical area by using the terrain elevation data of the preset geographical area and the principles of fluid mechanics. The terrain elevation data is the data describing the height change of the earth's surface, which can be obtained through various methods such as surveying and mapping, remote sensing, etc. The principles of fluid mechanics are the theories describing the laws of fluid motion, including the law of conservation of mass, the law of conservation of momentum, the law of conservation of energy, etc. The system comprehensively considers the terrain factors and the laws of airflow movement to make a reasonable division of the airflow channels in the preset geographical area.

[0033] When specifically implemented, the system can convert the terrain elevation data of the preset geographical area into three-dimensional grid data for subsequent processing and analysis. Then, the system can extract the ridge lines and valley lines from the three-dimensional grid data as important references for the division of airflow channels. Taking the ridge lines as the demarcation, the system divides the preset geographical area into multiple sub-areas. Within each sub-area, the system calculates the airflow movement direction based on the principles of fluid mechanics and combines the adjacent grid cells with the same airflow movement trend according to the airflow movement direction, finally forming multiple airflow channels.

[0034] In practical applications, the terrain of the preset geographical area may be very complex, with a large number of terrain features such as mountains, canyons, basins, etc., making it difficult to accurately judge the airflow movement direction. To solve this problem, the system can introduce terrain factors such as slope, aspect, curvature, etc. as correction parameters for calculating the airflow movement direction. The system can also consider the influence of surface cover factors such as vegetation and buildings on the airflow movement to further improve the accuracy of the airflow channel division.

[0035] S102. Perform non-linear interpolation calculation on the real-time meteorological data according to the positions of the preset detection points in each airflow channel to obtain the meteorological data of each preset detection point; In this step, the system maps the real-time meteorological data to the preset detection points in the airflow channels to obtain the meteorological data of each preset detection point. The real-time meteorological data usually comes from meteorological observation stations or meteorological sensors, including various meteorological elements such as wind speed, wind direction, air temperature, air pressure, etc. The preset detection points are virtual observation points set by the system inside the airflow channels to simulate the airflow disturbance situation. Since the real-time meteorological data and the preset detection points may not completely coincide in spatial position, the system needs to perform data interpolation calculation.

[0036] When specifically implemented, the system can adopt non-linear interpolation algorithms such as Kriging interpolation and spline interpolation to interpolate the real-time meteorological data to the positions of the preset detection points. The non-linear interpolation algorithms can consider the spatial correlation and non-linear variation characteristics of the meteorological elements to improve the accuracy of the interpolation results. The system can optimize the parameter settings of the interpolation algorithm, such as the search radius, the number of samples, etc., according to the morphological characteristics of the airflow channels and the distribution laws of the meteorological elements.

[0037] In practical applications, the spatio-temporal resolution of real-time meteorological data may be low, making it difficult to meet the requirements of refined simulation. To address this issue, the system can introduce meteorological numerical prediction models such as WRF, MM5, etc. to generate meteorological data products with high spatio-temporal resolution as a supplement to real-time meteorological data. The system can also use machine learning algorithms such as neural networks and support vector machines to establish correlation models between meteorological elements and factors such as terrain and underlying surface to intelligently correct and optimize real-time meteorological data.

[0038] S103. Generate an airflow disturbance feature map based on the meteorological data of each preset detection point; In this step, the system generates a feature map reflecting the airflow disturbance situation based on the meteorological data of each preset detection point. Airflow disturbance is an important factor affecting the flight stability and safety of drones, manifested as drastic changes in aspects such as airflow velocity, direction, and turbulence intensity. The airflow disturbance feature map is a way to intuitively express the spatial distribution of airflow disturbance and can provide a reference for the flight path planning and flight control of drones.

[0039] Specifically, when implementing, the system can select meteorological elements such as wind speed, wind direction, and turbulence intensity, calculate the airflow disturbance indicators at each preset detection point, such as wind shear rate and turbulence energy dissipation rate. Then, the system can use spatial interpolation algorithms such as inverse distance weighted interpolation and ordinary kriging interpolation to interpolate the discrete airflow disturbance indicators to the entire preset geographical area to generate a continuous airflow disturbance feature map. The system can adjust the parameter settings of the interpolation algorithm according to the application requirements of the airflow disturbance feature map to control the smoothness and detail performance of the feature map.

[0040] S104. Divide the preset geographical area into a stable airflow area, a weak disturbance area, and a strong disturbance area based on the airflow disturbance feature map, and set regional transition buffer zones between the stable airflow area, the weak disturbance area, and the strong disturbance area respectively; In this step, the system conducts airflow stability zoning on the preset geographical area based on the airflow disturbance feature map and sets regional transition buffer zones. Airflow stability zoning divides the preset geographical area into different sub-areas according to the airflow disturbance intensity, including a stable airflow area, a weak disturbance area, and a strong disturbance area. There are obvious differences in the airflow disturbance characteristics in different sub-areas, posing different requirements for the flight control of drones. The regional transition buffer zone is a transition area set between adjacent sub-areas to alleviate the problem of sudden airflow changes that drones may encounter during cross-regional flight.

[0041] In specific implementation, the system can perform threshold segmentation on the airflow disturbance feature map, and divide the preset geographical area into a stable airflow area, a weak disturbance area, and a strong disturbance area according to the magnitude of the airflow disturbance index. The selection of the threshold can refer to the flight performance and adaptability of the UAV, or can be determined through flight test experiments and expert experience. For the setting of the regional conversion buffer zone, the system can adopt morphological operations, such as dilation, erosion, etc., to process the boundaries of adjacent sub-regions and generate a transition region with a certain width. The system can also consider the influence of factors such as terrain and underlying surface on the airflow stability zoning and make adaptive adjustments.

[0042] In practical applications, problems such as regional fragmentation and irregular boundaries may occur in the airflow stability zoning, which affect the continuity and smoothness of the UAV flight path. To solve this problem, the system can introduce region merging and boundary smoothing algorithms to optimize the initially generated airflow stability zoning. The system can also dynamically adjust the airflow stability zoning according to the mission requirements and performance parameters of the UAV, such as expanding the range of the stable airflow area and narrowing the range of the strong disturbance area, etc., to improve the flexibility and adaptability of the flight path planning.

[0043] S105. When it is determined according to the real-time position coordinates of the virtual UAV in the virtual training environment that the virtual UAV enters the regional conversion buffer zone, adjust the pitch angle, roll angle, and yaw angle of the virtual UAV at a preset time interval with a preset increment coefficient to obtain the angle change amount; When it is determined according to the real-time position coordinates of the virtual UAV in the virtual training environment that the virtual UAV enters the regional conversion buffer zone, the system adjusts the pitch angle, roll angle, and yaw angle of the virtual UAV at a preset time interval with a preset increment coefficient to obtain the angle change amount. Specifically, determine the difference in airflow disturbance characteristics between the two regions on both sides of the regional conversion buffer zone; calculate the target attitude angles according to the difference in airflow disturbance characteristics, and the target attitude angles include the target pitch angle, target roll angle, and target yaw angle; obtain the preset increment coefficient at the current moment at a preset time interval, and the value range of the preset increment coefficient is from 0 to 1; multiply the difference between the target attitude angle and the current attitude angle by the preset increment coefficient to obtain the angle change amount, and the angle change amount includes the pitch angle change amount, roll angle change amount, and yaw angle change amount.

[0044] In this step, the system determines whether the virtual UAV enters the regional conversion buffer zone according to the real-time position of the virtual UAV, and gradually adjusts the attitude angles of the virtual UAV when it enters the regional conversion buffer zone to simulate the attitude change process of the virtual UAV when crossing different airflow disturbance regions. The adjustment of the attitude angles is achieved by setting a preset time interval and a preset increment coefficient, and finally the change amounts of the pitch angle, roll angle, and yaw angle of the virtual UAV are obtained.

[0045] In specific implementation, the system first needs to obtain the position coordinates of the virtual UAV in real time and compare them with the spatial range of the regional conversion buffer zone to determine whether the virtual UAV has entered the regional conversion buffer zone. When the virtual UAV enters the regional conversion buffer zone, the system starts to execute the algorithm for attitude angle adjustment.

[0046] The first step of the algorithm is to determine the difference in airflow disturbance characteristics between the two regions on both sides of the regional conversion buffer zone. The airflow disturbance characteristics can include multiple parameters such as wind speed, wind direction, and turbulence intensity. The system needs to calculate the differences in these parameters between the two regions on both sides of the buffer zone to quantify the degree of change in airflow disturbance.

[0047] The second step is to calculate the target attitude angles based on the difference in airflow disturbance characteristics. The target attitude angles include the target pitch angle, target roll angle, and target yaw angle, representing the attitude state that the virtual UAV should achieve when fully entering the target area. The calculation of the target attitude angles needs to comprehensively consider factors such as the difference in airflow disturbance characteristics, the flight performance of the UAV, and the control strategy, and can be estimated using physical models, empirical formulas, or machine learning methods.

[0048] The third step is to obtain the preset increment coefficient at the current moment according to the preset time interval. The preset time interval represents the time step of attitude angle adjustment, such as 0.1 second, 0.5 second, etc., and can be set according to factors such as the control frequency and computing resources of the UAV. The preset increment coefficient represents the proportion of the attitude angle change amount in each time step to the total amount of target attitude angle change, and the value range is from 0 to 1. The preset increment coefficient can adopt a fixed value, such as 0.1, 0.2, etc., or a changing value, such as gradually decreasing with time increase, to achieve a smooth transition of attitude angle change.

[0049] The fourth step is to multiply the difference between the target attitude angle and the current attitude angle by the preset increment coefficient to obtain the attitude angle change amount in the current time step. The attitude angle change amount includes the pitch angle change amount, roll angle change amount, and yaw angle change amount, respectively representing the rotational angle changes of the virtual UAV in three spatial directions. The system updates the current attitude angle according to the attitude angle change amount to obtain the new attitude angle of the virtual UAV at the end of the current time step.

[0050] The above four steps are executed in a loop until the virtual UAV completely enters the target area, and the entire process of attitude angle adjustment can be completed. In practical applications, the system can adaptively adjust the parameters of the attitude angle adjustment algorithm according to factors such as the flight state of the virtual UAV and the change pattern of airflow disturbance to improve the accuracy and stability of attitude control.

[0051] S106. Convert the angle change amount into a force feedback signal and feedback it to the operator through a force feedback device.

[0052] In this step, the system converts the attitude angle change of the virtual UAV into a force feedback signal and transmits it to the operator through a force feedback device. Force feedback is a haptic interaction technology that simulates the tactile sensations in the real environment by applying changing forces or torques to the operator. In the virtual UAV training system, through force feedback technology, the operator can intuitively perceive the attitude changes of the virtual UAV under airflow disturbances, improving the immersion and realism of the control experience.

[0053] Specifically, the system can establish a mapping relationship between the attitude angle change and the force feedback signal, such as a linear proportional relationship, a non-linear curve relationship, etc. The force feedback signal can be represented in forms such as current, voltage, PWM wave, etc., and is used to drive the actuators in the force feedback device, such as motors, hydraulic cylinders, etc. The force feedback device can be a commercially available gamepad, joystick, etc., or a dedicated force feedback device can be customized and developed, such as a six-degree-of-freedom force feedback device based on a parallel mechanism. The system needs to calibrate and calibrate the force feedback device to ensure that the force feedback signal can be accurately transmitted to the operator.

[0054] In practical applications, the performance parameters of the force feedback device, such as the magnitude, frequency, accuracy, etc. of the feedback force, may not fully meet the requirements of virtual UAV training. To solve this problem, the system can introduce perception enhancement technology to compensate and optimize the force feedback signal through multi-channel feedback such as vision and hearing. The system can also adjust the force feedback parameters according to the physiological characteristics and control habits of the operator to improve the control comfort. At the same time, the system can set the threshold and limit of the force feedback to avoid physical damage to the operator caused by excessive feedback force or too long duration.

[0055] In the above embodiment, by establishing an airflow channel and a detection point network, an accurate model of the airflow state in the preset geographical area is realized. Based on the non-linear interpolation calculation of real-time meteorological data, the accurate meteorological data of the detection points can be obtained, and then a disturbance feature map reflecting the regional airflow distribution characteristics can be generated. By dividing the area into different airflow disturbance levels and setting buffer zones, the system can smoothly adjust the flight attitude at preset time intervals and increasing coefficients when the virtual UAV crosses different airflow regions, avoiding a sharp increase in control difficulty caused by sudden attitude changes. Converting the calculated angle change into a force feedback signal and transmitting it to the operator enables the operator to truly feel the control resistance in different airflow environments, enhancing the realism during the training process. This virtual-real interaction method based on real-time meteorological data and physical models enables the UAV simulation training system to more accurately simulate the airflow influence in the actual flight environment and helps the operator master the flight skills under complex meteorological conditions.

[0056] To further improve the authenticity and interactivity of the virtual training environment, the system also needs to consider the impact of the drone itself on the surrounding airflow environment. During actual flight, the drone is not only affected by the environmental airflow, but its high-speed rotating propellers also generate a significant wake effect. These wakes interact with the surrounding airflow, forming a more complex airflow environment. Therefore, based on the above embodiments, the present application also proposes a virtual-real interaction method considering the wake effect of the drone, which realizes a higher-fidelity flight simulation training effect by simulating the bidirectional coupling effect between the drone and the airflow environment. The following combines Figure 2 , and describes a virtual-real interaction method considering the wake effect of the drone in the embodiments of the present application: Please refer to Figure 2 , which is a schematic flowchart of a virtual-real interaction method considering the wake effect of the drone in the embodiments of the present application.

[0057] S201. Take the virtual drone as an airflow disturbance source and generate a moving disturbance field centered on the virtual drone; In this step, the system regards the virtual drone as a moving airflow disturbance source, and generates a moving disturbance field centered on the virtual drone in the virtual environment by simulating the wake effect generated by the drone propellers. The moving disturbance field is an airflow disturbance area that changes with the movement of the drone, which reflects the impact of the drone on the surrounding airflow environment. By introducing the moving disturbance field, the system can realize the bidirectional coupling effect between the drone and the airflow environment and improve the authenticity of flight simulation.

[0058] Specifically, the system can adopt the computational fluid dynamics (CFD) method, such as numerical simulation based on the Euler equation or the Navier-Stokes equation, to calculate the wake velocity field generated by the drone propellers. The system needs to set appropriate boundary conditions and initial conditions according to information such as the geometric shape of the drone, the rotation speed of the propellers, and the blade parameters, and select an appropriate turbulence model and discretization scheme to ensure the accuracy and stability of the wake field simulation. After obtaining the wake velocity field, the system can superimpose it on the background airflow field around the drone to form a moving disturbance field centered on the drone.

[0059] S202. Based on the interaction law between the moving disturbance field and the airflow channels in the preset geographical area, form a dynamic airflow environment; Based on the interaction law between the moving perturbation field and the air flow channels in the preset geographical area, the system forms a dynamic air flow environment, specifically including: calculating the perturbation propagation direction of the moving perturbation field in each air flow channel; determining the perturbation intensity of the moving perturbation field on each air flow channel according to the perturbation propagation direction; superimposing the perturbation intensity on the original air flow characteristics of each air flow channel to obtain a superimposed result, and calculating new air flow distribution data based on the superimposed result; generating a dynamic air flow environment according to the new air flow distribution data.

[0060] Among them, generating a dynamic air flow environment according to the new air flow distribution data specifically includes: extracting the air flow gradient between each air flow region and calculating the change rate of the air flow gradient over time; determining the transition characteristics between the air flow regions according to the change rate; using the continuous air flow field generated based on the transition characteristics as the dynamic air flow environment.

[0061] In this step, the system considers the interaction between the moving perturbation field and the air flow channels in the preset geographical area, and on this basis, forms a dynamically changing air flow environment. The moving perturbation field will perturb and change the original air flow channels, and the morphological characteristics of the air flow channels will also affect the propagation and attenuation of the moving perturbation field. By simulating this bidirectional coupling effect, the system can generate a more realistic and complex air flow environment, providing higher-fidelity simulation conditions for UAV flight training.

[0062] When specifically implemented, the system first needs to calculate the perturbation propagation direction of the moving perturbation field in each air flow channel. The perturbation propagation direction depends on the velocity direction of the moving perturbation field and the morphological characteristics of the air flow channel, such as the curvature of the channel, the change in cross-sectional area, etc. The system can use methods such as streamline tracing and the method of characteristics to solve the propagation path of the perturbation in the air flow channel. Then, the system calculates the perturbation intensity of the moving perturbation field on each air flow channel according to the perturbation propagation direction. The perturbation intensity is related to factors such as the velocity magnitude of the moving perturbation field, the propagation distance, and the damping characteristics of the air flow channel. The system can adopt methods such as the energy attenuation model and the turbulent dissipation model to quantitatively describe the spatial distribution law of the perturbation intensity. Next, the system superimposes the calculated perturbation intensity on the original air flow characteristics of each air flow channel to obtain the new air flow distribution data of the air flow channel under the action of the perturbation. Finally, the system generates a dynamic air flow environment according to the new air flow distribution data as the background condition for virtual UAV flight.

[0063] When generating a dynamic airflow environment, the system needs to consider the transition characteristics between different airflow regions to ensure the continuity and realism of the airflow field. The system can extract the airflow gradients between individual airflow regions and calculate the rate of change of the airflow gradients over time. The airflow gradient reflects the degree of change in the airflow velocity and direction in space. The larger the gradient, the more obvious the difference between the airflow regions. The rate of change of the airflow gradient reflects the speed of transition between the airflow regions. The system can determine the transition boundaries between different airflow regions and the size of the transition regions based on the rate of change of the airflow gradient. Within the transition regions, the system uses methods such as interpolation and smoothing to generate continuously varying airflow velocity and direction data, thus obtaining a natural and stable dynamic airflow environment.

[0064] S203. Calculate the feedback disturbance acting on the virtual unmanned aerial vehicle according to the dynamic airflow environment; The system calculates the feedback disturbance acting on the virtual unmanned aerial vehicle according to the dynamic airflow environment, specifically including: obtaining the airflow velocity and direction at the position of the virtual unmanned aerial vehicle in the dynamic airflow environment; calculating the forces acting on each force-bearing surface of the virtual unmanned aerial vehicle by the airflow velocity and direction; calculating the resultant moment according to the forces on each force-bearing surface and the position of the action points; and using the change in the attitude angle obtained by converting the resultant moment as the feedback disturbance acting on the virtual unmanned aerial vehicle.

[0065] In this step, the system calculates the feedback disturbing forces and moments acting on the virtual unmanned aerial vehicle according to the generated dynamic airflow environment. The feedback disturbance refers to the interference and influence on the unmanned aerial vehicle caused by the airflow environment, which will change the force state and flight attitude of the unmanned aerial vehicle. By calculating the feedback disturbance, the system can simulate the motion response of the unmanned aerial vehicle under complex airflow conditions and improve the pertinence and effectiveness of flight control and maneuvering training.

[0066] In specific implementation, the system first needs to obtain the airflow velocity and direction data at the position of the virtual UAV in the dynamic airflow environment. This can be achieved by setting virtual sensors in the airflow field or estimating the discrete airflow data around the UAV using interpolation algorithms. Then, the system maps the airflow velocity and direction data to each force-bearing surface of the virtual UAV, such as the wings, tail, fuselage, etc. The size, shape, and spatial position of the force-bearing surface can be determined according to the 3D model of the UAV and its current attitude. Next, the system calculates the normal and tangential components of the airflow velocity and direction on each force-bearing surface respectively, and calculates the aerodynamic forces corresponding to each component according to aerodynamic principles such as Bernoulli's equation and Kutta's theorem. The aerodynamic forces include lift, drag, side force, etc., and their magnitudes and directions depend on factors such as the angle of attack, frontal area of the force-bearing surface, and local airflow conditions. Finally, the system vectorially synthesizes the aerodynamic forces and their acting point positions on each force-bearing surface to obtain the resultant force and moment acting on the entire UAV. The resultant moment can be further converted into the change amount of the UAV's attitude angle and output as a feedback disturbance to the flight control system.

[0067] S204. Integrate the feedback disturbance with the angle change amount to obtain an integrated angle change amount; In this step, the system integrates the attitude angle change amount caused by the feedback disturbance with the active attitude adjustment amount of the UAV in the regional transition buffer zone to obtain a comprehensive attitude angle change command. The integration process takes into account the dual effects of the airflow disturbance and the control command received by the UAV, and can generate a more stable and coordinated attitude control command, avoiding adverse phenomena such as sudden changes or chattering.

[0068] In specific implementation, the system first needs to unify the attitude angle change amount caused by the feedback disturbance and the active attitude adjustment amount into the same coordinate system, such as the body coordinate system or the geographic coordinate system. This requires coordinate transformation and rotational alignment of the two change amounts based on the current attitude and position information of the UAV. Then, the system can adopt a weighted superposition method to linearly combine the two change amounts according to certain weight coefficients to obtain the integrated attitude angle change amount. The values of the weight coefficients can be determined according to factors such as the airflow disturbance characteristics of the area where the UAV is located, the flight mission requirements, and the operator's habits. Generally, the active attitude adjustment amount should play a dominant role, while the change amount caused by the feedback disturbance plays an auxiliary and corrective role.

[0069] S205. Update the force feedback signal based on the integrated angle change amount.

[0070] In this step, the system generates a new force feedback signal based on the fused angular change amount and transmits it to the operator. The update process of the force feedback signal reflects the two-way interaction between the virtual UAV and the operator. The operator can perceive the change in the UAV's attitude through the force feedback device and adjust their operation strategy and actions accordingly. At the same time, the operator's operation instructions also affect the flight state and attitude control process of the UAV, forming a closed-loop human-machine interaction system.

[0071] Specifically, when implementing, the system needs to establish a mapping relationship between the fused angular change amount and the force feedback signal. This mapping relationship can be linear or non-linear, depending on the type and characteristics of the force feedback device. For a linear mapping relationship, the system can simply multiply the angular change amount by a proportionality coefficient to obtain the change amount of the force feedback signal. For a non-linear mapping relationship, the system can use methods such as look-up table method, interpolation method, etc., to calculate the change amount of the force feedback signal according to the pre-calibrated force-angle curve. After obtaining the change amount of the force feedback signal, the system superimposes it on the original force feedback signal to obtain the updated force feedback signal. The updated force feedback signal is transmitted to the operator through the force feedback device, and the operator can judge the change in the UAV's attitude based on the characteristics such as the magnitude, direction, and frequency of the force and make corresponding operation responses.

[0072] In the above embodiment, by using the virtual UAV as an air flow disturbance source and generating a moving disturbance field, the dynamic simulation of the air flow change caused by the UAV flight is realized. The system calculates and updates the dynamic air flow environment based on the interaction law between the moving disturbance field and the existing air flow channel, and then obtains the feedback disturbance acting on the virtual UAV. After fusing this feedback disturbance with the original angular change amount, the force feedback signal is updated, enabling the system to simultaneously reflect the comprehensive influence of the environmental air flow and the UAV's own movement on the flight state. This dynamic feedback mechanism considering the interaction between the UAV and the air flow environment achieves a force feedback effect closer to the real flight state and improves the accuracy and authenticity of the aerodynamic effect simulation during the training process.

[0073] Next, the system in the embodiment of the present invention application will be described from the perspective of hardware processing. Please refer to Figure 3 , which is a schematic structural diagram of an entity device of a virtual-real interaction system for UAV simulation training flight provided by an embodiment of the present application.

[0074] It should be noted that Figure 3 The structure of the system shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0075] As Figure 3As shown, the system includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the Read-Only Memory (ROM) 302 or the program loaded from the storage section 308 into the Random Access Memory (RAM) 303, such as executing the method in the above embodiments. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0076] The following components are connected to the I / O interface 305: an input section 306 including a camera, an infrared sensor, etc.; an output section 307 including a Liquid Crystal Display (LCD), a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read from it can be installed into the storage section 308 as needed.

[0077] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication section 309, and / or installed from the removable medium 311. When the computer program is executed by the Central Processing Unit (CPU) 301, various functions defined in the present invention are executed.

[0078] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above.

[0079] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. Among them, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the above module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0080] As another aspect, the present invention also provides a computer-readable storage medium, which may be included in the system described in the above embodiments; or may exist alone without being assembled into the system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of a system, the system implements the method provided in the above embodiments.

[0081] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present application.

[0082] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if detecting (the stated condition or event)" can be interpreted as "if determining...", "in response to determining...", "when detecting (the stated condition or event)", or "in response to detecting (the stated condition or event)".

[0083] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid-state drive), etc.

[0084] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by relevant hardware instructed by a computer program. This program can be stored in a computer-readable storage medium. When this program is executed, it can include the processes of the above method embodiments. The aforementioned storage media include: various media such as ROM, random access memory (RAM), magnetic disks, or optical discs that can store program codes.

Claims

1. A virtual-real interaction method for simulated training flight of an unmanned aerial vehicle, characterized in that, Including: Dividing airflow channels in the preset geographic area according to the terrain elevation data of the preset geographic area and the principles of fluid mechanics; Performing non-linear interpolation calculation on the real-time meteorological data according to the positions of preset detection points in each of the airflow channels to obtain the meteorological data of each of the preset detection points; Generating an airflow disturbance feature map according to the meteorological data of each of the preset detection points; Based on the airflow disturbance feature map, dividing the preset geographic area into a stable airflow area, a weak disturbance area, and a strong disturbance area, and respectively setting area conversion buffer zones between the stable airflow area, the weak disturbance area, and the strong disturbance area; When it is determined that the virtual drone enters the area conversion buffer zone according to the real-time position coordinates of the virtual drone in the virtual training environment, adjusting the pitch angle, roll angle, and yaw angle of the virtual drone at a preset time interval with a preset increment coefficient to obtain an angle change amount; Converting the angle change amount into a force feedback signal and feeding it back to the operator through a force feedback device.

2. The method according to claim 1, characterized in that, The dividing of the airflow channels in the preset geographic area according to the terrain elevation data of the preset geographic area and the principles of fluid mechanics specifically includes: Converting the terrain elevation data of the preset geographic area into three-dimensional grid data; Extracting ridge lines and valley lines according to the three-dimensional grid data; Dividing the preset geographic area into multiple sub-areas with the ridge lines as the boundaries; Calculating the airflow movement direction in each of the sub-areas based on the principles of fluid mechanics; According to the airflow movement direction, combining adjacent grid units with the same airflow movement trend to form multiple airflow channels.

3. The method according to claim 1, wherein The adjusting of the pitch angle, roll angle, and yaw angle of the virtual drone at a preset time interval with a preset increment coefficient to obtain an angle change amount specifically includes: Determining the difference in airflow disturbance characteristics between the areas on both sides of the area conversion buffer zone; Calculating target attitude angles according to the difference in airflow disturbance characteristics, where the target attitude angles include a target pitch angle, a target roll angle, and a target yaw angle; Obtaining the preset increment coefficient at the current moment at a preset time interval, and the value range of the preset increment coefficient is from 0 to 1; Multiplying the difference between the target attitude angle and the current attitude angle by the preset increment coefficient to obtain an angle change amount, where the angle change amount includes a pitch angle change amount, a roll angle change amount, and a yaw angle change amount.

4. The method according to claim 1, wherein After the converting of the angle change amount into a force feedback signal and feeding it back to the operator through a force feedback device, the method further includes: Regarding the virtual drone as an airflow disturbance source and generating a moving disturbance field centered on the virtual drone; Forming a dynamic airflow environment based on the interaction law between the moving disturbance field and the airflow channels in the preset geographic area; Calculating the feedback disturbance acting on the virtual drone according to the dynamic airflow environment; Fusing the feedback disturbance with the angle change amount to obtain a fused angle change amount; Updating the force feedback signal based on the fused angle change amount.

5. The method according to claim 4, wherein The forming of the dynamic airflow environment based on the interaction law between the moving disturbance field and the airflow channels in the preset geographic area specifically includes: Calculate the disturbance propagation direction of the moving disturbance field in each of the air flow channels; Determine the disturbance intensity of the moving disturbance field on each of the air flow channels according to the disturbance propagation direction; Superimpose the disturbance intensity on the original air flow characteristics of each of the air flow channels to obtain a superimposed result, and calculate new air flow distribution data based on the superimposed result; Generate a dynamic air flow environment according to the new air flow distribution data.

6. The method according to claim 5, wherein The generating a dynamic air flow environment according to the new air flow distribution data specifically includes: Extract the air flow gradient between each air flow region, and calculate the change rate of the air flow gradient over time; Determine the transition characteristics between the air flow regions according to the change rate; Use the continuous air flow field generated based on the transition characteristics as the dynamic air flow environment.

7. The method according to claim 4, characterized in that The calculating the feedback disturbance acting on the virtual unmanned aerial vehicle according to the dynamic air flow environment specifically includes: Obtain the air flow velocity and direction at the position of the virtual unmanned aerial vehicle in the dynamic air flow environment; Calculate the acting force of the air flow velocity and direction on each force-bearing surface of the virtual unmanned aerial vehicle; Calculate the resultant moment according to the acting force on each force-bearing surface and the position of the acting point; Use the attitude angle change amount obtained by converting the resultant moment as the feedback disturbance acting on the virtual unmanned aerial vehicle.

8. A virtual-real interaction system for simulated training flight of an unmanned aerial vehicle, characterized in that The system includes: One or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the system to execute the method according to any one of claims 1-7.

9. A computer-readable storage medium, comprising instructions, characterized in that, When the instructions run on the system, enable the system to execute the method according to any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product runs on the system, enable the system to execute the method according to any one of claims 1-7.

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