Wind field data modeling method and system for small and medium-sized unmanned aerial vehicle wind resistance test

Abstract

The invention discloses a wind field data modeling method and system for a small and medium-sized unmanned aerial vehicle wind resistance test, and the method comprises the steps: collecting the topographic data, wind speed data and fan data of a test wind field, and determining a preset region and an unmeasured region according to the preset trajectory of an unmanned aerial vehicle based on the test wind field; the method comprises the following steps: acquiring boundary conditions of an unmeasured area and disturbance grid wake flow through fluid simulation based on topographic data and a preset area, performing wind speed vector decomposition on a grid according to a preset wind field boundary condition, and acquiring a preset wind field model according to a disturbance time sequence, an unmanned aerial vehicle maneuvering weight and the disturbance grid wake flow, and obtaining a predicted wind field of the unmeasured area through Gaussian process regression according to the boundary condition of the unmeasured area based on a preset wind field model, and coupling the predicted wind field through a grid time sequence by using different unmanned aerial vehicle preset trajectories to obtain a target wind field model. According to the method, through space-time collaborative interpolation and fluid simulation of Gaussian process regression, the inlet boundary condition better conforms to the fluid mechanics law, and the physical consistency and reliability of the target wind field are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wind field modeling, in particular to a wind field data modeling method and system for wind resistance test of small and medium-sized unmanned aerial vehicles. BACKGROUND

[0002] Small and medium-sized unmanned aerial vehicles have been widely used in aerial surveying and mapping, power inspection, agricultural plant protection, emergency rescue and other fields due to their high flexibility, low cost and convenient deployment. With the extension of application scenarios to complex weather environments, the wind resistance performance becomes a core indicator determining the safety and reliability of the task. As a key link of unmanned aerial vehicle research and verification, the wind resistance test needs to rely on accurate wind field models to simulate real wind environment. Currently, the wind field modeling mainly adopts the combination of computational fluid dynamics (CFD) simulation, wind tunnel test and field wind speed collection, and a spatio-temporal distribution model of wind speed and direction is constructed to provide input conditions for unmanned aerial vehicle wind resistance performance test.

[0003] The existing technology has limitations in wind field modeling. On the one hand, CFD simulation can generate a three-dimensional wind field, but it deviates greatly from the actual flight environment, making it difficult to restore dynamic wind characteristics such as turbulence and gusts. Limited by physical space, it is unable to reproduce large-scale and multi-scenario wind field changes. Field data collection is easily affected by sensor accuracy and environmental interference, and the wind field prediction of unmeasured areas relies on simple interpolation, lacking quantitative wind field disturbance influence on different trajectories under the demand of multi-unmanned aerial vehicle and multi-trajectory wind resistance test. Therefore, how to simply and efficiently perform CFD simulation wind field and construct accurate local wind field data has become a problem to be solved. SUMMARY

[0004] The present application provides a wind field data modeling method and system for wind resistance test of small and medium-sized unmanned aerial vehicles, aiming to solve at least one of the above technical problems.

[0005] In the first aspect, to achieve the above-mentioned purpose, the present application provides a wind field data modeling method for wind resistance test of small and medium-sized unmanned aerial vehicles, comprising:

[0006] Collecting topographic data, wind speed data and fan data of the test wind field, determining a preset area and an unmeasured area based on the preset trajectory of the unmanned aerial vehicle according to the test wind field;

[0007] Based on the topographic data, the preset area is divided into grids, the wind speed data and fan data are aligned with the grid coordinates, the preset wind field boundary conditions are obtained, and the unmeasured area boundary conditions and disturbance grid wake are obtained through CFD fluid simulation according to the preset wind field boundary conditions;

[0008] According to the preset wind field boundary conditions, the grid is subjected to wind speed vector decomposition, and the disturbance displacement and unmanned aerial vehicle maneuvering weight are obtained based on the wind speed vector and the flight deviation of unmanned aerial vehicles with different frequencies.

[0009] sequentially analyzing the perturbation displacement according to the grid coordinates, obtaining a preset wind field model according to the perturbation time sequence, the unmanned aerial vehicle maneuvering weight, and the perturbation grid wake flow;

[0010] obtaining a predicted wind field of the unmeasured area through Gaussian process regression based on the preset wind field model and the boundary conditions of the unmeasured area, and coupling the predicted wind field through grid time sequence using different preset trajectories of the unmanned aerial vehicle to obtain a target wind field model.

[0011] Further, the method for determining the preset area and the unmeasured area comprises:

[0012] collecting topographic data, wind speed data, and wind turbine data of the test wind field, calculating a minimum horizontal enclosing rectangle and a maximum height difference in the vertical direction based on the trajectory coordinates of the preset trajectory of the unmanned aerial vehicle according to the flight period to obtain a three-dimensional extreme envelope of the trajectory, fitting a preset safety distance based on the preset wind resistance level and the flight speed of the unmanned aerial vehicle, if the preset wind resistance level of the unmanned aerial vehicle is improved by one level, the safety distance is expanded by 15%, if the flight speed of the unmanned aerial vehicle is doubled in unit time, the safety distance is expanded by one-half, and the preset area is obtained.

[0013] a one-kilometer range around the preset area is taken as a preset buffer area, the slope change rate and the obstacle density in the preset buffer area are counted according to the topographic data, the terrain complexity is obtained by weighting and summing the slope change rate and the obstacle density, the terrain complexity is taken as an exponential decay coefficient to multiply the preset buffer area to obtain an actual buffer area, and the test wind field outside the preset area and the actual buffer area is taken as the unmeasured area.

[0014] Further, the method for obtaining the boundary conditions of the unmeasured area and the perturbation grid wake flow comprises:

[0015] the preset area is divided by a structured grid based on the topographic data, the grid resolution is adjusted according to the spatial density of the trajectory coordinates, the grid is encrypted by a multiple according to the slope difference in the terrain mutation area, and the sampling point coordinates of the wind speed data and the installation coordinates of the wind turbine are converted to the same spatial coordinate system as the grid.

[0016] the wind speed data and the wind turbine data are distributed to each grid unit through a spatial interpolation algorithm according to the grid coordinates to form the boundary conditions of the preset wind field, a calculation domain containing the preset area and the unmeasured area is constructed according to the boundary conditions of the preset wind field, and an inlet boundary, an outlet boundary, and a wall boundary are set.

[0017] the fluid motion control equation is solved by detached eddy simulation based on the calculation domain to obtain the boundary conditions of the unmeasured area, and the perturbation grid wake flow is extracted, the perturbation grid wake flow comprises a wake flow diffusion range, a wind speed decay law, a gust duration, a turbulent intensity distribution characteristic, and a turbulent time scale.

[0018] Further, the method for obtaining the disturbance displacement and the UAV maneuvering weight comprises:

[0019] obtaining the wind speed vector of each grid based on the preset wind field boundary condition, decomposing the wind speed vector into an axial component along the preset trajectory of the UAV and a radial component perpendicular to the trajectory, taking the initial state of the position, speed and attitude of the UAV in the preset trajectory as the integral initial value according to the axial component and the radial component, and obtaining the wind disturbance theoretical deviation of the preset trajectory under the action of the wind field alone according to the integral initial value through the preset aerodynamic model;

[0020] taking the initial state as the simulation initial value, performing windless simulation according to the flight control instruction of the preset trajectory through the UAV maneuvering dynamics equation to obtain the disturbance displacement component under the action of each flight control instruction alone, obtaining the actual flight deviation according to the actual flight trajectory of the UAV and the preset trajectory of the UAV, removing the abnormal points of sensor noise and electromagnetic interference from the actual flight deviation through the anti-outlier filtering, calculating the Pearson correlation coefficient of the filtered deviation and the disturbance displacement component, deducting the disturbance displacement component if the correlation coefficient is greater than 0.7, and obtaining the actual wind disturbance deviation, and taking the filtered deviation as the actual wind disturbance deviation if the correlation coefficient is less than 0.3;

[0021] determining the embedding dimension through the false nearest neighbor method according to the actual wind disturbance deviation, determining the delay time through the mutual information method, reconstructing the phase space of the actual wind disturbance deviation according to the embedding dimension and the delay time, mapping the one-dimensional time series actual wind disturbance deviation into a high-dimensional disturbance displacement vector, taking the L2 norm of the high-dimensional disturbance displacement vector and the wind disturbance theoretical deviation as the disturbance displacement, and taking the ratio of the wind disturbance theoretical deviation and the disturbance displacement as the UAV maneuvering weight.

[0022] Further, the method for obtaining the preset wind field model comprises:

[0023] extracting the disturbance displacement based on the grid coordinates and the flight data sampling time step, obtaining a three-dimensional matrix, taking the gust duration as a short-term window length, determining a long-term window length according to the turbulence time scale, extracting the short-term disturbance standard deviation, the maximum modulus, the minimum modulus and the long-term disturbance mean through the short-term window length and the long-term window length sliding based on the three-dimensional matrix, and obtaining a four-dimensional feature matrix according to the network coordinates;

[0024] According to the three-dimensional matrix, the disturbance displacement is corrected coordinate by coordinate, if the grid unmanned aerial vehicle maneuvering weight is greater than 0.7, the product of the disturbance displacement and the unmanned aerial vehicle maneuvering inverse weight is taken as the correction value, if the unmanned aerial vehicle maneuvering weight is less than 0.3, it is kept unchanged, if the network is located in the wake diffusion range, the wind speed decay law value corresponding to the grid coordinate is taken as the weight coefficient of the disturbance displacement, a corrected three-dimensional matrix is obtained, the corrected three-dimensional matrix is used to perform inversion through a preset aerodynamic model, and an inversion wind speed and an inversion wind direction are obtained, and the wind field dynamic characteristics are calculated according to the inversion wind speed, the inversion wind direction and the four-dimensional feature matrix, and the wind field dynamic characteristics calculation formula is:

[0025]

[0026] Wherein γ(t) is the wind field dynamic characteristic at time t, s is the short-term window length, Δt is the flight data sampling time step, h is the time index, d(h) is the disturbance displacement, is the average disturbance displacement in the short-term window, tanh is the hyperbolic tangent function, f is the long-term window length, d max is the maximum disturbance displacement in the short-term window, d min is the minimum disturbance displacement in the short-term window.

[0027] Based on the inversion wind speed, the inversion wind direction and the wind field dynamic characteristics, the Kriging interpolation is carried out on the preset area and the unmeasured area, and a preset wind field model is obtained.

[0028] Further, the method for obtaining the predicted wind field comprises:

[0029] Based on the unmeasured area boundary condition, the wake diffusion boundary and the wind speed decay law are extracted, the grid data of the unmeasured area is corrected through the preset wind field model according to the wake diffusion boundary and the wind speed decay law, the prior mean value of the prediction point is obtained, and the prediction point of the unmeasured area is calculated by using the prior mean value of the prediction point of the unmeasured area and the grid data of the preset area through Gaussian process regression, and the Gaussian process regression formula is:

[0030]

[0031] Wherein is the prediction point of the unmeasured area, μ(x u ,t u ) is the prior mean value of the prediction point, N is the total number of spatial grids of the preset area, M is the time step of the preset area, x i is the spatial coordinate of the i th grid of the preset area, t k is the time stamp of the k th time step of the preset area, is the inversion wind speed of the i th grid and the k th time step of the preset area, is the average wind speed, x u is the prediction point coordinate of the unmeasured area, t uFor the predicted timestamp, x u For the predicted spatial coordinates;

[0032] Align the point set of the unmeasured area predicted point with the grid and time step of the preset area to obtain the predicted wind field of the unmeasured area.

[0033] Further, the method for obtaining the target wind field model comprises:

[0034] Based on different preset trajectories of the unmanned aerial vehicle, obtain discrete preset trajectory points according to the grid coordinates and the flight data sampling time step, and obtain the required wind speed and the required wind direction according to the anti-wind experiment requirements;

[0035] According to the grid coordinates of the preset trajectory points, obtain the predicted wind speed and the predicted wind direction through the predicted wind field, take the predicted wind speed and the predicted wind direction of the preset trajectory points as the target initial value, and according to the required wind speed, the required wind direction and the target initial value, perform iterative optimization through the target wind field loss function to obtain the target wind field with minimized trajectory deviation, and the target wind field loss function expression is:

[0036]

[0037] Wherein L is a loss function, represents the deviation of the predicted wind field and the multiple unmanned aerial vehicle trajectories, n is the number of discrete points of a single trajectory, v p,i is the required wind speed of the i-th point of the preset trajectory, v t,i is the actual wind speed of the predicted wind field at the i-th point of the trajectory, θ p,i is the required wind direction of the i-th point of the preset trajectory, θ t,i is the actual wind direction of the predicted wind field at the i-th point of the trajectory, and lambda is a smoothing coefficient, and Delta t is a flight data sampling time step.

[0038] In the second aspect, in order to achieve the above object, the present application also provides a wind field data modeling system for anti-wind test of small and medium-sized unmanned aerial vehicles, comprising:

[0039] The wind field preset area acquisition module is used for collecting topographic data, wind speed data and fan data of a test wind field, determining a preset area and an unmeasured area based on the test wind field according to a preset trajectory of an unmanned aerial vehicle;

[0040] The wind field boundary condition acquisition module is used for performing grid division on the preset area based on the topographic data, aligning the wind speed data, the fan data and the grid coordinates to obtain preset wind field boundary conditions, and obtaining unmeasured area boundary conditions and disturbed grid wake through CFD fluid simulation according to the preset wind field boundary conditions;

[0041] The wind field disturbance acquisition module is configured to perform wind speed vector decomposition on a grid according to preset wind field boundary conditions, perform anti-outlier filtering and phase space reconstruction based on wind speed vectors and different frequency UAV flight deviations, and obtain disturbance displacement and UAV maneuvering weight.

[0042] The preset wind field modeling module is configured to perform time series analysis on the disturbance displacement according to grid coordinates, and obtain a preset wind field model according to disturbance time series, UAV maneuvering weight, and disturbance grid wake.

[0043] The target wind field modeling module is configured to obtain a predicted wind field of the unmeasured area based on the preset wind field model according to unmeasured area boundary conditions through Gaussian process regression, and obtain a target wind field model by coupling the predicted wind field with the grid time series using different UAV preset trajectories.

[0044] The present application has the following advantages:

[0045] The present application uses a space-time Gaussian process regression space-time collaborative interpolation model to calculate grid wind speed vectors in combination with terrain data and anemometer data, fully utilizes historical wind speed prior information and multi-time-space point measured data, and improves the prediction accuracy of preset wind field area wind speed vectors. Meanwhile, the wind field interference characteristics are inversed based on UAV attitude data, the local wind field details around the UAV flight trajectory can be captured, the feature integrity of the three-dimensional wind field is improved, the fluid simulation is combined with filtering to eliminate noise and mutations, the inlet boundary conditions are more consistent with the fluid mechanics law, and the physical consistency and reliability of the target wind field are improved. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 The present application is a wind field data modeling method for small and medium-sized UAV wind resistance test. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application, and are not used to limit the present application.

[0048] Referring to Figure 1 The present application provides a wind field data modeling method for small and medium-sized UAV wind resistance test, which comprises:

[0049] Collecting terrain data, wind speed data and fan data of a test wind field, determining a preset area and an unmeasured area based on the test wind field according to a UAV preset trajectory;

[0050] Grid the preset area based on the terrain data, align the wind speed data, fan data and grid coordinates, obtain the preset wind field boundary condition, obtain the unmeasured area boundary condition and disturbance grid wake through CFD fluid simulation according to the preset wind field boundary condition;

[0051] According to the preset wind field boundary condition, the grid is subjected to wind speed vector decomposition, and the anti-outlier filtering and phase space reconstruction are carried out based on the wind speed vector and the flight deviation of the unmanned aerial vehicle with different frequencies, so as to obtain the disturbance displacement and the unmanned aerial vehicle maneuvering weight;

[0052] According to the grid coordinates, the disturbance displacement is subjected to time series analysis, and the preset wind field model is obtained according to the disturbance time series, the unmanned aerial vehicle maneuvering weight and the disturbance grid wake;

[0053] Based on the preset wind field model, the unmeasured area is obtained through Gaussian process regression according to the unmeasured area boundary condition, different unmanned aerial vehicle preset trajectories are used to couple the grid time series and the predicted wind field, and the target wind field model is obtained.

[0054] In actual evaluation, a test area is a mountainous area of 5km by 3km, including valleys, steep slopes, and small unmanned aerial vehicles with a maximum take-off weight of 3.5kg, a designed wind resistance level of 12m / s, DEM data with a resolution of 1m collected in the test area, wind speed data collected from 5 points around the test environment, 2 small wind turbines in the area, recording the start-stop state and wind speed intervention range, planning 3 preset trajectories of unmanned aerial vehicles, the trajectory length is 5km, the flight height is 60m, the distance from the line is 8m, the flight period is 10min, including 3 times of gust simulation, according to the three-dimensional extreme envelope of the preset trajectory, the preset area range of 3.45km by 0.58km by 30m is obtained, the terrain complexity in the preset buffer area is counted, and the area ratio of the slope change rate greater than 15° is 30%, the obstacle density is 2 / km 2 , the terrain complexity is calculated by weighting and is 0.6, 0.6 is taken as an exponential decay coefficient to multiply the preset buffer area, the preset area range is expanded by 1.2km to obtain the actual buffer area, and the test wind field outside the preset area and the actual buffer area is taken as the unmeasured area;

[0055] In the actual evaluation, the preset area is structured grid division, the grid resolution is set to 5m square, the terrain slope difference greater than 20° grid is encrypted to 2m, the total grid number is 1.2 million, the anemometer sampling point, the fan installation coordinate is converted to the grid coordinate system, and the wind speed data is distributed to the grid through Kriging interpolation, the preset wind field boundary condition of the inlet wind speed 8m / s, the outlet free flow, and the wind speed superposition intervention value of the fan area is obtained, the preset wind field boundary condition is used for CFD fluid simulation, the calculation domain including the preset area and the unmeasured area is constructed, and the inlet, outlet and wall boundary conditions are set, the DES is used to solve the fluid motion control equation, the unmeasured area boundary condition of the wake diffusion range 200m, the wind speed attenuation 30% at 100m away from the fan, the gust duration 5-7s, and the terrain complex area turbulence intensity 15% is obtained, and the wake diffusion range and wind speed attenuation law in the disturbance grid wake are extracted;

[0056] In the actual evaluation, the grid wind speed vector is decomposed according to the preset wind field boundary condition of the test area, the initial position (0, 0, 0) of the preset trajectory of the unmanned aerial vehicle, the speed 8m / s, and the attitude horizontal are taken as the integral initial value, the wind disturbance theoretical deviation under the action of the wind field alone is calculated through the aerodynamic model of the preset lift coefficient and the drag coefficient, wherein the trajectory offset is ±0.5m, the wind direction deviation is ±5°, the initial state is taken as the simulation initial value, the disturbance displacement component of the flight control alone is obtained by simulating the flight control instruction action through the unmanned aerial vehicle dynamics equation, wherein the trajectory offset is ±0.2m, the actual flight deviation is removed by anti-outlier filtering to eliminate abnormal points caused by electromagnetic interference, the Pearson correlation coefficient of the filtered deviation and the flight control disturbance displacement component is calculated, the flight control disturbance displacement component is deducted from the part with the correlation coefficient greater than 0.7 in the gust section, and the actual wind disturbance deviation is obtained as the trajectory offset ±0.8m, the part with the correlation coefficient less than 0.3 in the stable wind section is directly taken as the trajectory offset ±0.3m); for the actual wind disturbance deviation, the embedding dimension is 3 by false nearest neighbor method, the delay time is 0.2s by mutual information method, the actual wind disturbance deviation is reconstructed in phase space, the disturbance displacement of the gust section is 1.2, the stable section is 0.5, the unmanned aerial vehicle maneuvering weight of the gust section is 0.7, and the stable section is 0.3;

[0057] In the actual evaluation, a short-term window length of 5s and a long-term window length of 20s are set according to the three-dimensional matrix of disturbance displacement, the short-term disturbance standard deviation and the long-term disturbance mean are extracted by sliding, a four-dimensional feature matrix is formed, the grid of the gust core area with a maneuvering weight greater than 0.7 is corrected by the product of displacement and inverse weight; the grid of the tail flow diffusion range 200m downstream of the fan is corrected by the wind speed attenuation law, the inversion wind speed of 12m / s in the gust section and 8m / s in the stable section are obtained through the aerodynamic model inversion, the inversion wind direction with a perpendicular component of the trajectory of 30° is obtained, the dynamic characteristics of the wind field are calculated by combining the four-dimensional feature matrix, the gust section γ(t) is 0.8 and the stable section is 0.3, the unmeasured area grid data is corrected by the preset wind field model, the wind speed of the unmeasured area prediction point is calculated by Gaussian process regression, the unmeasured area prediction wind field is obtained, the wind speed attenuation of the complex terrain area is 15%, the gust influence range is expanded by 10%, 3 preset trajectories are discretized, 6000 trajectory points are obtained, the precision is 10min / 0.1s, the required wind speed in the stable section is 8m / s±1m / s and the required wind speed in the gust section is 12m / s±2m / s, the required wind direction is ±10° with the trajectory, the predicted wind speed in the stable section is 7.8m / s and the predicted wind speed in the gust section is 11.5m / s, which are used as the target initial value, the loss function is iteratively optimized, the smoothing coefficient is set to 0.5, the trajectory deviation is minimized, the target wind field is obtained, the deviation of the gust section is 0.6 and the deviation of the stable section is 0.3, and the target wind field is obtained according to the deviation.

[0058] In the embodiment, the method for determining the preset area and the unmeasured area comprises:

[0059] The terrain data, wind speed data and fan data of the test wind field are collected, the horizontal direction minimum enclosing rectangle and the vertical direction maximum height difference are calculated according to the flight period based on the trajectory coordinates of the preset unmanned aerial vehicle trajectory, the trajectory three-dimensional extreme envelope is obtained, the preset safety distance is fitted according to the preset wind resistance level and flight speed of the unmanned aerial vehicle, if the preset wind resistance level of the unmanned aerial vehicle is improved by one level, the safety distance is expanded by 15%, if the speed of the unmanned aerial vehicle is doubled in unit time, the safety distance is expanded by one half, and the preset area is obtained.

[0060] A one-kilometer range around the preset area is used as a preset buffer area, the slope change rate and obstacle density in the preset buffer area are counted according to the terrain data, the terrain complexity is obtained by using the slope change rate and obstacle density through weighting and summing, the terrain complexity is used as an exponential attenuation coefficient to multiply the preset buffer area, the actual buffer area is obtained, and the test wind field outside the preset area and the actual buffer area is used as the unmeasured area.

[0061] In the embodiment, the method for obtaining the boundary condition of the unmeasured area and the tail flow of the disturbance grid comprises:

[0062] The preset area is divided by a structured grid based on the terrain data, the grid resolution is adjusted according to the spatial density of the track coordinates, and the grid is encrypted by a multiple in the terrain mutation area according to the slope difference; the sampling point coordinates of the wind speed data and the installation coordinates of the fan are converted to the same spatial coordinate system as the grid;

[0063] The wind speed data and the fan data are distributed to each grid unit by a spatial interpolation algorithm according to the grid coordinates, a preset wind field boundary condition is formed, a calculation domain including the preset area and the unmeasured area is constructed according to the preset wind field boundary condition, and an inlet boundary, an outlet boundary and a wall boundary are set;

[0064] Based on the calculation domain, the fluid motion control equation is solved by the detached vortex simulation to obtain the boundary condition of the unmeasured area, and the disturbance grid wake is extracted, the disturbance grid wake including a wake diffusion range, a wind speed decay law, a gust duration, a turbulent intensity distribution characteristic and a turbulent time scale.

[0065] In this embodiment, the method for obtaining the disturbance displacement and the UAV maneuvering weight comprises:

[0066] Based on the preset wind field boundary condition, the wind speed vector of each grid is obtained, the wind speed vector is decomposed into an axial component along the preset track of the UAV and a radial component perpendicular to the track, the initial state of the position, the speed and the attitude in the preset track of the UAV is taken as an integral initial value according to the axial component and the radial component, and the wind disturbance theoretical deviation of the preset track under the action of the wind field is obtained by a preset aerodynamic model according to the integral initial value;

[0067] The initial state is taken as a simulation initial value, the UAV is simulated without wind by a UAV maneuvering dynamics equation according to the flight control instruction of the preset track, a disturbance displacement component under the action of each flight control instruction is obtained, an actual flight deviation is obtained according to the actual flight track of the UAV and the preset track of the UAV, abnormal points of sensor noise and electromagnetic interference are removed by anti-outlier filtering on the actual flight deviation, a Pearson correlation coefficient of the filtered deviation and the disturbance displacement component is calculated, if the correlation coefficient is greater than 0.7, the disturbance displacement component is deducted, an actual wind disturbance deviation is obtained, and if the correlation coefficient is less than 0.3, the filtered deviation is taken as the actual wind disturbance deviation;

[0068] The embedding dimension is determined by the false nearest neighbor method according to the actual wind disturbance deviation, the delay time is determined by the mutual information method, the actual wind disturbance deviation is reconstructed in a phase space according to the embedding dimension and the delay time, the one-dimensional time series actual wind disturbance deviation is mapped into a high-dimensional disturbance displacement vector, the L2 norm of the high-dimensional disturbance displacement vector and the wind disturbance theoretical deviation is taken as the disturbance displacement, and the ratio of the wind disturbance theoretical deviation to the disturbance displacement is taken as the UAV maneuvering weight.

[0069] In this embodiment, the method for obtaining the preset wind field model comprises:

[0070] The disturbance displacement is extracted based on the grid coordinates and the flight data sampling time step, a three-dimensional matrix is obtained, the gust duration is taken as a short-term window length, a long-term window length is determined according to a turbulence time scale, the short-term disturbance standard deviation, the maximum modulus length, the minimum modulus length and the long-term disturbance mean are extracted by sliding through the short-term window length and the long-term window length based on the three-dimensional matrix, and a four-dimensional feature matrix is obtained according to the network coordinates;

[0071] The disturbance displacement is corrected according to the three-dimensional matrix, if the unmanned aerial vehicle maneuvering weight of the grid is greater than 0.7, the product of the disturbance displacement and the unmanned aerial vehicle maneuvering inverse weight is taken as a correction value, if the unmanned aerial vehicle maneuvering weight is less than 0.3, the correction value is kept unchanged, if the network is located in a wake diffusion range, the wind speed attenuation law value corresponding to the grid coordinates is taken as a weight coefficient of the disturbance displacement, a corrected three-dimensional matrix is obtained, the corrected three-dimensional matrix is used to perform inversion through a preset aerodynamic model, an inversion wind speed and an inversion wind direction are obtained, and a wind field dynamic feature is calculated according to the inversion wind speed, the inversion wind direction and the four-dimensional feature matrix, the wind field dynamic feature calculation formula is:

[0072]

[0073] Wherein γ(t) is the wind field dynamic feature at time t, s is the short-term window length, Δt is the flight data sampling time step, h is a time index, d(h) is the disturbance displacement, is the disturbance displacement mean in the short-term window, tanh is the hyperbolic tangent function, f is the long-term window length, d max is the maximum disturbance displacement in the short-term window, d min is the minimum disturbance displacement in the short-term window.

[0074] The preset area and the unmeasured area are subjected to Kriging interpolation based on the inversion wind speed, the inversion wind direction and the wind field dynamic feature, and a preset wind field model is obtained.

[0075] In the embodiment, the method for obtaining the predicted wind field comprises:

[0076] The wake diffusion boundary and the wind speed attenuation law are extracted based on the unmeasured area boundary condition, the grid data of the unmeasured area is corrected through the preset wind field model according to the wake diffusion boundary and the wind speed attenuation law, the prior mean value of the prediction point is obtained, and the prediction point of the unmeasured area is calculated by using the prior mean value of the prediction point of the unmeasured area and the grid data of the preset area through Gaussian process regression, the Gaussian process regression formula is:

[0077]

[0078] Wherein is the prediction point of the unmeasured area, μ(x u , t u) is the prior mean of the prediction point, N is the total number of spatial grids of the preset area, M is the time step number of the preset area, x i is the spatial coordinate of the i-th grid of the preset area, t k is the time stamp of the k-th time step of the preset area, is the inversion wind speed of the i-th grid and the k-th time step of the preset area, is the mean wind speed, x u is the prediction point coordinate of the unmeasured area, t u is the predicted time stamp, x u is the predicted spatial coordinate;

[0079] The point set of the prediction point of the unmeasured area is aligned with the grid and time step of the preset area, and the predicted wind field of the unmeasured area is obtained.

[0080] In the embodiment, the method for obtaining the target wind field model comprises:

[0081] Based on different preset trajectories of unmanned aerial vehicles, the grid coordinates and the flight data sampling time step length are used to obtain discrete preset trajectory points, and the preset trajectory point acquisition requirement wind speed and requirement wind direction are obtained according to the wind resistance experiment requirement;

[0082] The grid coordinates of the preset trajectory points are used to obtain the predicted wind speed and the predicted wind direction through the predicted wind field, the predicted wind speed and the predicted wind direction of the preset trajectory points are taken as target initial values, the target wind field loss function is used to iteratively optimize according to the requirement wind speed, the requirement wind direction and the target initial value, and the target wind field with minimized trajectory deviation is obtained, and the target wind field loss function expression is:

[0083]

[0084] Wherein L is a loss function, represents the deviation of the predicted wind field and the multiple unmanned aerial vehicle trajectories, n is the number of discrete points of a single trajectory, v p,i is the requirement wind speed of the i-th point of the preset trajectory, v t,i is the actual wind speed of the predicted wind field at the i-th point of the trajectory, θ p,i is the requirement wind direction of the i-th point of the preset trajectory, θ t,i is the actual wind direction of the predicted wind field at the i-th point of the trajectory, λ is a smoothing coefficient, and Δt is a flight data sampling time step length.

[0085] In the second aspect, in order to achieve the above object, the present application also provides a wind field data modeling system for wind resistance test of small and medium-sized unmanned aerial vehicles, comprising:

[0086] The wind field preset area acquisition module is used for collecting topographic data, wind speed data and fan data of the test wind field, and determining the preset area and the unmeasured area based on the preset trajectory of the unmanned aerial vehicle;

[0087] The wind field boundary condition acquisition module is configured to perform grid division on the preset area based on the terrain data, align the wind speed data, the wind turbine data and the grid coordinates, obtain preset wind field boundary conditions, and obtain unmeasured area boundary conditions and disturbed grid wake through CFD fluid simulation according to the preset wind field boundary conditions;

[0088] The wind field disturbance acquisition module is configured to perform wind speed vector decomposition on the grid according to the preset wind field boundary conditions, perform anti-outlier filtering and phase space reconstruction based on the wind speed vector and the flight deviation of the unmanned aerial vehicle with different frequencies, and obtain disturbance displacement and unmanned aerial vehicle maneuvering weight.

[0089] The preset wind field modeling module is configured to perform time series analysis on the disturbance displacement according to the grid coordinates, and obtain a preset wind field model according to the disturbance time series, the unmanned aerial vehicle maneuvering weight and the disturbed grid wake.

[0090] The target wind field modeling module is configured to obtain a predicted wind field of the unmeasured area based on the preset wind field model according to the unmeasured area boundary conditions through Gaussian process regression, and obtain a target wind field model by coupling the predicted wind field through grid time series with different unmanned aerial vehicle preset trajectories.

[0091] The above description is only the preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for modeling wind field data in wind resistance tests of small and medium-sized unmanned aerial vehicles (UAVs), characterized in that, Includes the following steps: Collect terrain data, wind speed data, and wind turbine data of the test wind field, and determine the preset area and unmeasured area based on the test wind field according to the preset trajectory of the UAV; Based on the terrain data, the preset area is divided into grids, and the wind speed data, wind turbine data and grid coordinates are aligned to obtain the preset wind field boundary conditions. Based on the preset wind field boundary conditions, the boundary conditions of the unmeasured area and the wake of the disturbed grid are obtained by CFD fluid simulation. Based on the preset wind field boundary conditions, the grid is decomposed into wind speed vectors. Based on the wind speed vectors and UAV flight deviations of different frequencies, anti-outlier filtering and phase space reconstruction are performed to obtain disturbance displacement and UAV maneuver weights. The disturbance displacement is analyzed over time based on the grid coordinates, and a preset wind field model is obtained based on the disturbance time sequence, UAV maneuver weights, and the wake of the disturbance grid. Based on the preset wind field model, the predicted wind field of the undetermined area is obtained through Gaussian process regression according to the boundary conditions of the undetermined area. The wind field is predicted by using different preset UAV trajectories through grid temporal coupling to obtain the target wind field model.

2. The wind field data modeling method for wind resistance testing of small and medium-sized UAVs according to claim 1, characterized in that, The method for determining the preset region and the unmeasured region includes: Collect terrain data, wind speed data, and wind turbine data of the test wind field. Based on the trajectory coordinates of the UAV's preset trajectory, calculate the minimum horizontal enclosing rectangle and the maximum vertical height difference according to the flight cycle to obtain the trajectory's three-dimensional extreme value envelope. Based on the trajectory's three-dimensional extreme value envelope, fit a preset safety distance according to the UAV's preset wind resistance level and flight speed. If the UAV's preset wind resistance level increases by one level, the safety distance is expanded by 15%. If the UAV's speed doubles per unit time, the safety distance is expanded by half to obtain the preset area. The area within one kilometer of the preset zone is designated as the preset buffer zone. The slope change rate and obstacle density within the preset buffer zone are statistically analyzed based on terrain data. The terrain complexity is obtained by weighted summation of the slope change rate and obstacle density. The terrain complexity is then multiplied by the preset buffer zone as an exponential decay coefficient to obtain the actual buffer zone. The test wind field outside the preset zone and the actual buffer zone is designated as the unmeasured zone.

3. The wind field data modeling method for wind resistance testing of small and medium-sized UAVs according to claim 1, characterized in that, The method for obtaining the boundary conditions of the unmeasured region and the wake of the perturbed grid includes: Based on the terrain data, the preset area is divided into a structured grid. The grid resolution is adjusted according to the spatial density of the trajectory coordinates. For areas with abrupt changes in terrain, the grid is densified by multiples based on the slope difference. The sampling point coordinates of the wind speed data and the installation coordinates of the wind turbine are converted to the same spatial coordinate system as the grid. Based on the grid coordinates, wind speed data and wind turbine data are distributed to each grid cell through a spatial interpolation algorithm to form preset wind field boundary conditions. Based on the preset wind field boundary conditions, a computational domain containing preset areas and unmeasured areas is constructed, and inlet boundaries, outlet boundaries, and wall boundaries are set. Based on the computational domain, the fluid motion control equations are solved by vortex simulation to obtain the boundary conditions of the unmeasured area and extract the perturbation grid wake. The perturbation grid wake includes the wake diffusion range, wind speed attenuation law, gust duration, turbulence intensity distribution characteristics and turbulence time scale.

4. The wind field data modeling method for wind resistance testing of small and medium-sized UAVs according to claim 1, characterized in that, The method for obtaining the disturbance displacement and UAV maneuver weights is as follows: The wind speed vector of each grid is obtained based on the preset wind field boundary conditions. The wind speed vector is decomposed into an axial component along the preset trajectory of the UAV and a radial component perpendicular to the trajectory. The initial state of position, velocity and attitude of the UAV in the preset trajectory is used as the initial value of integration based on the axial component and the radial component. The theoretical deviation of wind disturbance of the preset trajectory under the action of wind field alone is obtained through the preset aerodynamic model based on the initial value of integration. Using the initial state as the initial value for simulation, the drone dynamics equations are used to perform windless simulation based on the flight control commands of the preset trajectory to obtain the disturbance displacement components under the individual action of each flight control command. The actual flight deviation is obtained based on the actual flight trajectory of the drone and the preset trajectory of the drone. The abnormal points of sensor noise and electromagnetic interference are removed from the actual flight deviation by anti-outlier filtering. The Pearson correlation coefficient between the filtered deviation and the disturbance displacement components is calculated. If the correlation coefficient is greater than 0.7, the disturbance displacement components are deducted to obtain the actual wind disturbance deviation. If the correlation coefficient is less than 0.3, the filtered deviation is taken as the actual wind disturbance deviation. The embedding dimension is determined by the spurious nearest neighbor method based on the actual wind disturbance deviation, and the delay time is determined by the mutual information method. The phase space of the actual wind disturbance deviation is reconstructed based on the embedding dimension and the delay time, and the one-dimensional time series actual wind disturbance deviation is mapped to a high-dimensional disturbance displacement vector. The L2 norm of the high-dimensional disturbance displacement vector and the theoretical wind disturbance deviation is used as the disturbance displacement, and the ratio of the theoretical wind disturbance deviation to the disturbance displacement is used as the UAV maneuver weight.

5. The wind field data modeling method for wind resistance testing of small and medium-sized UAVs according to claim 1, characterized in that, The method for obtaining the preset wind field model includes: The disturbance displacement is extracted based on grid coordinates and flight data sampling time step to obtain a three-dimensional matrix. The duration of gusts is used as the short-term window length, and the long-term window length is determined according to the turbulence time scale. Based on the three-dimensional matrix, the short-term disturbance standard deviation, maximum modulus, minimum modulus and long-term disturbance mean are extracted by sliding between the short-term window length and the long-term window length. A four-dimensional feature matrix is ​​obtained based on the grid coordinates. The disturbance displacement is corrected coordinate-by-coordinate based on the three-dimensional matrix. If the UAV maneuver weight of the grid is greater than 0.7, the product of the disturbance displacement and the UAV maneuver inverse weight is used as the correction value. If the UAV maneuver weight is less than 0.3, it remains unchanged. If the network is located within the wake diffusion range, the wind speed attenuation law value corresponding to the grid coordinates is used as the weight coefficient of the disturbance displacement to obtain the corrected three-dimensional matrix. The corrected three-dimensional matrix is ​​then used to perform inversion through a preset aerodynamic model to obtain the inverted wind speed and inverted wind direction. The dynamic characteristics of the wind field are calculated based on the inverted wind speed, inverted wind direction, and four-dimensional feature matrix. The formula for calculating the dynamic characteristics of the wind field is as follows: Where γ(t) represents the wind field dynamics at time t, s is the short-term window length, Δt is the flight data sampling time step, h is the time index, and d(h) is the disturbance displacement. Let denoted as the mean displacement of the disturbance within the short-term window, tanh be the hyperbolic tangent function, f be the length of the long-term window, and d be the mean displacement of the disturbance within the short-term window. max d represents the maximum disturbance displacement within the short-term window. min This represents the minimum disturbance displacement within a short-term window. Based on the inverted wind speed, inverted wind direction, and dynamic characteristics of the wind field, Kriging interpolation is performed on the preset area and the unmeasured area to obtain the preset wind field model.

6. The wind field data modeling method for wind resistance testing of small and medium-sized UAVs according to claim 1, characterized in that, The method for obtaining the predicted wind field includes: Based on the boundary conditions of the unmeasured area, the wake diffusion boundary and wind speed attenuation law are extracted. According to the wake diffusion boundary and wind speed attenuation law, the grid data of the unmeasured area is corrected using a preset wind field model to obtain the prior mean of the prediction points. Using the prior mean of the prediction points in the unmeasured area and the grid data of the preset area, the prediction points in the unmeasured area are calculated through Gaussian process regression. The Gaussian process regression formula is as follows: in For the predicted points in the undetermined area, μ(x) u ,t u ) represents the prior mean of the predicted points, N represents the total number of spatial grids in the preset area, M represents the time step of the preset area, and x represents the value of x. i Let t be the spatial coordinates of the i-th grid in the preset area. k The timestamp of the k-th time step in the preset area. The inversion wind speed is the i-th grid and the k-th time step in the preset area. x is the average wind speed u The coordinates of the predicted points in the undetermined area, t u For the predicted timestamp, x u The predicted spatial coordinates; Align the set of predicted points in the unmeasured area with the grid and time step of the preset area to obtain the predicted wind field in the unmeasured area.

7. The wind field data modeling method for wind resistance testing of small and medium-sized UAVs according to claim 1, characterized in that, The method for obtaining the target wind field model includes: Based on different drone preset trajectories, discrete preset trajectory points are obtained according to grid coordinates and flight data sampling time step. The required wind speed and required wind direction are obtained from the preset trajectory points according to the wind resistance test requirements. Based on the grid coordinates of preset trajectory points, predicted wind speed and direction are obtained through predicted wind field. Using the predicted wind speed and direction of the preset trajectory points as initial target values, the target wind field loss function is iteratively optimized based on the required wind speed, required wind direction, and initial target values ​​to obtain the target wind field with minimized trajectory deviation. The expression for the target wind field loss function is: Where L is the loss function, representing the deviation between the predicted wind field and the multi-UAV trajectory, n is the number of discrete points on a single trajectory, and v p,i To determine the required wind speed at point i on the preset trajectory, v t,i To predict the actual wind speed at point i on the trajectory, θ p,i To determine the required wind direction at point i on the preset trajectory, θ t,i To predict the actual wind direction at point i on the trajectory, λ is the smoothing coefficient, and Δt is the time step of the flight data sampling.

8. A wind field data modeling system for wind resistance testing of small and medium-sized unmanned aerial vehicles (UAVs), used to execute the wind field data modeling method for wind resistance testing of small and medium-sized UAVs as described in any one of claims 1-7, characterized in that, include: Wind field preset area acquisition module: used to collect terrain data, wind speed data and wind turbine data of the test wind field, and determine the preset area and unmeasured area based on the test wind field and the preset trajectory of the UAV; Wind field boundary condition acquisition module: used to divide the preset area into grids based on the terrain data, align the wind speed data and wind turbine data with the grid coordinates, obtain the preset wind field boundary conditions, and obtain the boundary conditions of the unmeasured area and the wake of the disturbed grid through CFD fluid simulation based on the preset wind field boundary conditions; Wind field disturbance acquisition module: used to perform wind speed vector decomposition on the grid according to the preset wind field boundary conditions, and perform anti-outlier filtering and phase space reconstruction based on the wind speed vector and UAV flight deviations of different frequencies to obtain disturbance displacement and UAV maneuver weights; Preset wind field modeling module: used to perform time-series analysis on the disturbance displacement based on grid coordinates, and obtain a preset wind field model based on the disturbance time sequence, UAV maneuver weights and disturbance grid wake; Target wind field modeling module: This module is used to obtain the predicted wind field of the undetermined area based on the boundary conditions of the undetermined area through Gaussian process regression according to the preset wind field model. It uses different preset UAV trajectories to predict the wind field through grid temporal coupling to obtain the target wind field model.

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