Wing optimization method and device, equipment, storage medium and product

Abstract

The invention discloses a wing optimization method and device, equipment, a storage medium and a product, and relates to the technical field of aeronautical engineering.The wing optimization method comprises the steps that a wind tunnel device is started in response to a test instruction for a wing of an unmanned aerial vehicle, so that airflow of the wind tunnel device acts on the wing and drives a coating on the wing to flow; shooting to obtain a coating picture until the coating on the wing is stable; and performing feature extraction on the coating picture, and generating a wing optimization scheme based on the obtained optimization features. According to the method, the wing optimization scheme can be generated based on the obtained optimization characteristics only by observing the characteristics of the coating on the wing, and the problem that the equipment cost and the operation complexity of a traditional eddy current detection technology are remarkably poor can be avoided.

Description

Technical Field

[0001] The present application relates to the field of aviation engineering technology, and in particular to a wing optimization method, device, equipment, storage medium and product. Background Art

[0002] Diagnosing the aerodynamic characteristics of drone wings is crucial for ensuring efficient and stable flight. By analyzing the aerodynamic performance of the wing under different conditions, potential design flaws or performance bottlenecks can be identified, providing a basis for design optimization.

[0003] Among the related technologies, traditional eddy current detection technology relies on high-precision sensors or particle image velocimetry (PIV) methods. Although these methods can better capture and measure eddy current phenomena, they have significant disadvantages in equipment cost and operation complexity. Summary of the Invention

[0004] The main purpose of this application is to provide a wing optimization method, device, equipment, storage medium and product, aiming to solve the technical problem that traditional eddy current detection technology can capture and measure eddy current phenomena well, but has significant disadvantages in equipment cost and operation complexity.

[0005] To achieve the above objectives, the present application proposes a wing optimization method, which includes:

[0006] In response to a test instruction for a wing of the UAV, starting a wind tunnel device so that airflow of the wind tunnel device acts on the wing and drives the coating on the wing to flow;

[0007] until the coating on the wing is stable, and a coating picture is taken;

[0008] Feature extraction is performed on the coating image, and a wing optimization solution is generated based on the obtained optimization features.

[0009] In one embodiment, the coating is a colorable fluid liquid attached to the wing.

[0010] In one embodiment, the step of starting the wind tunnel device in response to the test instruction for the wings of the drone includes:

[0011] Obtain Reynolds values under real flight conditions;

[0012] determining an airflow velocity of the wind tunnel apparatus based on the Reynolds number;

[0013] The angle of attack of the UAV is adjusted to a target angle of attack.

[0014] In one embodiment, the coating image shows the flow trajectory of the fluid liquid.

[0015] In one embodiment, the step of photographing the coating until the coating on the wing is stable comprises:

[0016] Until the coating on the wing is stable, the target multi-light source module is started to illuminate the coating, and a picture of the coating that has been colored after illumination is taken.

[0017] In one embodiment, the step of extracting features from the coating image and generating a wing optimization solution based on the obtained optimization features includes:

[0018] aligning the coating images based on timestamps of the coating images;

[0019] Feature extraction is performed on the aligned coating images, and the extracted optimization features are input into a preset optimization solution generation model to generate a solution, thereby obtaining a wing optimization solution.

[0020] In addition, to achieve the above objectives, the present application also proposes a wing optimization device, which includes:

[0021] a response module, configured to, in response to a test instruction for a wing of a UAV, start a wind tunnel device so that airflow from the wind tunnel device acts on the wing and drives a coating on the wing to flow;

[0022] A shooting module, used for waiting until the coating on the wing is stable and taking a picture of the coating;

[0023] A generation module is used to extract features from the coating image and generate a wing optimization solution based on the obtained optimization features.

[0024] In addition, to achieve the above-mentioned purpose, the present application also proposes a wing optimization device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program is configured to implement the steps of the wing optimization method as described above.

[0025] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the wing optimization method described above are implemented.

[0026] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the wing optimization method described above.

[0027] One or more technical solutions proposed in this application have at least the following technical effects:

[0028] Compared with the related technologies, traditional eddy current detection technology relies on high-precision sensors or particle image velocimetry (PIV) methods. Although these methods can better capture and measure eddy current phenomena, they have significant disadvantages in equipment cost and operational complexity. In response to the test instructions for the wings of the drone, the present application starts the wind tunnel device so that the airflow of the wind tunnel device acts on the wing and drives the coating on the wing to flow; until the coating on the wing is stable, a coating picture is taken; features are extracted from the coating picture, and a wing optimization plan is generated based on the obtained optimization features. It can be understood that the present application uses the airflow of the wind tunnel device to act on the wing and drive the coating on the wing to flow, and after the coating is stable, the coating picture is taken, and features are extracted from the taken coating picture, and a wing optimization plan is generated based on the obtained optimization features. The present application only needs to observe the features of the coating on the wing to generate a wing optimization plan based on the obtained optimization features, which can avoid the problem that traditional eddy current detection technology has significant disadvantages in equipment cost and operational complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0030] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0031] Figure 1 A schematic diagram of a flow chart provided for the first embodiment of the wing optimization method of this application;

[0032] Figure 2 A schematic diagram of a wing for the wing optimization method of this application;

[0033] Figure 3 A schematic diagram of the process flow provided for the second embodiment of the wing optimization method of this application;

[0034] Figure 4 This is a schematic diagram of the module structure of the wing optimization device according to an embodiment of the present application;

[0035] Figure 5 Schematic diagram of the equipment structure of the hardware operating environment involved in the wing optimization method in the embodiment of the present application.

[0036] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0037] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0038] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0039] The main solution of the embodiment of the present application is: in response to a test instruction for the wing of a drone, starting a wind tunnel device so that the airflow of the wind tunnel device acts on the wing and drives the coating on the wing to flow; until the coating on the wing is stable, taking a coating picture; performing feature extraction on the coating picture, and generating a wing optimization solution based on the obtained optimization features.

[0040] This application can generate a wing optimization plan based on the obtained optimization characteristics simply by observing the characteristics of the coating on the wing, thereby avoiding the significant disadvantages of traditional eddy current detection technology in terms of equipment cost and operational complexity.

[0041] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication, and program execution capabilities, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device capable of performing the above functions. This embodiment and the following embodiments will be described below using a wing optimization device as an example.

[0042] Based on this, the embodiment of the present application provides a wing optimization method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the wing optimization method of the present application.

[0043] In this embodiment, the wing optimization method includes steps S100 to S300:

[0044] Step S100, in response to a test instruction for a wing of a UAV, starting a wind tunnel device so that airflow of the wind tunnel device acts on the wing and drives the coating on the wing to flow;

[0045] It should be noted that the wing optimization device in this embodiment is implemented by a wind tunnel device. This device includes an application interface. When the application interface receives a test command for the drone's wing, it activates the wind tunnel device. The airflow from the wind tunnel acts on the drone's wing, driving the special coating on the wing surface to flow and cause deformation.

[0046] Further, the test instruction includes the flight condition type, and the flight condition type includes a cruise mode, a climb mode, and a maneuver mode.

[0047] Further, the wing optimization device selects the wind tunnel control parameters according to the flight condition type included in the test instruction. Specifically, the wind tunnel control parameters include:

[0048] Initial wind speed range: 10 - 30 m / s;

[0049] Turbulence intensity (T1) level: Level I to Level IV. Turbulence intensity is a key parameter in fluid mechanics used to quantify the intensity of turbulent fluctuations in an air flow;

[0050] Level I: Low turbulence intensity, TI < 0.5%, suitable for basic aerodynamic research (such as observing laminar separation bubbles), with high data stability, but may underestimate the effects of the real flight environment;

[0051] Level II: Medium turbulence intensity, 0.5% < TI < 5%, simulating the actual atmospheric environment (civil airliner cruise), balancing experimental controllability and authenticity;

[0052] Level III: High turbulence intensity, 10% > TI > 5%, used for extreme condition tests (storm environment, fighter jet maneuver), triggering complex flow phenomena;

[0053] Level IV: Extremely high turbulence intensity, TI > 10%, simulating near - ground strong turbulence (such as urban building complex wake)

[0054] Temperature control curve: Changes at a rate of ΔT = 5 - 15 °C / min during the test period.

[0055] The wing optimization device can select different wind tunnel control parameters to simulate different flight conditions of the UAV. By covering the typical flight conditions of the UAV (cruise, climb, maneuver), the test results can directly guide the optimization of aerodynamic design. Specifically, the wind tunnel parameters corresponding to different flight conditions are:

[0056] Cruise mode: Medium - low speed (15 - 25 m / s), low turbulence (Level II), simulating a stable flight state.

[0057] Climb mode: High speed (20 - 30 m / s), high turbulence (Level III), simulating the airflow disturbance during accelerated climb.

[0058] Maneuver mode: Random pulsating wind speed (±5 m / s, 1 - 5 Hz), simulating dynamic conditions such as sharp turns and rolls of the UAV. The random pulsating disturbance can trigger the transient characteristics of flow separation on the wing surface and capture the eddy details missed by traditional steady - state tests.

[0059] Furthermore, the wing optimization equipment maintains laminar flow (turbulence <0.5%) during the startup phase to avoid initial impact damage to the coating, protect the integrity of the coating, and ensure that the flow trajectory is clearly discernible. During the data acquisition phase, different wind tunnel control parameters are selected according to different flight conditions to actively stimulate the target vortex structure. In the terminal phase, the reverse pressure gradient is used to control the airflow attenuation to prevent sudden shutdown from causing coating backflow or splashing, which would contaminate the data.

[0060] Step S200, until the coating on the wing is stable, photographing the coating;

[0061] It is understandable that the wing optimization equipment is also equipped with a micro-periscope high-speed camera array (4mm diameter, frame rate 1200fps), embedded in the leading edge of the wing. The mirror is coated with diamond film to reduce aerodynamic interference. This micro-camera array can operate stably in high-speed airflow environments and capture vortex images on the wing surface. For example, under high subsonic flight conditions, the camera array can accurately capture the formation and development process of vortices, providing high-quality data for subsequent image analysis. The diamond coating on the mirror can reduce the interference of airflow on the camera lens, improving the clarity and stability of the image.

[0062] It should be noted that when the coating flow on the wing tends to be stable, that is, the coating flow deformation rate is lower than 0.1mm / s for 30 consecutive seconds, the wing optimization equipment will control the micro periscope high-speed camera array to take pictures of the wing coating.

[0063] Furthermore, if the coating flow deformation rate is detected to be too high during the coating deformation process, the wind tunnel control parameters will be adjusted to prevent excessive deformation and inaccurate data.

[0064] It is understandable that, since the pressure on the lower surface of the wing is always greater than that on the upper surface, the high-pressure airflow on the lower surface of a wing with a limited wingspan will bypass the wingtip and flow to the low-pressure area of the upper surface, forming an air vortex around the wingtip. This phenomenon not only increases the induced drag, but may also interfere with the aircraft behind. In order to reduce the impact of the wingtip vortex, step S200 is performed to simulate the actual flight conditions of the UAV through a wind tunnel device, and eddy current testing is performed by observing the coating flow on the wing. This can avoid the traditional eddy current detection technology from relying on high-precision sensors or particle image velocimetry (PIV) methods. Although these methods can better capture and measure eddy current phenomena, they have significant disadvantages in terms of equipment cost and operational complexity, thereby reducing the cost and operational complexity of eddy current detection.

[0065] Step S300: extracting features from the coating image and generating a wing optimization solution based on the obtained optimization features.

[0066] It should be noted that after the wing optimization equipment obtains high-quality coating images, the next step is to conduct in-depth analysis of these images and extract key features from the images, such as vortex location, separation point, airflow direction and any other information about airflow behavior. Based on the extracted features, combined with previously accumulated knowledge and technical standards, the ultimate goal is to generate a detailed wing optimization plan to assist in wing design, such as leading edge shape adjustment and wingtip angle design, thereby reducing drag and improving lift efficiency.

[0067] Specific, key features:

[0068] Separation Point: The location where the coating flow line suddenly breaks or turns, marking the separation of the airflow from the wing surface.

[0069] Eddy current area: The coating presents a spiral or closed loop trajectory, reflecting the location and intensity of the vortex core.

[0070] Airflow direction: The linear trajectory along which the coating is stretched points in the direction of the local airflow.

[0071] Furthermore, the wing optimization equipment identifies aerodynamic performance bottlenecks (such as premature separation and strong wingtip vortices) based on key features, and adjusts geometric parameters such as leading edge radius, sweep angle and wingtip shape according to the aerodynamic performance bottlenecks.

[0072] Furthermore, after acquiring the coating flow image, the wing optimization device needs to pre-process it through a computing device to optimize the image quality and extract key aerodynamic features. The pre-processing steps are as follows:

[0073] 1. Noise suppression

[0074] Algorithm selection:

[0075] Non-local mean denoising (NL-Means): Based on global similarity block matching in the image, it preserves flow edge details and is particularly suitable for random noise caused by wind tunnel vibration.

[0076] Wavelet threshold denoising: For high-frequency noise (such as camera sensor noise), the noise is separated from the signal through wavelet decomposition.

[0077] 2. Contrast enhancement

[0078] CLAHE (Contrast Limited Adaptive Histogram Equalization):

[0079] Partition processing: Divide the image into 8×8 sub-regions and perform local histogram equalization.

[0080] Contrast Limiting: The clipping threshold is set to 3.0 to prevent artifacts caused by over-enhancement.

[0081] Gamma Correction: Adjust the gamma value to expand dark details and enhance the visibility of eddy areas.

[0082] 3. Sharpening and edge enhancement

[0083] Unsharp Mask:

[0084] The Gaussian blur kernel generates a low-frequency component, which is then subtracted from the original image and then weighted superimposed.

[0085] The intensity coefficient is 1.2 and the threshold is 0.01 to avoid noise amplification.

[0086] Canny edge detection optimization:

[0087] Double thresholds (low threshold = 50, high threshold = 150) are combined with morphological closing operations to connect broken edges.

[0088] Furthermore, the key feature extraction techniques are as follows:

[0089] 1. Eddy current position positioning

[0090] Vorticity field calculation:

[0091] The velocity field is obtained based on the optical flow method (Farneback algorithm), and the vorticity extreme value area is calculated.

[0092] Morphological processing:

[0093] The vorticity binary map is opened (3×3 kernel) to remove isolated noise points, and the vortex core center is marked through connected domain analysis.

[0094] 2. Separation point and reattachment point detection

[0095] Curvature analysis method:

[0096] The curvature is calculated along the flow trajectory, and the separation point corresponds to the maximum curvature.

[0097] Grayscale gradient mutation detection:

[0098] The local gradient variance is calculated using a sliding window (15×15 pixels), and regions with sudden increases in variance are marked as separation / reattachment points.

[0099] 3. Flow direction visualization

[0100] Optical flow vector field rendering:

[0101] The dense optical flow field is color-coded (HSV space: hue represents direction, saturation represents velocity).

[0102] Vector simplification: Sampling every 20 pixels, arrow length normalized to 5-15 pixels.

[0103] In a feasible embodiment, the coating is a colorable fluid liquid attached to the wing.

[0104] It is understood that the coating is a mixed type of paint, which is applied to the leading edge of the drone's wing. Figure 2 , Figure 2 A schematic diagram of the wing is provided, in which AB and BC are the leading edges of the wing.

[0105] Specifically, the coating is applied to the wing surface via an electrostatic spraying process with a spraying voltage of 50kV and curing conditions of 10 minutes of ultraviolet light (wavelength 385nm). The electrostatic spraying process forms a uniform coating on the wing surface. Optimizing the spraying voltage and curing conditions ensures the quality and performance of the coating. For example, at a spraying voltage of 50kV, the coating solution is atomized by the electric field and evenly sprayed onto the wing surface, forming a thin and uniform coating. Under UV light irradiation, the coating undergoes a curing reaction, forming a stable coating structure with good adhesion and wear resistance.

[0106] The coating material composition is as follows:

[0107] Nano-scale thermochromic particles (particle size 50-100nm, accounting for 5%-8% of the total coating mass): tungsten oxide Coated with a silica core, the response temperature range is -20°C to 120°C, and the color gradually changes from blue (low temperature) to red (high temperature). This material can respond quickly when the surface temperature of the wing changes, and intuitively reflect the temperature difference caused by the vortex through color changes. For example, under the impact of high-speed airflow, the temperature of the vortex area may change. Thermochromic particles can capture this change and convert it into a visual color signal. Moreover, its small particle size can ensure the uniformity and fineness of the coating, and will not have a significant impact on the aerodynamic performance of the wing.

[0108] Shear thickening substrate (60%): A composite of polyethylene glycol (PEG2000) and hydrophobic silica (particle size 10 nm), the viscosity increases 3 times when the shear rate is >1000s-1, enhancing the contrast of the flow trajectory. When the airflow generates shear force on the wing surface, the viscosity of the shear thickening substrate increases, making the morphological changes of the coating in the vortex area more obvious, thereby enhancing the contrast of the flow trajectory and facilitating subsequent image acquisition and analysis. For example, in the wingtip vortex area, strong shear force will make the viscosity change of the coating more significant, highlighting the outline of the vortex, which is conducive to accurately identifying the location and intensity of the vortex.

[0109] Ultraviolet phosphor (2%): Europium-doped yttrium oxide (Y2O3:Eu3+) emits red light under 365nm ultraviolet light and is used for darkfield imaging. In darkfield environments, the UV phosphor emits a bright red light, creating a sharp contrast with the surrounding environment, further improving image clarity and resolution. Even in low light or complex ambient lighting conditions, it can accurately capture eddy current information on the wing surface, ensuring the accuracy and reliability of the image data.

[0110] Self-healing polymer (30%): A polyurethane-urea (PUU) network structure with dynamic disulfide bonds can repair microcracks within 30 minutes. Wings may be subjected to various external forces during flight, causing microcracks in the coating and affecting the accuracy of eddy current testing. Self-healing polymers can automatically repair these microcracks in a short period of time, ensuring the integrity and stability of the coating, extending its service life, reducing maintenance costs, and improving its reliability and stability.

[0111] In a possible embodiment, the coating image shows the flow track of the fluid liquid.

[0112] It's understandable that in wind tunnel experiments, airflow strikes the surface of a drone's wing at a specific speed and direction. According to fluid mechanics theory, when airflow encounters a solid surface, a boundary layer forms around it. Within the boundary layer, the airflow velocity is lower near the solid surface, but gradually increases with distance from the surface until it reaches the mainstream velocity. This velocity gradient generates shear stress, exerting force on the coating and causing it to flow.

[0113] In addition, under certain conditions, if the air flow speed is high enough, the flow state within the boundary layer may change from laminar flow to turbulent flow. In the turbulent state, the air flow becomes more irregular and full of vortices. These vortices can significantly enhance the force on the coating surface, further intensify the flow of the coating, and change its flow trajectory.

[0114] In a feasible implementation manner, before step S100, the following steps are included:

[0115] Obtain Reynolds values under real flight conditions;

[0116] It should be noted that before starting the wind tunnel device, the wing optimization equipment can also set the wind control parameters. It is necessary to obtain the actual Reynolds number experienced by the UAV under actual flight conditions. The Reynolds number (Re) is a dimensionless parameter used to describe the tendency of the fluid flow state to change from laminar flow to turbulent flow. Its calculation formula is:

[0117]

[0118] where ρ is the fluid density, v is the gas velocity, L is the characteristic length (such as the chord length of an airfoil), and μ is the dynamic viscosity.

[0119] For drones, since they usually operate in a lower speed range, they may face the problem of low Reynolds number effect. By accurately measuring or estimating the flight parameters of the drone in a typical mission profile, the corresponding true Reynolds number value can be obtained. This step ensures that the wind tunnel test environment is as close to the actual flight conditions as possible.

[0120] determining an airflow velocity of the wind tunnel apparatus based on the Reynolds number;

[0121] It is understandable that the wing optimization equipment is based on the obtained Reynolds number. The next step is to determine the airflow speed in the wind tunnel device. In order to ensure that the experimental results are representative, the airflow conditions in the wind tunnel should be as close as possible to the actual flight environment. This means that the airflow speed and other related parameters in the wind tunnel need to be adjusted so that the Reynolds number generated in the wind tunnel is consistent with that during external flight.

[0122] The angle of attack of the UAV is adjusted to a target angle of attack.

[0123] It should be noted that the target angle of attack is defined as the angle between the incoming airflow and the wing reference line. It is one of the key factors affecting important aerodynamic characteristics such as the lift coefficient and the drag coefficient. The wing optimization equipment adjusts the position of the UAV model to achieve a predetermined target angle of attack. Proper angle of attack settings not only help researchers better understand aerodynamic performance under specific conditions but also reveal potential stalling or other nonlinear behaviors. For example, in certain situations, increasing the wingtip-to-root ratio or sweep angle may increase the effective angle of attack of the outer wing, which can easily lead to airflow separation. Proper selection and precise control of the angle of attack are crucial for obtaining reliable test results. Before flight testing, the wing optimization equipment must adjust the angle of attack to the target angle of attack. This is because the angle of attack directly affects lift. Within a certain range, increasing the angle of attack increases the pressure differential between the upper and lower surfaces of the wing, thereby increasing lift. However, excessive angles of attack can significantly increase pressure differential drag and induced drag. Adjusting the angle of attack can balance the lift-to-drag ratio and optimize flight efficiency.

[0124] Furthermore, the angle of attack of the drone model can be selected at different flight conditions to simulate the following changes in flight conditions:

[0125] 1. Flight speed changes, reducing the angle of attack is equivalent to increasing the flight speed (maintaining lift).

[0126] 2. Changes in flight altitude, increasing the angle of attack ≈ simulate a low-density, high-altitude environment (requiring greater lift).

[0127] 3. Load changes, increasing the angle of attack ≈ simulating heavy-load takeoff or maneuvering overload.

[0128] 4. Stall boundary test, gradually increase the angle of attack until it reaches the critical value.

[0129] In wind tunnel experiments, wing optimization equipment can reproduce complex flight scenarios at low cost by adjusting the wing's angle of attack rather than actually changing flight parameters (such as speed / altitude).

[0130] In this embodiment, after applying a coating of mixed materials on the wing, a test flight is performed, and the flow of the coating on the wing is photographed by a camera mounted on the fuselage to calculate the actual vortex measurement value of the wing.

[0131] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction and will not be repeated hereafter. Based on this step S200, the wing optimization method further includes step A01:

[0132] Step A01: until the coating on the wing is stable, start the target multi-light source module to illuminate the coating, and take a picture of the coating that has been colored after illumination.

[0133] It is understood that the wing optimization equipment is also equipped with a light source control device. After the coating on the wing is stabilized, the wing optimization equipment turns on the light source control device, selects the target light source type, such as 365nm ultraviolet LED and 850nm infrared laser, and adjusts the light source intensity and angle to ensure that the light source can evenly illuminate the coating on the wing surface. After the wing coating develops color, the micro-periscope high-speed camera array is controlled to capture an image of the wing coating.

[0134] Furthermore, a light intensity meter can be used to measure the intensity of the light source and adjust it as needed.

[0135] In a feasible implementation, step 300 includes the following steps:

[0136] aligning the coating images based on timestamps of the coating images;

[0137] It should be noted that when the wing optimization equipment takes pictures of the coating, each picture will be marked with a corresponding timestamp. These timestamps record the specific time when the picture was taken. These timestamp information needs to be obtained for subsequent alignment operations and to compensate for the camera perspective differences to ensure the synchronization of the images in time and space, and to avoid image distortion and data errors caused by perspective differences and time delays. Figure 3 , Figure 3 A schematic diagram of the coating is provided.

[0138] The aligned coating images are subjected to feature extraction, and the extracted optimization features are input into a preset optimization solution generation model for solution generation to obtain a wing optimization solution.

[0139] It is understandable that the optimization features specifically include separation point: the location where the airflow separates; vortex area: the trajectory of paint movement caused by the vortex; airflow direction: displayed by the direction in which the paint is stretched.

[0140] It should be noted that the preset optimization scheme generation model of the wing optimization equipment will generate a wing optimization scheme based on the obtained optimization features. Specifically, the preset optimization scheme generation model will not only refer to the optimization features, but also refer to the color features in the coating image and the flight parameters (airspeed, angle of attack), comprehensively consider the influence of the vortex, optical properties and flight state on the vortex, and obtain the vortex-related data of the vortex core coordinates (accuracy ±1mm) and vortex strength (error <5%), and generate the wing optimization scheme based on the vortex-related data.

[0141] Furthermore, the wing optimization equipment can also be equipped with a MEMS piezoresistive sensor (size 2mm×2mm), which is conformally bonded under the coating. It can closely adhere to and monitor the air pressure changes on the wing surface in real time. It is used to monitor the local air pressure in real time (sampling rate 10kHz), and is jointly calibrated with image features to improve the accuracy of eddy current measurement. The sensor data and the eddy current parameters extracted from the image are input into the filter to correct the measurement deviation caused by the delayed coating response (delay <5ms), thereby improving the real-time and accuracy of the system.

[0142] In this implementation, vortices are measured in real time through a deep learning algorithm, and key parameters such as vortex core coordinates and vortex strength are output, helping pilots understand the aerodynamic state of the wing surface in real time, optimize flight operations, and improve flight safety and performance.

[0143] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the wing optimization method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0144] This application also provides a wing optimization device, please refer to Figure 4 , the wing optimization device comprises:

[0145] A response module 10 is configured to start a wind tunnel device in response to a test instruction for a wing of a UAV, so that the airflow of the wind tunnel device acts on the wing and drives the coating on the wing to flow;

[0146] A photographing module 20 is used to photograph the coating until the coating on the wing is stable;

[0147] The generation module 30 is used to extract features from the coating image and generate a wing optimization solution based on the obtained optimization features.

[0148] Optionally, the coating is a colorable fluid liquid attached to the wing.

[0149] Optionally, the response module includes:

[0150] The adjustment submodule is used to obtain the Reynolds value under actual flight conditions; determine the airflow speed of the wind tunnel device based on the Reynolds value; and adjust the angle of attack of the UAV to a target angle of attack.

[0151] Optionally, the coating image shows a flow track of the fluid liquid.

[0152] Optionally, the shooting module includes:

[0153] The starter module is used to start the target multi-light source module to irradiate the coating until the coating on the wing is stable, and to take pictures of the coating that has been colored after irradiation.

[0154] Optionally, the generating module includes:

[0155] The alignment submodule is used to align the coating images based on the timestamps of the coating images; extract features from the aligned coating images, input the extracted optimization features into a preset optimization solution generation model to generate a solution, and obtain a wing optimization solution.

[0156] The wing optimization device provided in this application utilizes the wing optimization method described in the aforementioned embodiments to address the technical challenges of wing optimization. Compared to the prior art, the wing optimization device provided in this application achieves the same beneficial effects as the wing optimization method described in the aforementioned embodiments. Other technical features of the wing optimization device are the same as those disclosed in the aforementioned embodiments and are not further detailed here.

[0157] The present application provides a wing optimization device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the wing optimization method in the above-mentioned embodiment one.

[0158] Reference below Figure 5, which shows a schematic diagram of the structure of a wing optimization device suitable for implementing embodiments of the present application. The wing optimization device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, tablet computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 5 The wing optimization device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0159] like Figure 5 As shown, the wing optimization device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the wing optimization device. Processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems may be connected to I / O interface 1006: input device 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output device 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 1003 including, for example, a magnetic tape, hard disk, etc.; and communication device 1009. Communication device 1009 can allow the wing optimization device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a wing optimization device with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented or have alternatively.

[0160] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0161] The wing optimization device provided in this application utilizes the wing optimization method described in the aforementioned embodiment to address the technical challenges of wing optimization. Compared to the prior art, the wing optimization device provided in this application achieves the same beneficial effects as the wing optimization method described in the aforementioned embodiment. Other technical features of the wing optimization device are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0162] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0163] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

[0164] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, wherein the computer-readable program instructions are used to execute the wing optimization method in the above-mentioned embodiment.

[0165] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination thereof. More specific examples of computer-readable storage media may 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 or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0166] The above-mentioned computer-readable storage medium may be included in the wing optimization device; or it may exist independently without being assembled into the wing optimization device.

[0167] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the wing optimization device, the wing optimization device: responds to a test instruction for the wing of the drone, starts a wind tunnel device so that the airflow of the wind tunnel device acts on the wing and drives the coating on the wing to flow; until the coating on the wing is stable, a coating image is taken; and features are extracted from the coating image, and a wing optimization plan is generated based on the obtained optimization features.

[0168] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0169] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0170] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0171] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned wing optimization method, thereby solving the technical problem of wing optimization. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the wing optimization method provided in the aforementioned embodiment, and are not further elaborated here.

[0172] The present application also provides a computer program product, comprising a computer program, which implements the steps of the above-mentioned wing optimization method when executed by a processor.

[0173] The computer program product provided in this application can solve the technical problem of wing optimization. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the wing optimization method provided in the above embodiment, and will not be repeated here.

[0174] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A wing optimization method, characterized in that: The wing optimization method comprises: In response to a test instruction for a wing of a UAV, starting a wind tunnel device so that airflow of the wind tunnel device acts on the wing and drives the coating on the wing to flow; until the coating on the wing is stable, and a coating picture is taken; Feature extraction is performed on the coating image, and a wing optimization solution is generated based on the obtained optimization features.

2. The wing optimization method according to claim 1, characterized in that: The coating is a color-developing fluid liquid attached to the wing.

3. The wing optimization method according to claim 1, wherein: The step of starting the wind tunnel device before the test instruction for the wings of the UAV includes: Obtain Reynolds values under real flight conditions; determining an airflow velocity of the wind tunnel apparatus based on the Reynolds number; The angle of attack of the UAV is adjusted to a target angle of attack.

4. The wing optimization method according to claim 2, wherein: The coating image shows the flow path of the flowing liquid.

5. The wing optimization method according to claim 1, wherein: The step of photographing the coating until the coating on the wing is stable comprises: Until the coating on the wing is stable, the target multi-light source module is started to illuminate the coating, and a picture of the coating that has been colored after illumination is taken.

6. The wing optimization method according to claim 1, wherein: The step of extracting features from the coating image and generating a wing optimization solution based on the obtained optimization features includes: aligning the coating images based on timestamps of the coating images; Feature extraction is performed on the aligned coating images, and the extracted optimization features are input into a preset optimization solution generation model to generate a solution, thereby obtaining a wing optimization solution.

7. A wing optimization device, characterized in that: The device comprises: a response module, configured to, in response to a test instruction for a wing of a UAV, start a wind tunnel device so that airflow from the wind tunnel device acts on the wing and drives a coating on the wing to flow; A shooting module, used for waiting until the coating on the wing is stable and taking a picture of the coating; A generation module is used to extract features from the coating image and generate a wing optimization solution based on the obtained optimization features.

8. A wing optimization device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the wing optimization method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the wing optimization method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, which implements the steps of the wing optimization method according to any one of claims 1 to 6 when executed by a processor.

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