Deformation inversion method, device and equipment of unmanned aerial vehicle wing and storage medium

Abstract

The invention discloses an unmanned aerial vehicle wing deformation inversion method, device and equipment and a storage medium, and relates to the technical field of unmanned aerial vehicles, and the method comprises the steps: carrying out the static loading test of an unmanned aerial vehicle wing through a static test system, and collecting the target strain data of the unmanned aerial vehicle wing; and performing deformation inversion analysis on the unmanned aerial vehicle wing based on the target strain data through a target deformation inversion algorithm to obtain deformation inversion data of the unmanned aerial vehicle wing. A static force loading test is carried out through the static force test system, deformation inversion is carried out in combination with a target deformation inversion algorithm, and the deformation measurement accuracy of the unmanned aerial vehicle wing can be improved.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a method, apparatus, equipment, and storage medium for inverting the deformation of UAV wings. Background Technology

[0002] Real-time monitoring of wing deformation is crucial for ensuring flight safety during drone flight. Wings are subjected to various forces during flight, including aerodynamic loads, gravity, and inertial forces, leading to deformations such as bending and twisting. These deformations directly affect the drone's aerodynamic performance, stability, and safety. If the deformation exceeds design limits, it may cause structural damage or even a flight accident. Therefore, high-precision, real-time monitoring of wing deformation is one of the key requirements for the development of drone technology.

[0003] Currently, wing deformation monitoring mainly relies on traditional methods, such as ground static and dynamic load experiments, and sensors like resistance strain gauges. However, these methods have significant limitations: First, traditional monitoring methods lack precision and struggle to capture minute wing deformations; second, data acquisition and processing have poor real-time performance, failing to adapt to rapid changes under complex flight conditions; furthermore, traditional equipment is susceptible to environmental factors such as electromagnetic interference, resulting in insufficient monitoring stability under harsh conditions. Simultaneously, existing deformation inversion algorithms are mostly based on simplified models, making it difficult to accurately reflect the actual deformation behavior of complex structures, leading to discrepancies between monitoring results and actual operating conditions. These shortcomings severely restrict the reliability and practicality of UAV wing deformation monitoring. Therefore, there is an urgent need to propose a scheme capable of measuring wing deformation of aircraft during flight with high precision. Summary of the Invention

[0004] The main objective of this application is to provide a method, apparatus, and equipment for inverting the deformation of UAV wings, aiming to solve the technical problem of low accuracy in UAV wing deformation measurement.

[0005] To achieve the above objectives, this application proposes a deformation inversion method for an unmanned aerial vehicle (UAV) wing, the method comprising: A static loading test was conducted on the UAV wing using a static testing system, and the target strain data of the UAV wing was collected. Based on the target strain data, the deformation inversion algorithm is used to perform deformation inversion on the UAV wing to obtain the deformation inversion data of the UAV wing.

[0006] In one embodiment, the static testing system includes a static testing bench, a fiber optic strain measurement system, and a fiber optic temperature sensor. The step of conducting a static loading test on the UAV wing using the static testing system and collecting target strain data of the UAV wing includes: The drone wing was fixed using a static test bench, and a loading test was performed on the fixed drone wing. Data is collected from the UAV wing using the fiber optic strain measurement system and the fiber optic temperature sensor to obtain target strain data of the UAV wing during the loading test.

[0007] In one embodiment, the fiber optic strain measurement system includes a fiber optic strain sensor and a fiber optic demodulator. The step of acquiring target strain data of the UAV wing during the loading test by using the fiber optic strain measurement system and the fiber optic temperature sensor includes: The initial strain data of the UAV wing during the loading test is obtained by the fiber Bragg grating strain sensor, and the initial strain data is transmitted to the fiber Bragg grating demodulator in the form of an optical signal. Temperature compensation is performed on the fiber optic strain sensor using the fiber optic temperature sensor to generate the temperature measurement value of the UAV wing and transmit it to the fiber optic demodulator. The initial strain data in optical signal form is converted into an electrical signal by the fiber optic demodulator, and the target strain data is obtained based on the initial strain data in electrical signal form and the temperature measurement value.

[0008] In one embodiment, the loading test includes simulating uniformly distributed loads and concentrated loads, and the step of performing the loading test on the fixed UAV wing includes: A uniformly distributed load is simulated on the UAV wing by uniformly loading and / or unloading the lower surface of the UAV wing. A concentrated load is applied to the drone wing by uniformly loading and / or unloading at the end position of the drone wing.

[0009] In one embodiment, the deformation inversion data includes first inversion data and second inversion data. The step of performing deformation inversion on the UAV wing based on the target strain data using a target deformation inversion algorithm to obtain the deformation inversion data of the UAV wing includes: Acquire the first strain data of the UAV wing under concentrated load, and perform deformation inversion on the first strain data using a target deformation inversion algorithm to generate the first inversion data of the UAV wing under concentrated load; The second strain data of the UAV wing under simulated uniform load is obtained, and the first strain data is subjected to deformation inversion using a target deformation inversion algorithm to generate the second inversion data of the UAV wing under simulated uniform load.

[0010] In one embodiment, after the step of conducting a static loading test on the UAV wing using a static testing system and collecting target strain data of the UAV wing, the method further includes: The third strain data of the UAV wing in the static loading test is obtained by a resistance strain gauge, and the target strain data and the third strain data are compared. If the error between the target strain data and the third strain data is less than a preset error threshold, then deformation inversion analysis is performed based on the target strain data.

[0011] In one embodiment, the deformation inversion method for the UAV wing further includes: In the static loading test, the true value of the deformation measurement of the UAV wing is obtained through a displacement measurement system; The deformation inversion data and the true value of the deformation measurement are compared, and the deformation inversion result of the static test system for the UAV wing is determined based on the comparison result.

[0012] Furthermore, to achieve the above objectives, this application also proposes a deformation inversion device for an unmanned aerial vehicle (UAV) wing, the deformation inversion device comprising: The strain data acquisition module is used to conduct a static loading test on the UAV wing using a static test system and acquire the target strain data of the UAV wing. The deformation data calculation module is used to perform deformation inversion analysis on the UAV wing based on the target strain data using a target deformation inversion algorithm, and obtain the deformation inversion data of the UAV wing.

[0013] In addition, to achieve the above objectives, this application also proposes a deformation inversion device for a UAV wing, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the deformation inversion method for a UAV wing as described above.

[0014] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the deformation inversion method for UAV wings as described above.

[0015] One or more technical solutions proposed in this application have at least the following technical effects: This application proposes a method, apparatus, device, and storage medium for deformation inversion of a UAV wing. A static loading test is conducted on the UAV wing using a static testing system to collect target strain data. Based on this target strain data, a target deformation inversion algorithm is used to perform deformation inversion analysis on the UAV wing, obtaining the deformation inversion data of the UAV wing. Combining static loading tests with a target deformation inversion algorithm improves the accuracy of UAV wing deformation measurement. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating an embodiment of the deformation inversion method for the UAV wing of this application; Figure 2 A schematic diagram of the layout of the resistance strain gauge provided in the embodiment of the deformation inversion method for the UAV wing of this application; Figure 3 A simplified flowchart illustrating the deformation inversion method for an unmanned aerial vehicle (UAV) wing provided in this application embodiment; Figure 4 This is a schematic diagram of the module structure of the deformation inversion device for the UAV wing according to an embodiment of this application; Figure 5 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the deformation inversion method of the UAV wing in this embodiment of the application.

[0019] 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 Implementation

[0020] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0021] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0022] The main solution of this application embodiment is: to conduct a static loading test on the UAV wing using a static test system and collect the target strain data of the UAV wing; and to perform deformation inversion analysis on the UAV wing based on the target strain data using a target deformation inversion algorithm to obtain the deformation inversion data of the UAV wing.

[0023] In this embodiment, for ease of description, the deformation inversion device of the UAV wing will be used as the main execution subject for the following description.

[0024] Real-time monitoring of wing deformation is crucial for ensuring flight safety during drone flight. Wings are subjected to various forces during flight, including aerodynamic loads, gravity, and inertial forces, leading to deformations such as bending and twisting. These deformations directly affect the drone's aerodynamic performance, stability, and safety. If the deformation exceeds design limits, it may cause structural damage or even a flight accident. Therefore, high-precision, real-time monitoring of wing deformation is one of the key requirements for the development of drone technology.

[0025] Currently, wing deformation monitoring mainly relies on traditional methods, such as ground static and dynamic load experiments, and sensors like resistance strain gauges. However, these methods have significant limitations: First, traditional monitoring methods lack precision and struggle to capture minute wing deformations; second, data acquisition and processing have poor real-time performance, failing to adapt to rapid changes under complex flight conditions; furthermore, traditional equipment is susceptible to environmental factors such as electromagnetic interference, resulting in insufficient monitoring stability under harsh conditions. Simultaneously, existing deformation inversion algorithms are mostly based on simplified models, making it difficult to accurately reflect the actual deformation behavior of complex structures, leading to discrepancies between monitoring results and actual operating conditions. These shortcomings severely restrict the reliability and practicality of UAV wing deformation monitoring. Therefore, there is an urgent need to propose a scheme capable of measuring wing deformation of aircraft during flight with high precision.

[0026] This application provides a solution that combines static loading tests using a static testing system with deformation inversion algorithms to improve the accuracy of deformation measurement of UAV wings.

[0027] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as a deformation inversion device for a UAV wing. The following description uses a deformation inversion device for a UAV wing as an example to illustrate this embodiment and the subsequent embodiments.

[0028] Based on this, embodiments of this application provide a deformation inversion method for an unmanned aerial vehicle (UAV) wing, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the deformation inversion method for the wing of an unmanned aerial vehicle (UAV) according to this application.

[0029] In this embodiment, the deformation inversion method for the UAV wing includes steps S11-S12: Step S11: A static loading test is performed on the UAV wing using a static testing system to collect target strain data of the UAV wing.

[0030] It should be noted that the static test system refers to a system used to apply static loads to the wings of UAVs and measure their strain data. It is also capable of deformation inversion, inverting the deformation data of the UAV wings from the strain data. The system includes a static test bench, a fiber optic strain measurement system, and a fiber optic temperature sensor.

[0031] Additionally, it should be noted that the target strain data refers to the strain data of the UAV wing collected by sensors during the static loading test, which is used for subsequent deformation inversion.

[0032] Additionally, it should be noted that a drone wing refers to the wing structure installed on a drone, whose main function is to provide lift for the drone and maintain its flight.

[0033] In one embodiment of this application, the UAV used is a shipborne vertical take-off and landing fixed-wing compound aircraft. Its specific parameters include a fuselage length of 2.1m, a wingspan of 4m, a distance of more than 0.15m between the main spar and secondary spar of the wing, a wing length of 1.7m on each side, and a chord length of 0.3m.

[0034] The fuselage, skin, and wing spars are all made of carbon fiber composite materials. The wing spars are made of Toray JG4524Y mixed carbon fiber, which has high tensile strength (greater than 3900MPa), density (1.78g / cm3), and a thickness of 6mm.

[0035] The skin material consists of carbon fiber fabric, PVC foam interlayer, and carbon fiber prepreg, exhibiting excellent tensile strength (3530MPa) and tensile modulus (230GPa). The overall structure uses epoxy resin as the matrix, and the process mainly involves two steps: first, molding, where the prepreg, 0.1mm carbon fiber fabric, 0.2mm PVC foam interlayer, and 0.1mm carbon fiber fabric are laid in a skin mold according to product requirements. Vacuum bagging technology is used to vacuum-pressurize the carbon fiber fabric, PVC foam, and epoxy resin interlayer within the mold, completing the skin molding process; second, curing, where the carbon fiber fabric, PVC foam, and epoxy resin already laid in the mold are cured at 60℃ under vacuum and pressure for 72 hours, followed by sanding and painting to complete the composite material skin molding process.

[0036] The wing ribs are made of aerospace-grade plywood, characterized by high strength, good water resistance, small bending angle, and excellent seismic resistance. The material is birch wood, processed through glue impregnation, soaking, drying, and hot pressing. This aerospace-grade plywood has a density of 0.70 g / cm³ and a tensile strength of 80 MPa.

[0037] After preparing basic components such as the skin, ribs, and spars, and deploying the fiber optic strain sensors proposed in this application on the spars, the wing molding process will proceed. First, structural adhesive is used to fix the spars and ribs, completing the basic internal frame structure of the wing. Next, the skin is pressurized and molded in the skin mold, with high-strength structural adhesive used to bond all contact points between components. After pressurized molding, the wing is cured at room temperature for 72 hours, completing the prototype manufacturing of the UAV wing.

[0038] Specifically, the UAV wing is fixed on a static test bench to ensure its position is stable and consistent with the test requirements; a predetermined static load is applied to the UAV wing using a loading device, which can be a simulated uniform load or a concentrated load; during the loading process, a fiber optic strain sensor is used to collect the strain data of the UAV wing in real time, and a fiber optic temperature sensor is used to collect the temperature measurement value during the loading process. The strain data and temperature measurement value are input into a fiber optic demodulator, and the target strain data is generated through the processing of the fiber optic demodulator.

[0039] Step S12: Based on the target strain data, the UAV wing is subjected to deformation inversion using a target deformation inversion algorithm to obtain the deformation inversion data of the UAV wing.

[0040] It should be noted that the target deformation inversion algorithm refers to the deformation inversion algorithm based on Ko displacement theory. Ko displacement theory is a structural deformation calculation method based on the strain-displacement relationship. It is mainly used to convert measured strain data into the actual displacement (deformation) of the structure. By measuring the strain distribution on the surface of the structure through strain sensors and combining the geometric parameters of the structure (such as the cross-sectional shape of a beam, material properties, etc.), a mathematical relationship between strain and displacement is established using mechanical models (such as beam theory, plate and shell theory). The deformation field (such as bending, torsion, etc.) of the structure is then inverted through numerical calculations (such as the least squares method). In UAV wing deformation monitoring, Ko displacement theory is usually combined with beam bending theory or thin plate theory to convert strain data into wing deflection (bending deformation) and torsion angle.

[0041] Specifically, the collected target strain data is input into the target deformation inversion algorithm. The algorithm processes and analyzes the target strain data according to the preset mathematical model and calculation formula, and outputs the deformation inversion data of the UAV wing, including the specific values ​​and distribution of the deformation.

[0042] This embodiment employs the above-described scheme, using a static testing system to conduct a static loading test on the UAV wing and collect target strain data of the UAV wing. Based on the target strain data, a target deformation inversion algorithm is used to perform deformation inversion analysis on the UAV wing, obtaining the deformation inversion data of the UAV wing. Combining static loading testing with a target deformation inversion algorithm improves the accuracy of UAV wing deformation measurement.

[0043] Based on the above implementation scheme, in one feasible implementation, the static test system includes a static test bench, a fiber optic strain measurement system, and a fiber optic temperature sensor. The step of conducting a static loading test on the UAV wing using the static test system and collecting the target strain data of the UAV wing includes S21~S22: Step S21: Fix the UAV wing using a static test bench and perform a loading test on the fixed UAV wing.

[0044] It should be noted that the static test bench is a device used to fix the UAV wing and provide stable support, ensuring that the position and attitude of the UAV wing meet the requirements during the test.

[0045] In one embodiment of this application, a self-balancing static test bench is used, designed to accurately measure the elastic deformation of the wing under load during ground static testing. To minimize the impact of bench deformation on wing deformation measurement, the static test bench is designed to improve its rigidity while replicating the actual fuselage-wing connection state. This enhances the stability of the bench, reduces its elastic deformation, and thus ensures the accuracy and reliability of the measurement results. Furthermore, to reduce wing deformation caused by bench deformation, this application employs a symmetrical bench configuration, meaning that both sides completely replicate the wing loading state of a real UAV. When loading is applied, loads are applied simultaneously to both sides, essentially eliminating measurement errors in wing elastic deformation caused by bench deformation.

[0046] Specifically, the drone wing is placed on a static test bench, and the wing is firmly fixed to the bench using clamps or fixing devices to ensure that it will not move during the test; a predetermined static load is applied to the fixed drone wing, including simulated uniform load and concentrated load.

[0047] Step S22: Data is collected from the UAV wing using the fiber optic strain measurement system and the fiber optic temperature sensor to obtain the target strain data of the UAV wing during the loading test.

[0048] It should be noted that a fiber optic grating strain measurement system is a system that uses fiber optic grating sensors to measure strain, acquiring strain information by detecting changes in the wavelength of the fiber optic grating. The system consists of a fiber optic grating strain sensor and a fiber optic grating demodulator. The core of the fiber optic grating strain sensor is the grating region formed by periodic refractive index modulation within the fiber. When the UAV wing deforms, the fiber optics experience strain, causing a change in the grating period and a shift in the reflected light wavelength. The fiber optic grating demodulator monitors the change in the reflected light wavelength in real time and converts it into strain data. The demodulator calculates the corresponding strain value by detecting the wavelength shift of the optical signal. Since temperature changes also affect the reflected wavelength of the fiber optic grating, temperature compensation is required. This can be achieved by deploying temperature sensors near the sensor to measure ambient temperature changes and using a temperature compensation algorithm to eliminate temperature interference with the measurement results. The fiber optic grating demodulator converts the acquired wavelength shift data into strain values, obtaining the strain distribution of the UAV wing at different locations.

[0049] Specifically, the fiber Bragg grating strain measurement system is activated to collect strain data of the UAV wing in real time during the loading process; at the same time, the fiber Bragg grating temperature sensor is activated to measure changes in ambient temperature in order to perform temperature compensation on the fiber Bragg grating strain sensor; the collected strain data and temperature data are processed to obtain the target strain data.

[0050] This embodiment uses the above-described scheme to fix the UAV wing on a static test bench and combines a fiber optic strain measurement system and a fiber optic temperature sensor for data acquisition. This allows for more accurate acquisition of the target strain data of the UAV wing during the loading test, providing a reliable data foundation for subsequent deformation inversion analysis.

[0051] Based on the above implementation scheme, in one feasible implementation, the fiber optic strain measurement system includes a fiber optic strain sensor and a fiber optic demodulator. The step of acquiring target strain data of the UAV wing during the loading test by collecting data from the UAV wing through the fiber optic strain measurement system and the fiber optic temperature sensor includes S31~S33: Step S31: Obtain the initial strain data of the UAV wing during the loading test through the fiber Bragg grating strain sensor, and transmit the initial strain data to the fiber Bragg grating demodulator in the form of an optical signal.

[0052] It should be noted that a fiber optic strain sensor is a sensor based on the principle of a fiber Bragg grating (FBG). It senses strain by forming a periodic refractive index change (grating) within the fiber. When the fiber is subjected to an external force, the period of the grating changes, causing a shift in the wavelength of the reflected light, thereby enabling strain measurement.

[0053] Furthermore, fiber Bragg grating sensors possess high precision in strain and temperature measurements, multi-point distributed layout, strong anti-interference capabilities, and corrosion resistance. However, their high sensitivity to parameters such as temperature and pressure makes the deployment process of fiber Bragg grating sensors a technical challenge. The first-generation process directly covered the grating with epoxy resin, then directly molded and extruded it with a skin. However, during the skin compression and molding process, as well as during experimental loading, the fiber grating area was easily compressed, leading to chirping, peak splitting, and sidelobes. The high elastic modulus and hardness of epoxy resin resulted in inaccurate strain measurements. The second-generation process used softer 704 silicone to protect the grating area, and then fixed the two ends of the grating with epoxy resin. However, despite the use of 704 silicone protection, the fiber grating area was still compressed by the skin during the skin compression and molding process. The 704 silicone was deformed, causing further compression of the grating area, ultimately resulting in sidelobes, chirping, and even breakage in some sensors. The third-generation process completely solves the chirping phenomenon caused by sensor compression during the skin molding process by opening holes in the skin to ensure that the grating area is not compressed at any time. However, since the fiber Bragg grating sensor is embedded inside the wing, if one fiber Bragg grating sensor breaks, the entire series of sensors will be scrapped. Due to the lack of complete strain data, an entire wing will be scrapped.

[0054] This application proposes a sensor deployment method that allows for pre-embedding without causing uneven pressure damage to the sensor grating area. The non-measurement fiber optic cable is routed inside the fuselage and protected by a 2mm inner diameter polyurethane flexible tube. Only the measurement portion of the fiber Bragg grating sensor is deployed on the wing spar surface, with a skin opening at the junction of the measurement and non-measurement sections. If the sensor is damaged, it can be replaced through the protective sleeve, improving both the ease of replacement and reducing replacement costs.

[0055] Furthermore, the fiber Bragg grating strain sensor was first pre-stretched to 1000με~2000με, then simply fixed with instant adhesive. Next, 704 silicone was evenly applied to the grating area, ensuring complete coverage and protecting it from external forces other than the tensile force being measured. The remaining fiber was evenly coated with approximately 1mm thick epoxy resin. This ensured that the epoxy would not peel off during strain measurement after curing and also provided pre-embedded protection for the fiber. Finally, three strings of sensors, totaling 15 measurement points, were installed on the upper and lower surfaces of each beam, and protected with epoxy resin and 704 silicone. After completing the sensor installation on the wing spars, the skin molding process began. To prevent damage to the sensor grating area during wing skin molding, perforations were made at the contact points with the fiber Bragg grating area, while maintaining the wing's strength. This prevented uneven pressure on the fiber Bragg grating sensor grating area during molding.

[0056] In one embodiment of this application, the fiber optic strain sensor uses OSC1100 bare optical fiber manufactured by Beijing Tongwei Technology Co., Ltd. The parameters of this sensor model are shown in Table 1 below:

[0057] Table 1 The strain measurement formula for this type of fiber optic strain sensor is: Δε = Δλ1000 / Eε Where Δε is the calculated strain value, in microstrain με; Δλ is the change in center wavelength, in nm; and Eε is the conversion coefficient, which is 1.2 pm / με for this type of sensor.

[0058] Additionally, it should be noted that a fiber Bragg grating demodulator is a device used to detect and process the reflected light signal from a fiber Bragg grating sensor. It converts the optical signal into an electrical signal and calculates the wavelength change, thereby enabling strain measurement. The fiber Bragg grating demodulator detects the wavelength change of the reflected light from the fiber Bragg grating sensor using an optical filter or a spectrum analyzer, converts the detected wavelength change into an electrical signal, and calculates the strain value using a built-in algorithm.

[0059] Additionally, it should be noted that the initial strain data refers to the strain change of the UAV wing during the loading test, which is transmitted into the fiber Bragg grating demodulator in the form of optical signals.

[0060] Specifically, fiber Bragg grating strain sensors are deployed in key areas of the UAV wing (such as the main spars and skin). This ensures the sensors are tightly integrated with the wing structure, enabling accurate detection of strain changes. In one embodiment of this application, a five-point string layout is used, with five fiber Bragg grating strain sensors in each string. When the UAV wing deforms during a loading test, the fiber Bragg grating strain sensors detect the strain changes, causing a change in the grating period and a shift in the wavelength of the reflected light. The sensors convert these strain changes into optical signals and transmit them to a fiber Bragg grating demodulator.

[0061] Step S32: Temperature compensation is performed on the fiber optic strain sensor using the fiber optic temperature sensor to generate the temperature measurement value of the UAV wing and transmit it to the fiber optic demodulator.

[0062] It should be noted that a fiber Bragg grating (FBG) temperature sensor is a temperature sensor based on the principle of a fiber Bragg grating (FBG). It senses temperature changes by measuring changes in the wavelength of the light reflected from the grating. Temperature changes cause thermal expansion and changes in the refractive index of the fiber, resulting in a shift in the reflected wavelength. Since temperature changes also affect the reflected wavelength of the FBG, temperature compensation is required. This can be achieved by deploying temperature sensors near the FBG to measure changes in ambient temperature and using a temperature compensation algorithm to eliminate the interference of temperature on the measurement results.

[0063] In one embodiment of this application, two fiber Bragg grating temperature sensors are located on each of the two wings of the UAV to perform temperature compensation on the fiber Bragg grating strain sensor, eliminating the center wavelength change caused by temperature variations. In one embodiment of this application, the fiber Bragg grating temperature sensor is model OSC4310, and the main parameters of this strain sensor are shown in Table 2.

[0064] Table 2 The temperature measurement formula for this model of temperature sensor is: ΔT = Δλ1000 / ET Where ΔT is the calculated temperature change, in microstrain με; Δλ is the center wavelength change, in nm; and ET is the temperature conversion coefficient, which is 10 pm / ℃ for this type of sensor.

[0065] Specifically, there are two fiber Bragg grating temperature sensors on each of the two wings of the UAV, used to perform temperature compensation for the fiber Bragg grating strain sensors. The fiber Bragg grating temperature sensors monitor changes in ambient temperature in real time, causing changes in the grating period and a shift in the wavelength of the reflected light; the temperature sensors convert these temperature changes into optical signals and transmit the optical signals to the fiber Bragg grating demodulator.

[0066] Step S33: The initial strain data in optical signal form is converted into an electrical signal by the fiber optic grating demodulator, and the target strain data is obtained based on the initial strain data in electrical signal form and the temperature measurement value.

[0067] Specifically, the fiber optic grating demodulator detects the wavelength changes of the reflected light from the fiber optic strain sensor and temperature sensor using an optical filter or a spectrometer; the demodulator converts the detected optical signal into an electrical signal and calculates the wavelength changes of the strain sensor and temperature sensor according to a preset formula and algorithm; and by using a temperature compensation formula, the influence of temperature on the reflected wavelength is subtracted from the strain measurement results, thereby obtaining an accurate strain value.

[0068] In one embodiment of this application, the temperature compensation formula is: Δλ=λ0(αΔT+ξΔT) Where Δλ is the change in center wavelength, λ0 is the initial wavelength, ΔT is the temperature change, α is the thermal expansion coefficient of the optical fiber material, and ξ is the thermo-optic coefficient of the optical fiber material.

[0069] For example, assuming the strain sensor detects a wavelength change of 0.5 nm and the temperature sensor detects a wavelength change of 0.2 nm; according to the temperature compensation formula Δλ=λ0(αΔT+ξΔT), the effect of temperature change on wavelength is calculated to be 0.1 nm. Subtracting the temperature effect from the wavelength change of the strain sensor, the actual wavelength change caused by strain is obtained as 0.4 nm. Then, the target strain data is calculated using the strain measurement formula of the fiber optic strain sensor.

[0070] This embodiment, through the above-described scheme, combines a fiber Bragg grating strain sensor and a fiber Bragg grating temperature sensor, along with data processing by a fiber Bragg grating demodulator, to accurately measure the strain data of a UAV wing during a loading test and eliminate the interference of temperature changes on the measurement results. This method offers advantages such as high precision, resistance to electromagnetic interference, and real-time monitoring, providing reliable data support for structural health monitoring of UAV wings.

[0071] Based on the above implementation scheme, in one feasible implementation, the loading test includes simulating uniformly distributed load and concentrated load, and the step of performing the loading test on the fixed UAV wing includes S41~S42: Step S41, simulate a uniformly distributed load on the UAV wing by uniformly loading and / or unloading the lower surface of the UAV wing.

[0072] It should be noted that simulated uniformly distributed load is a method of applying loads that simulate the uniformly distributed external loads experienced by an airfoil during flight. This is achieved by uniformly applying a load to the lower surface of the airfoil. This method can simulate the aerodynamic loads experienced by the airfoil during flight and is used to evaluate the deformation of the airfoil under uniform load. By monitoring the deformation and strain of the airfoil under simulated uniformly distributed load, the inversion accuracy of the deformation inversion system under normal flight conditions can be verified.

[0073] Specifically, the UAV wing is fixed on a static test bench to ensure its position is stable and consistent with the test requirements. Multiple loading points are evenly distributed on the lower surface of the wing to ensure uniform load distribution. A loading device is used to uniformly apply a load to the lower surface of the wing. The load can be gradually increased from 0 kg to a first load weight and then gradually decreased back to 0 kg. The first load weight is determined according to specific conditions; in this application, 40 kg is commonly used as the first load weight. During the loading process, strain measurement equipment (such as fiber optic strain sensors) is used to collect the wing's strain data in real time. The load value and corresponding strain data at each loading point are recorded during the loading process, ensuring a sufficiently high data acquisition frequency to capture the real-time strain changes of the wing during loading. The loading points can be selected independently, typically 8 kg, 16 kg, 20 kg, 24 kg, 32 kg, and 40 kg.

[0074] Step S42, apply concentrated load to the UAV wing by uniformly loading and / or unloading at the end position of the UAV wing.

[0075] It should be noted that concentrated loading is a loading method that applies all loads to a location near the wingtip, used to simulate special operating conditions that may occur during flight, such as concentrated forces on the wingtip. This method can assess the deformation of the wing under localized high loads. By monitoring the deformation and strain of the wing under concentrated loads, the inversion accuracy of the deformation inversion system under unconventional load conditions can be verified.

[0076] Specifically, a loading point is selected at the wingtip to ensure concentrated load application. A loading device is used to uniformly apply the load to the wingtip, gradually increasing the load from 0 kg to a second load weight and then gradually decreasing it back to 0 kg. The second load weight is determined based on specific conditions; in this application, 20 kg is commonly used. During loading, strain measurement equipment (such as a fiber optic strain sensor) is used to collect wing strain data in real time. The load value and corresponding strain data at each loading point are recorded, ensuring a sufficiently high data acquisition frequency to capture real-time strain changes in the wing during loading. The loading point can be selected independently, typically 4 kg, 8 kg, 10 kg, 16 kg, 18 kg, and 20 kg.

[0077] This embodiment, through the above-described scheme and by simulating uniformly distributed loads and concentrated loads, can comprehensively evaluate the deformation of an unmanned aerial vehicle (UAV) wing under different operating conditions. Simulating uniformly distributed loads can assess the overall deformation behavior of the wing under uniform loads, while concentrated loads can assess the local deformation behavior of the wing under locally high loads. The combination of these two loading methods provides rich data support for the verification and optimization of the deformation inversion algorithm, thereby improving the accuracy and reliability of deformation inversion.

[0078] Based on the above implementation scheme, in one feasible implementation, the deformation inversion data includes first inversion data and second inversion data. The step of performing deformation inversion on the UAV wing based on the target strain data using a target deformation inversion algorithm to obtain the deformation inversion data of the UAV wing includes S51~S52: Step S51: Obtain the first strain data of the UAV wing under concentrated load, and perform deformation inversion on the first strain data using a target deformation inversion algorithm to generate the first inversion data of the UAV wing under concentrated load.

[0079] It should be noted that the first strain data refers to the strain data of the UAV wing collected by a fiber optic strain sensor under concentrated load.

[0080] Additionally, it should be noted that the first inversion data refers to the deformation data of the UAV wing under concentrated load obtained after processing the first strain data through the target deformation inversion algorithm.

[0081] Specifically, in the concentrated load test, strain data of the UAV wing is acquired in real time using fiber optic strain sensors; this data constitutes the first strain data. The acquired first strain data is transmitted to a computing device (such as a host computer). The first strain data is then processed using a target deformation inversion algorithm. The algorithm is typically based on a finite element model or optimization algorithm, which converts the strain data into deformation data through a mathematical model. The deformation data of the UAV wing under concentrated load is then calculated using the deformation inversion algorithm; this is the first inversion data.

[0082] Step S52: Obtain the second strain data of the UAV wing under simulated uniform load, and perform deformation inversion on the first strain data using a target deformation inversion algorithm to generate the second inversion data of the UAV wing under simulated uniform load.

[0083] It should be noted that the second deformation data refers to the strain data of the UAV wing collected by a fiber optic strain sensor under simulated uniform load.

[0084] Additionally, it should be noted that the second inversion data refers to the deformation data of the UAV wing under simulated uniform load obtained after processing the second strain data through the target deformation inversion algorithm.

[0085] Specifically, in the simulated uniformly distributed load test, strain data of the UAV wing is collected in real time using fiber optic strain sensors; this data is the second strain data. The collected second strain data is transmitted to a computing device (such as a host computer); the second strain data is processed using a target deformation inversion algorithm. The algorithm is usually based on a finite element model or optimization algorithm, which converts the strain data into deformation data through a mathematical model; the deformation data of the UAV wing under the simulated uniformly distributed load is calculated using the deformation inversion algorithm, i.e., the second inversion data.

[0086] This embodiment, through the aforementioned scheme, performs deformation inversion on strain data under concentrated load and simulated uniformly distributed load, enabling a more comprehensive evaluation of the UAV wing's deformation under different operating conditions. Deformation inversion under concentrated load can assess the local deformation behavior of the wing under locally high loads, while deformation inversion under simulated uniformly distributed loads can assess the overall deformation behavior of the wing under uniform loads. The combination of these two types of deformation inversion data provides more accurate data support for UAV design optimization and maintenance, contributing to improved UAV flight safety and performance.

[0087] Based on the above implementation scheme, in one feasible implementation, after the step of conducting a static loading test on the UAV wing using a static testing system and collecting the target strain data of the UAV wing, the method further includes steps S61 to S62: Step S61: Obtain the third strain data of the UAV wing in the static loading test using a resistance strain gauge, and compare the target strain data with the third strain data.

[0088] It should be noted that a resistance strain gauge is a traditional strain measurement device that measures strain by measuring changes in resistance and is used to verify the measurement results of a fiber optic strain sensor. Based on the resistance strain effect—that is, when a conductor or semiconductor material undergoes mechanical deformation under external force, its resistance changes accordingly—a resistance strain gauge is attached to the surface of the component being measured. When the component deforms, the resistance of the strain gauge changes, and the strain value is calculated by measuring this change in resistance using a strain gauge. The use of a resistance strain gauge is an electrical measurement method; each strain gauge requires two wires to form a measurement circuit, and special measures are needed to enhance the system's electromagnetic interference resistance. However, the fiber optic strain sensor of this application can achieve distributed measurement, with multiple sensors accommodating a single optical fiber.

[0089] In one embodiment of this application, a resistance strain gauge is attached tightly to the surface of a UAV wing spars using instant adhesive, and its placement is as follows: Figure 2 As shown, a total of 15 resistance strain gauges are deployed, all positioned 10 mm away from the fiber Bragg grating strain sensor. The resistance strain gauge acquisition system and the displacement measurement system share the same acquisition system, such as the Donghua Testing DH3820N distributed signal testing and analysis system. In one embodiment of this application, the resistance strain gauge model used is BE120-3AA-Q30P2K, and the main parameters of this strain sensor model are shown in Table 3.

[0090] Table 3 Additionally, it should be noted that the third strain data refers to the strain data of the UAV wing collected by a resistance strain gauge during the static loading test, which is used to compare with the target strain data collected by the fiber optic strain sensor.

[0091] Specifically, a resistance strain gauge is placed 10 mm away from the fiber optic strain sensor. During the static loading test, the strain data of the UAV wing is collected in real time by the resistance strain gauge, and these data are the third strain data.

[0092] Step S62: If the error between the target strain data and the third strain data is less than a preset error threshold, then a deformation inversion analysis is performed based on the target strain data.

[0093] It should be noted that the preset error threshold is used to determine whether the measurement results of the fiber optic strain sensor and the resistance strain gauge are consistent.

[0094] Specifically, the target strain data acquired by the fiber optic strain sensor is compared with the third strain data acquired by the resistance strain gauge, and the error between the two sets of data is calculated, i.e., the difference between the target strain data and the third strain data. It is then determined whether the calculated error is less than the preset error threshold. If the error is less than the error threshold, it indicates that the measurement results of the fiber optic strain sensor are reliable and can be used for subsequent deformation inversion analysis. If the error is greater than or equal to the error threshold, the sensor installation and data acquisition process need to be further checked, and the test should be repeated if necessary.

[0095] Furthermore, the third strain data of the UAV wing in the static loading test is obtained by using a resistance strain gauge. The accuracy analysis of the target strain data based on the third strain data is to verify the measurement accuracy of the fiber Bragg grating strain measurement system. Once it is confirmed that the measurement accuracy of the fiber Bragg grating strain measurement system meets the requirements, it is not necessary to obtain the strain data of the UAV wing through a resistance strain gauge in the actual application process. This is because, compared to the fiber Bragg grating strain measurement system, which requires two wires to form a measurement loop for one resistance strain gauge, the fiber Bragg grating strain sensor can perform distributed measurement, and a single optical fiber can accommodate multiple sensors.

[0096] This embodiment verifies the measurement accuracy of the fiber Bragg grating strain sensor by comparing its measurement results with those of a resistance strain gauge using the aforementioned method. The resistance strain gauge, as a traditional measurement method, possesses high reliability and accuracy and can serve as a reference for the fiber Bragg grating strain sensor's measurement results. If the difference between the two measurement results is less than a preset error threshold, the fiber Bragg grating strain sensor's measurement results can be considered reliable, thus providing accurate data support for subsequent deformation inversion analysis.

[0097] Based on the above implementation scheme, in one feasible implementation method, the deformation inversion method of the UAV wing further includes steps S71-S72: Step S71: In the static loading test, the true value of the deformation measurement of the UAV wing is obtained through the displacement measurement system.

[0098] It should be noted that the displacement measurement system is used to directly measure the wing deformation of the UAV, and typically uses high-precision displacement sensors, such as draw-wire sensors and laser displacement sensors. In this application, a draw-wire sensor is used as the true value measurement device for wing displacement. The draw-wire sensor provides a voltage signal that proportionally modifies the length of the draw-wire to the extension of the retractable stainless steel cable to achieve displacement measurement, offering advantages such as high precision, ease of installation, and strong anti-interference capabilities. In one embodiment of this application, the selected displacement measurement system is the Micro-Measurements ModelCDS-10, with a range of 254 mm and an accuracy of 0.1%FS; therefore, its error is approximately 0.2 mm.

[0099] Furthermore, during the deployment of the cable sensors, a hook was first attached to the wing's measurement location using instant adhesive, and then the cable sensor's latch was connected to the hook. During measurement, it was crucial to ensure the cable remained perpendicular to the ground. Finally, six measurement points were placed at each of the front and rear spars on each side of the wing, for a total of twelve measurement points, to measure the actual deformation of the wing under load.

[0100] In addition, the displacement measurement data acquisition system adopts the Donghua Test DH3820N distributed signal test and analysis system. The maximum sampling rate of this test system is 5kHz (kilohertz), and the bridge voltage power supply (DC) can be switched between 2V (volt), 5V, and 10V. The bridge voltage of the displacement measurement system is 10V, and the bridge voltage of the strain measurement system is 2V. The strain range is ±50000με, and the voltage range is ±50mV (millivolt), ±500mV, ±5V, and ±10V.

[0101] Additionally, it should be noted that the true value of the deformation measurement is the deformation data of the UAV wing obtained directly through the displacement measurement system, which is used to verify the accuracy of the deformation inversion data.

[0102] Specifically, displacement sensors are installed as needed, ensuring a tight fit between the sensors and the wing structure to accurately detect wing deformation. During the static loading test, the deformation data of the UAV wing is collected in real time through the displacement measurement system; these data are the true deformation measurements. The data acquisition frequency is ensured to be high enough to capture the real-time deformation changes of the wing during loading. The collected true deformation measurements are recorded for subsequent comparison with deformation inversion data.

[0103] Step S72: Compare the deformation inversion data with the true value of the deformation measurement, and determine the deformation inversion result of the static test system for the UAV wing based on the comparison result.

[0104] Specifically, the deformation inversion data is compared with the true deformation measurement values, the difference between the two is calculated, and the calculated error is evaluated to determine if it is within an acceptable range. If the deformation inversion data matches the true deformation measurement values, the deformation inversion algorithm is effective and can be used for subsequent deformation monitoring. If the deformation inversion data does not match the true deformation measurement values, the deformation inversion algorithm needs to be optimized or adjusted.

[0105] Furthermore, the true value of the deformation measurement of the UAV wing in the static loading test is obtained through the displacement measurement system. The accuracy analysis of the deformation inversion data based on the true value of the deformation measurement is to verify the measurement accuracy of the fiber grating strain measurement system. Once it is confirmed that the measurement accuracy of the fiber grating strain measurement system meets the requirements, it is not necessary to obtain the true value of the deformation measurement of the UAV wing through the displacement measurement system. The deformation inversion of the UAV wing can be directly performed using the fiber grating strain measurement system to calculate the deformation of the UAV wing.

[0106] This embodiment verifies the accuracy of the deformation inversion algorithm by comparing the deformation inversion data with the true deformation measurement values. The displacement measurement system, as a high-precision direct measurement method, can provide the true deformation values, offering a reliable reference for verifying the deformation inversion data. If the deformation inversion data matches the true deformation measurement values, it indicates that the deformation inversion algorithm accurately reflects the actual deformation of the UAV wing, providing reliable data support for UAV design optimization and maintenance. This method not only improves the reliability of deformation monitoring but also provides a practical basis for optimizing the deformation inversion algorithm.

[0107] For example, in one embodiment of this application, the deformation inversion data obtained by the fiber optic strain measurement system and the true deformation measurement value obtained by the displacement measurement system are compared, and the deformation inversion results of the fiber optic strain measurement system are analyzed. The following is an analysis of the deformation inversion results.

[0108] 1. Inversion results of concentrated load deformation on the left wing (1) Deformation inversion results of the main beam under concentrated load The strain distribution of the main spars from the wing root to the wing tip generally shows a decreasing trend, and the strain distribution on the upper and lower surfaces of the spars is relatively similar, but the strain gradient on the lower surface is more significant. The strain distribution of the secondary spars shows a decreasing trend from the wing root to the strut, and then gradually increases from the strut to the wingtip. Some localized changes occur near the strut because the structural stiffness at the strut is relatively high, resulting in a sudden increase in stiffness compared to other parts of the wing. Therefore, when the wing is under load, the strain changes on both sides of the strut are more pronounced, especially for the secondary spars, which are more affected by the strut due to their lower strain stiffness compared to the main spars. The strain gradient on the lower surface is also relatively larger. Furthermore, as the load gradually increases, the strain distribution trend remains basically unchanged on both the upper and lower surfaces, as well as on the front and rear spars, with only a numerical increase. This trend reflects the overall stability and stiffness of the structure. Further observation reveals that the strain at the root of the main spars is greater than that at the root of the secondary spars. This can be attributed to the main spars, as the primary load-bearing component of the wing, bearing greater stress and deformation under concentrated loads.

[0109] This application employs a deformation inversion algorithm based on Ko displacement theory, utilizing strain data acquired by fiber Bragg grating sensors to invert the deformation of the UAV wing. During the experiment, multiple sets of data were loaded, covering a load range from 0 kg to 20 kg and back to 0 kg.

[0110] First, we focused on the deformation inversion results of the main beam when the load was 4kg, 8kg, 10kg, 16kg, 18kg and 20kg. We observed that the deformation inversion system used in this application showed high inversion accuracy.

[0111] Furthermore, it was observed that as the load increased and then decreased, both the absolute error at the endpoint and the overall root mean square error of the wing decreased. Within a load range of 10 kg, the endpoint error remained within 1 mm. When the load was between 10 kg and 20 kg, the absolute error at the endpoint ranged from 1.0 mm to 4.0 mm, while the overall root mean square error increased slightly but remained within 4 mm. Moreover, it was observed that during the unloading phase, both the endpoint error and the overall root mean square error were larger than during the loading phase. As the load weight first increased and then decreased, the relative error at the endpoint initially rose from -4% to 10%. During the loading phase, when the load was less than 10 kg, the relative error at the endpoint was within 2%, and when the load was greater than 10 kg but less than 20 kg, the error at the endpoint was between 2% and 3%.

[0112] (2) Deformation inversion results of sub-beam under concentrated load Observations show that the error at the end points is smaller than the overall deformation inversion error. During loading and unloading, the error at the end points is within 2mm, and the overall error is within 3mm. The basic trend is that the error increases with increasing load and then decreases with decreasing load. Based on the relative error at the end points of the sub-beam under different concentrated loads, the relative error gradually increases during loading and unloading, from 1% to 3%.

[0113] (3) Calculation of airfoil torsion angle under concentrated load The inverted twist angle of the wing can be calculated by measuring the deformation of the main spar and the aft spar. When the main spar and the aft spar deform in the same direction, the formula for calculating the inverted twist angle of the wing is:

[0114] in, For the inversion twist angle of the wing; Deformation of the main beam; The deformation of the secondary beam; L is the distance between the main beam and the secondary beam.

[0115] By using the above method and combining the existing true deformation and inverted deformation of this application, the true torsion angle of the wing under load and the inverted torsion angle obtained by inverting the deformation through the inversion algorithm can be calculated.

[0116] First, after averaging the twist angles at different wing positions, we analyzed the changes in twist angles under different loads. It can be seen that both the inverted and actual twist angles increase with increasing load and decrease with decreasing load. The largest actual twist angle occurs at a load of 20 kg, reaching 0.3 degrees. The absolute error in calculating the twist angles shows that the overall calculation error is between -0.5 degrees and 0.1 degrees.

[0117] Further analysis of the torsion angle error at different positions revealed that the accuracy of the torsion angle at the wingtip was relatively high, with an error within 0.1 degrees, and the overall error was between -0.5 degrees and 0.1 degrees.

[0118] As can be seen from the formula for calculating the torsion angle, the absolute error levels of the main beam and the secondary beam directly affect the calculated torsion angle. When there is a 1mm error in the inversion displacement of the main beam and the secondary beam, this will result in a torsion angle calculation error of 0.4 degrees. Therefore, by comparing the absolute error levels of the main beam and the secondary beam, their influence on the torsion angle calculation can be evaluated. Even if the average absolute error levels of the main beam and the secondary beam are both within 3mm, the relatively larger average absolute error of the main beam inversion results in a greater impact on the torsion angle calculation.

[0119] 2. Inversion results of simulated uniformly distributed load deformation on the left wing (1) Inversion results of main beam deformation under simulated uniformly distributed load The observations show that the strain distribution of the main spars generally decreases from the wing root to the wingtip, and the strain distribution on the upper and lower surfaces of the spars is relatively similar. The strain distribution of the secondary spars decreases from the wing root to the strut, then gradually increases from the strut to the wingtip. Some localized changes occur near the strut due to the higher structural stiffness at the strut, resulting in a sudden increase in stiffness compared to other parts of the wing. Therefore, when the wing is under load, the strain changes on both sides of the strut are more pronounced, especially for the secondary spars, which are more affected by the strut because their strain stiffness is relatively smaller than that of the main spars. The strain gradient on the lower surface is also relatively larger. Furthermore, as the load gradually increases, the shape of the strain distribution remains basically unchanged on both the upper and lower surfaces, as well as on the front and rear spars, with only a numerical increase. This trend reflects the overall stability and stiffness of the structure. Further observation reveals that the strain at the root of the main spars is greater than that at the root of the secondary spars. This can be attributed to the main spars, as the primary load-bearing component of the wing, bearing greater stress and deformation under simulated uniformly distributed loads.

[0120] This application employs a deformation inversion algorithm based on Ko displacement theory, utilizing strain data acquired by fiber Bragg grating sensors to invert the deformation of the UAV wing. During the experiment, 19 sets of data were loaded, covering a load range from 0 kg to 40 kg and back to 0 kg. The deformation inversion results of the main beam were first examined under loads of 8 kg, 16 kg, 20 kg, 24 kg, 32 kg, and 40 kg. The deformation inversion system used in this application exhibits high inversion accuracy.

[0121] As the load increases and then decreases, the absolute error at the end point and the overall root mean square error of the wing both first increase and then decrease. Within a load range of 40 kg, the end point error remains within 1 mm. The overall root mean square error is within 4 mm. Studying the error level of the wing main spars at all different locations, from the wing root to the wing tip, the overall error level is within 3.5 mm.

[0122] As the load weight first increases and then decreases, the relative error at the end point gradually increases. When the load is between 10kg and 40kg, the relative error is within 3%.

[0123] (2) Inversion results of sub-beam deformation under simulated uniformly distributed load Further analysis of the inversion results shows that the error at the end points is slightly greater than the overall error. The inversion error at the end points is within 4 mm, while the overall error is within 3 mm.

[0124] By observing the deformation inversion accuracy at different locations of the sub-spar under simulated uniform load, it can be seen that the deformation inversion error gradually increases from the wing root to the wing tip, but the overall error remains within 2.5 mm. When the load is between 10 and 40 kg, the relative error is around 5%.

[0125] (3) Calculation of wing torsion angle under simulated uniform load Based on the calculated torsion angle at the wing tip, it was first observed that after averaging the torsion angle at different wing positions, the variation of the torsion angle under different loads was analyzed. It can be seen that both the inverted and actual torsion angles show a trend of increasing with increasing load and decreasing with decreasing load. The largest actual torsion angle occurred at a load of 20 kg, reaching 0.3 degrees. In contrast, the inverted torsion angle has higher accuracy; the absolute error of the calculated torsion angle shows that the overall calculation error is between -0.1 degrees and 0.1 degrees.

[0126] Further analysis of the twist angle error at different locations revealed that the overall distribution of the wing twist angle was relatively consistent, roughly around 0.1 degrees. However, the retrieved twist angle was larger than expected, with the error level within 0.1 degrees.

[0127] Even though the average absolute error levels of the main beam and the secondary beam are basically within 1mm to 3mm, which is relatively small, the calculation error of the torsion angle is also relatively small. However, by comparing the error levels of the main beam and the secondary beam, it can be seen that the error of the main beam has a greater impact on the torsion angle error under the simulated uniform load.

[0128] 3. Inversion results of concentrated load deformation on the right wing (1) Deformation inversion results of the main beam under concentrated load Observations of strain distribution on the upper and lower surfaces of the sub-spar under concentrated loads from 0 to 20 kg show that the strain distribution of the main spar from the wing root to the wingtip generally exhibits a decreasing trend, and the strain distributions on the upper and lower surfaces of the spar are relatively similar, but the strain gradient on the lower surface is more significant. The strain distribution of the sub-spar decreases from the wing root to the strut, and then gradually increases from the strut to the wingtip. Some local changes occur near the strut because the structural stiffness at the strut is relatively high, resulting in a sudden increase in stiffness compared to other parts of the wing. Therefore, when the wing is loaded, the strain changes on both sides of the strut are more pronounced, especially for the sub-spar, which is more affected by the strut due to its relatively lower strain stiffness compared to the main spar. The strain gradient on the lower surface is also relatively larger. Furthermore, as the load gradually increases, the shape of the strain distribution remains basically unchanged on both the upper and lower surfaces, as well as on the front and rear spars, with only a numerical increase. This trend reflects the overall stability and stiffness of the structure. Further observation reveals that the strain at the root of the main spar is greater than that at the root of the sub-spar. This can be attributed to the fact that the main spar, as the primary load-bearing component of the wing, bears greater stress and deformation when subjected to concentrated loads.

[0129] This application employs a deformation inversion algorithm based on Ko displacement theory, utilizing strain data acquired by fiber Bragg grating sensors to invert the deformation of the UAV wing. During the experiment, multiple sets of data were loaded, covering a load range from 0 kg to 20 kg and back to 0 kg.

[0130] First, we focused on the deformation inversion results of the main girder under loads of 4kg, 8kg, 10kg, 16kg, 18kg, and 20kg. We observed that the deformation inversion system used in this application exhibited high inversion accuracy. As the load increased and then decreased, the absolute error at the end points and the root mean square error of the entire wing decreased accordingly. During loading and unloading, the overall error remained within 3mm. The end point error was within 3mm during the loading phase, and the error during the unloading phase was between 2mm and 4mm.

[0131] The study of the error level of the wing main spars at all different locations shows that the overall error level is within 2.5mm from the wing root to the wing tip.

[0132] As the load weight first increases and then decreases, the relative error at the end point first rises from -5% to 15%. During the loading phase, when the load is less than 20kg, the relative error at the end point is within 2.5%.

[0133] (2) Deformation inversion results of sub-beam under concentrated load Preliminary observations indicate that the overall deformation inversion accuracy is relatively high.

[0134] Further analysis of the inversion accuracy shows that under different concentrated loads, the deformation inversion accuracy at the end of the sub-beam is within 1 mm during the loading stage, and the overall accuracy of the sub-beam is within 3 mm.

[0135] Further observation shows that the relative error gradually increases during the loading and unloading process, with the relative error during loading ranging from -5% to 1%.

[0136] (3) Calculation of airfoil torsion angle under concentrated load Based on the calculated torsion angle at the wing tip, the torsion angles at different locations on the wing were averaged. Then, the variation of the torsion angle under different loads was analyzed. It can be seen that both the inverted and actual torsion angles increase with increasing load and decrease with decreasing load. The largest actual torsion angle occurs at a load of 20 kg, reaching 0.2 degrees. In comparison, the accuracy of the inverted torsion angle is higher. Based on the absolute error of the calculated torsion angle, it can be seen that the overall calculation error is between -0.15 degrees and 0.1 degrees.

[0137] Further analysis of the twist angle error at different locations reveals that the overall distribution of the wing twist angle is relatively consistent, roughly around 0.1 degrees. However, the retrieved twist angle is larger, with an error level within ±0.2 degrees.

[0138] Even though the average absolute error of the main beam and the secondary beam is within 2mm, the average absolute error of the secondary beam is relatively large, which has a greater impact on the calculation error of the torsion angle.

[0139] 4. Inversion results of simulated uniformly distributed load deformation on the right wing (1) Inversion results of main beam deformation under simulated uniformly distributed load The observations show that the strain distribution of the main spars generally decreases from the wing root to the wingtip, and the strain distribution on the upper and lower surfaces of the spars is relatively similar. The strain distribution of the secondary spars decreases from the wing root to the strut, then gradually increases from the strut to the wingtip. Some localized changes occur near the strut due to the higher structural stiffness at the strut, resulting in a sudden increase in stiffness compared to other parts of the wing. Therefore, when the wing is under load, the strain changes on both sides of the strut are more pronounced, especially for the secondary spars, which are more affected by the strut because their strain stiffness is relatively smaller than that of the main spars. The strain gradient on the lower surface is also relatively larger. Furthermore, as the load gradually increases, the shape of the strain distribution remains basically unchanged on both the upper and lower surfaces, as well as on the front and rear spars, with only a numerical increase. This trend reflects the overall stability and stiffness of the structure. Further observation reveals that the strain at the root of the main spars is greater than that at the root of the secondary spars. This can be attributed to the main spars, as the primary load-bearing component of the wing, bearing greater stress and deformation under simulated uniformly distributed loads.

[0140] This application employs a deformation inversion algorithm based on Ko displacement theory, utilizing strain data acquired by fiber Bragg grating sensors to invert the deformation of the UAV wing. During the experiment, multiple sets of data were loaded, covering a load range from 0 kg to 40 kg and back to 0 kg.

[0141] First, we focused on the deformation inversion results of the main beam when the load was 8kg, 16kg, 20kg, 24kg, 32kg and 40kg. It can be observed that the deformation inversion system used in this application shows high inversion accuracy.

[0142] Furthermore, as the load increases and then decreases, both the absolute error at the end point and the root mean square error of the entire wing decrease accordingly. Within a load range of 40 kg, the end point error remains within 4 mm, and the overall error is within 3.5 mm. During the loading phase, when the load is greater than 10 kg but less than 40 kg, the relative error at the end point is within 5%.

[0143] (2) Inversion results of sub-beam deformation under simulated uniformly distributed load Further analysis of the deformation inversion results reveals that during the loading stage, the end-point error remained stable within 2 mm, and the overall error of the sub-beam was also within 2 mm. During the loading stage, the relative deformation error at the end points ranged from -5% to 1%.

[0144] (3) Calculation of wing torsion angle under simulated uniform load Based on the calculated torsion angle at the wing tip, the torsion angle at different locations was averaged. Then, the variation of the torsion angle under different loads was analyzed. It can be seen that both the inverted and actual torsion angles show an increasing trend with increasing load and a decreasing trend with decreasing load. The maximum actual torsion angle occurs at a load of 40 kg, reaching 0.25 degrees. Based on the absolute error of the calculated torsion angle, it can be seen that the overall calculation error is between -0.1 degrees and 0.4 degrees.

[0145] Further analysis of the twist angle error at different locations revealed that the overall distribution of the wing twist angle was relatively consistent, roughly around 0.2 degrees. However, the inverted twist angle was larger, with a twist angle of 0.6 degrees at the inverted tip. The overall error level of the inverted twist angle was within 0.4 degrees.

[0146] As can be seen from the formula for calculating the torsion angle, the absolute error levels of the main beam and the secondary beam directly affect the calculation results of the torsion angle. When there is a 1mm error in the inversion displacement of the main beam and the secondary beam, this will result in a torsion angle calculation error of 0.4 degrees. In reality, the actual torsion angle caused by deformation is usually between 0.5 degrees and 1 degree. Therefore, by comparing the absolute error levels of the main beam and the secondary beam, their influence on the torsion angle calculation can be evaluated.

[0147] Even though the average absolute error of the main beam and the secondary beam is within 2mm, the average absolute error of the main beam is relatively large, which has a significant impact on the calculation of the torsion angle.

[0148] 5. Analysis and Summary of Deformation Inversion Results Table 4 summarizes the deformation inversion results for both wings. Taking the left wing as an example, under concentrated load, the overall root mean square error (RMS) of the main wing spars is 2.124 mm, and the end RMS error is 1.146 mm. Under uniformly distributed load, the overall RMS error of the main wing spars is 2.599 mm, and the end RMS error is 2.332 mm. Under concentrated load, the overall RMS error of the secondary spars is 1.419 mm, and the end RMS error is 1.168 mm. The overall RMS error of the torsion angle is 0.164°, and the end RMS error is 0.215°. Under uniformly distributed load, the overall RMS error of the main wing spars is 1.561 mm, and the end RMS error is 2.266 mm. The overall RMS error of the torsion angle is 0.176°, and the end RMS error is 0.262°.

[0149] Furthermore, the deformation inversion accuracy under concentrated load is higher than that under simulated uniformly distributed load, which is preliminarily inferred to be related to the strain distribution. The concentrated load has a larger overall strain, and the strain is close to zero only in a small area at the wingtip, while the strain under simulated uniformly distributed load is smaller, or even zero, near the wingtip, which will have a certain impact on the accuracy of deformation inversion.

[0150]

[0151] Table 4 For example, to help understand the implementation process of the UAV wing deformation inversion method obtained by combining this embodiment with the above embodiment one, please refer to... Figure 3 , Figure 3 A simplified flowchart of a method for deformation inversion of an unmanned aerial vehicle (UAV) wing is provided, specifically: First, the UAV wing is fixed to a static test bench, and a static load is applied through a series of counterweights. Fiber Bragg grating sensors (fiber Bragg strain sensors) are installed on the wing to monitor and collect strain data in real time during the loading process. This data is transmitted via optical fiber to a fiber Bragg grating demodulator, which converts the optical signals into electrical signals. The demodulator then analyzes and displays the wing deformation using wing deformation analysis and display software. Algorithms (such as Ko displacement theory) are used to visualize the wing deformation inversion results, thereby achieving accurate measurement and inversion of the UAV wing deformation.

[0152] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the deformation inversion method of the UAV wing of this application. Any simple transformations based on this technical concept are within the protection scope of this application.

[0153] This application also provides a deformation inversion device for an unmanned aerial vehicle (UAV) wing; please refer to [reference needed]. Figure 4 The deformation inversion device for the UAV wing includes: The strain data acquisition module 401 is used to conduct a static loading test on the UAV wing through a static test system and acquire the target strain data of the UAV wing. The deformation data calculation module 402 is used to perform deformation inversion analysis on the UAV wing based on the target strain data using a target deformation inversion algorithm, and obtain the deformation inversion data of the UAV wing.

[0154] The UAV wing deformation inversion device provided in this application, employing the UAV wing deformation inversion method described in the above embodiments, can solve the technical problem of low accuracy in UAV wing deformation measurement. Compared with the prior art, the beneficial effects of the UAV wing deformation inversion device provided in this application are the same as those of the UAV wing deformation inversion method provided in the above embodiments, and other technical features in the UAV wing deformation inversion device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0155] This application provides a deformation inversion device for an unmanned aerial vehicle (UAV) wing. The UAV wing deformation inversion device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable 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 UAV wing deformation inversion method in Embodiment 1 above.

[0156] The following is for reference. Figure 5 The diagram illustrates a structural schematic of a deformation inversion device suitable for implementing embodiments of the present application for a drone wing. The deformation inversion device for a drone wing in embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The deformation inversion device for the drone wing shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0157] like Figure 5 As shown, the deformation inversion device for a UAV wing may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the UAV wing deformation inversion device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the deformation inversion device of the UAV wing to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows deformation inversion devices of the UAV wing with various systems, it should be understood that it is not required to implement or possess all of the systems shown. More or fewer systems may be implemented alternatively.

[0158] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0159] The UAV wing deformation inversion device provided in this application, employing the UAV wing deformation inversion method described in the above embodiments, can solve the technical problem of low accuracy in UAV wing deformation measurement. Compared with the prior art, the beneficial effects of the UAV wing deformation inversion device provided in this application are the same as those of the UAV wing deformation inversion method provided in the above embodiments, and other technical features of this UAV wing deformation inversion device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0160] 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 suitable manner in one or more embodiments or examples.

[0161] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0162] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the deformation inversion method of the UAV wing in the above embodiments.

[0163] 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 or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0164] The aforementioned computer-readable storage medium may be included in the deformation inversion device for the UAV wing; or it may exist independently and not be assembled into the deformation inversion device for the UAV wing.

[0165] The aforementioned computer-readable storage medium carries one or more programs. When the aforementioned one or more programs are executed by the deformation inversion device for the UAV wing, the deformation inversion device for the UAV wing causes the UAV wing to: conduct a static loading test on the UAV wing using a static test system to collect target strain data of the UAV wing; and perform deformation inversion analysis on the UAV wing based on the target strain data using a target deformation inversion algorithm to obtain deformation inversion data of the UAV wing.

[0166] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0167] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0168] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0169] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described deformation inversion method for UAV wings, which can solve the technical problem of low accuracy in UAV wing deformation measurement. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the UAV wing deformation inversion method provided in the above embodiments, and will not be repeated here.

[0170] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for inverting the deformation of an unmanned aerial vehicle wing.

[0171] The computer program product provided in this application can solve the technical problem of low accuracy in deformation measurement of UAV wings. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the UAV wing deformation inversion method provided in the above embodiments, and will not be repeated here.

[0172] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for deformation inversion of an unmanned aerial vehicle (UAV) wing, characterized in that, The deformation inversion method for the UAV wing includes: A static loading test was conducted on the UAV wing using a static testing system, and the target strain data of the UAV wing was collected. Based on the target strain data, the deformation inversion algorithm is used to perform deformation inversion on the UAV wing to obtain the deformation inversion data of the UAV wing.

2. The deformation inversion method for an unmanned aerial vehicle (UAV) wing as described in claim 1, characterized in that, The static testing system includes a static testing bench, a fiber optic strain measurement system, and a fiber optic temperature sensor. The step of conducting a static loading test on the UAV wing using the static testing system and collecting the target strain data of the UAV wing includes: The drone wing was fixed using a static test bench, and a loading test was performed on the fixed drone wing. Data is collected from the UAV wing using the fiber optic strain measurement system and the fiber optic temperature sensor to obtain target strain data of the UAV wing during the loading test.

3. The deformation inversion method for an unmanned aerial vehicle (UAV) wing as described in claim 2, characterized in that, The fiber optic strain measurement system includes a fiber optic strain sensor and a fiber optic demodulator. The step of acquiring target strain data of the UAV wing during the loading test by using the fiber optic strain measurement system and the fiber optic temperature sensor includes: The initial strain data of the UAV wing during the loading test is obtained by the fiber Bragg grating strain sensor, and the initial strain data is transmitted to the fiber Bragg grating demodulator in the form of an optical signal. Temperature compensation is performed on the fiber optic strain sensor using the fiber optic temperature sensor to generate the temperature measurement value of the UAV wing and transmit it to the fiber optic demodulator. The initial strain data in optical signal form is converted into an electrical signal by the fiber optic demodulator, and the target strain data is obtained based on the initial strain data in electrical signal form and the temperature measurement value.

4. The deformation inversion method for an unmanned aerial vehicle (UAV) wing as described in claim 2, characterized in that, The loading test includes simulating uniformly distributed loads and concentrated loads. The steps of performing the loading test on the fixed UAV wing include: A uniformly distributed load is simulated on the UAV wing by uniformly loading and / or unloading the lower surface of the UAV wing. A concentrated load is applied to the drone wing by uniformly loading and / or unloading at the end position of the drone wing.

5. The deformation inversion method for an unmanned aerial vehicle (UAV) wing as described in claim 4, characterized in that, The deformation inversion data includes first inversion data and second inversion data. The step of performing deformation inversion on the UAV wing based on the target strain data using a target deformation inversion algorithm to obtain the deformation inversion data of the UAV wing includes: Acquire the first strain data of the UAV wing under concentrated load, and perform deformation inversion on the first strain data using a target deformation inversion algorithm to generate the first inversion data of the UAV wing under concentrated load; The second strain data of the UAV wing under simulated uniform load is obtained, and the first strain data is subjected to deformation inversion using a target deformation inversion algorithm to generate the second inversion data of the UAV wing under simulated uniform load.

6. The deformation inversion method for an unmanned aerial vehicle (UAV) wing as described in claim 1, characterized in that, After the step of conducting a static loading test on the UAV wing using a static testing system and collecting target strain data of the UAV wing, the method further includes: The third strain data of the UAV wing in the static loading test is obtained by a resistance strain gauge, and the target strain data and the third strain data are compared. If the error between the target strain data and the third strain data is less than a preset error threshold, then deformation inversion analysis is performed based on the target strain data.

7. The deformation inversion method for an unmanned aerial vehicle (UAV) wing as described in claim 1, characterized in that, The deformation inversion method for the UAV wing also includes: In the static loading test, the true value of the deformation measurement of the UAV wing is obtained through a displacement measurement system; The deformation inversion data and the true value of the deformation measurement are compared, and the deformation inversion result of the static test system for the UAV wing is determined based on the comparison result.

8. A deformation inversion device for an unmanned aerial vehicle (UAV) wing, characterized in that, The deformation inversion device for the UAV wing includes: The strain data acquisition module is used to conduct a static loading test on the UAV wing using a static test system and acquire the target strain data of the UAV wing. The deformation data calculation module is used to perform deformation inversion analysis on the UAV wing based on the target strain data using a target deformation inversion algorithm, and obtain the deformation inversion data of the UAV wing.

9. A deformation inversion device for an unmanned aerial vehicle (UAV) wing, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the deformation inversion method for an unmanned aerial vehicle wing as described in any one of claims 1 to 7.

10. 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, it implements the steps of the deformation inversion method for the UAV wing as described in any one of claims 1 to 7.

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