Optimization Method and System for Fiber Wrapping in Micro-Vibration Sensing for Unmanned Aerial Vehicles

Abstract

This application provides a method and system for optimizing fiber winding for micro-vibration sensing in unmanned aerial vehicles (UAVs). The method involves first acquiring micro-vibration strain data from the UAV's wing surface; then fusing the UAV's flight attitude parameters and performing bending loss compensation processing on the micro-vibration strain data to generate a compensated vibration signal; next, applying fiber grating wavelength demodulation processing to the compensated vibration signal to generate a wavelength variation sequence; then, performing empirical mode decomposition (EMD) on the wavelength variation sequence to obtain multi-scale vibration modes; and finally, determining the optimal fiber winding spacing on the UAV's wing surface based on the modal energy distribution. The technical solution provided in this application not only achieves precise radio frequency interference through protocol identification but also adaptively adjusts interference and guidance strategies based on the UAV's response behavior, enabling controllable, safe, and orderly removal of intruding UAVs.

Description

Technical Field

[0001] This application relates to the field of optical fiber winding optimization technology, and in particular to a method and system for optimizing the winding of micro-vibration sensing optical fibers for unmanned aerial vehicles. Background Technology

[0002] With the current trend of unmanned aerial vehicles (UAVs) developing towards high speed and long endurance, the problem of micro-vibration of the aircraft wing surface during complex airflow and maneuvering flight is becoming increasingly prominent. Although these micro-vibrations are small in amplitude, their long-term cumulative effect may lead to structural fatigue damage, directly affecting flight safety and service life. Therefore, achieving accurate and real-time monitoring of wing surface micro-vibrations, and optimizing sensor deployment to improve monitoring efficiency, has become a key technical requirement in this field.

[0003] In the prior art, there is a monitoring scheme based on fiber optic grating sensor network and combined with fixed threshold judgment. This scheme detects the wavelength drift signal caused by structural vibration by deploying fiber optic grating sensor network on the wing surface, and judges and records the vibration event by preset a uniform signal strength threshold, thereby realizing the assessment of the structural vibration state.

[0004] However, this existing solution still has obvious shortcomings. Because it relies on a fixed judgment threshold, it is difficult to effectively adapt to the dynamic changes in the energy and frequency distribution of vibration signals during the variable speed flight of unmanned aerial vehicles. This results in insensitivity to monitoring weak vibration signals or easy misjudgment under strong background noise. At the same time, this solution does not consider the coupling relationship between sensor deployment parameters (such as winding interval) and dynamically changing vibration modes, and cannot optimize the sensor network itself according to the actual vibration characteristics, thus limiting the overall performance of the monitoring system. Summary of the Invention

[0005] This application provides a method and system for optimizing fiber optic winding for micro-vibration sensing in unmanned aerial vehicles, which solves the problems in the prior art where the fixed threshold judgment method is difficult to adapt to the dynamic changes in vibration characteristics under variable speed flight, and the lack of coordinated optimization between sensor deployment parameters and vibration modes, resulting in insufficient monitoring accuracy and reliability.

[0006] In a first aspect, this application provides a method for optimizing the winding of micro-vibration sensing optical fibers for unmanned aerial vehicles, including:

[0007] Collect micro-vibration strain data of the wing surface of the unmanned aerial vehicle;

[0008] The flight attitude parameters of the aircraft are integrated to perform bending loss compensation processing on the micro-vibration strain data to generate a compensated vibration signal.

[0009] The compensated vibration signal is subjected to fiber grating wavelength demodulation processing to generate a wavelength change sequence;

[0010] The wavelength variation sequence is subjected to empirical mode decomposition to obtain multi-scale vibration modes;

[0011] Based on the modal energy distribution of the multi-scale vibration modes, the optimal winding interval of the optical fiber on the wing surface of the unmanned aerial vehicle is determined by fuzzy logic optimization.

[0012] Optionally, micro-vibration strain data of the unmanned aerial vehicle wing surface are collected, including:

[0013] By using a fiber optic grating sensor network pre-deployed at predetermined positions on the wing surface of the unmanned aerial vehicle (UAV) to continuously acquire the original optical signals caused by the micro-vibrations of the wing surface of the UAV when the UAV is in a variable speed flight state;

[0014] The original optical signal is converted into a corresponding strain reading to obtain micro-vibration strain data.

[0015] Optionally, the micro-vibration strain data is processed by fusing the flight attitude parameters of the aircraft to perform bending loss compensation processing, generating a compensated vibration signal, including:

[0016] The flight attitude parameters of the unmanned aerial vehicle during variable speed flight are obtained, including the angle of attack and the acceleration.

[0017] Establish a dynamic correspondence between the flight attitude parameters and the background strain components in the micro-vibration strain data caused by the bending deformation of the unmanned aerial vehicle wing surface;

[0018] Based on the dynamic correspondence, calculate the signal attenuation component that matches the flight attitude parameters;

[0019] The micro-vibration strain data is input into a preset signal compensation model, and the background strain component is canceled out by the signal attenuation component to generate a compensated vibration signal.

[0020] Optionally, fiber optic grating wavelength demodulation processing is applied to the compensated vibration signal to generate a wavelength variation sequence, including:

[0021] Receive the compensated vibration signal and obtain the reflection spectrum corresponding to the compensated vibration signal;

[0022] Identify the position of the characteristic wavelength in the reflection spectrum that corresponds to each sensing unit in the fiber Bragg grating sensing network;

[0023] Calculate the offset of the characteristic wavelength position as the unmanned aerial vehicle (UAV) continuously shifts due to the micro-vibration of the UAV's wing surface during variable speed flight.

[0024] The offsets of the characteristic wavelength positions corresponding to each sensing unit are arranged in time sequence to generate a wavelength change sequence.

[0025] Optionally, the wavelength variation sequence is subjected to empirical mode decomposition to obtain multi-scale vibration modes, including:

[0026] Identification step: Identify the local extrema points in the wavelength change sequence;

[0027] Construction steps: Based on the local extreme points, construct the upper and lower envelopes of the wavelength variation sequence;

[0028] Calculation generation steps: Calculate the mean of the upper envelope and the lower envelope to generate an envelope mean sequence;

[0029] Separation step: Separate the envelope mean sequence from the wavelength change sequence to obtain the first-order vibration component;

[0030] Separation step: Determine whether the first vibration component satisfies the determination condition of the intrinsic mode function. If the determination condition of the intrinsic mode function is met, then the first vibration component is taken as the first intrinsic mode function, and the first intrinsic mode function is separated from the wavelength change sequence to obtain the residual sequence.

[0031] The identification step, construction step, calculation generation step, separation step, and judgment separation step are repeatedly performed on the residual sequence to extract multiple intrinsic mode functions;

[0032] The extracted intrinsic mode functions are arranged in a preset order according to their corresponding average frequencies, and the arranged intrinsic mode functions are used as multi-scale vibration modes.

[0033] Optionally, based on the modal energy distribution of the multi-scale vibration modes, the optimal winding interval of the optical fiber on the wing surface of the unmanned aerial vehicle is determined through fuzzy logic optimization processing, including:

[0034] Calculate the modal energy corresponding to each eigenmode function in the multi-scale vibration modes;

[0035] Based on the modal energy, determine the dominant vibrational component in the multi-scale vibration mode and the energy level corresponding to the dominant vibrational component;

[0036] The energy level of the dominant vibration component is input into a preset fuzzy logic rule base, and the adjustment amount of the fiber winding interval is obtained through fuzzy reasoning.

[0037] The optimal winding interval is determined based on the adjustment amount of the fiber winding interval and the preset initial winding interval reference value.

[0038] Optionally, the energy level of the dominant vibration component is input into a preset fuzzy logic rule base, and the adjustment amount of the fiber winding interval is obtained through fuzzy inference, including:

[0039] The energy levels of the dominant vibrational components are divided into multiple fuzzy sets, and a corresponding membership function is assigned to each fuzzy set;

[0040] Based on the energy distribution ratio of the dominant vibration component at different scales, the corresponding conditional rules in the fuzzy logic rule base are activated.

[0041] Perform fuzzy inference operations on the activated conditional rules to obtain a fuzzy output set regarding the winding interval adjustment amount;

[0042] The fuzzy output set is defuzzified to convert the fuzzy amount into an adjustment amount for the fiber winding interval.

[0043] Secondly, this application provides a micro-vibration sensing fiber optic winding optimization system for unmanned aerial vehicles, comprising:

[0044] The data acquisition module is used to collect micro-vibration strain data of the unmanned aerial vehicle's wing surface;

[0045] The compensation processing module is used to fuse the flight attitude parameters of the aircraft to perform bending loss compensation processing on the micro-vibration strain data and generate a compensation vibration signal.

[0046] The demodulation processing module is used to apply fiber grating wavelength demodulation processing to the compensated vibration signal to generate a wavelength change sequence;

[0047] The decomposition processing module is used to perform empirical mode decomposition on the wavelength change sequence to obtain multi-scale vibration modes.

[0048] The determination module is used to determine the optimal winding interval of the optical fiber on the wing surface of the unmanned aerial vehicle based on the modal energy distribution of the multi-scale vibration modes through fuzzy logic optimization processing.

[0049] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are to be invoked and executed by the processing component to implement the micro-vibration sensing fiber winding optimization method for unmanned aerial vehicles as described in the first aspect above.

[0050] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements the micro-vibration sensing fiber winding optimization method for unmanned aerial vehicles as described in the first aspect.

[0051] This application achieves precise perception and utilization of the micro-vibration characteristics of unmanned aerial vehicle (UAV) wings by constructing a complete technology chain from data acquisition to parameter optimization. First, by acquiring raw strain data and integrating it with flight attitude for intelligent compensation, the measurement error introduced by the bending deformation of the airframe is effectively eliminated, providing a high-fidelity vibration signal for subsequent analysis. Then, through the coordinated processing of wavelength demodulation and empirical mode decomposition, the complex vibration signal is decomposed into multi-scale modes with clear physical meaning, thereby clearly revealing the vibration energy distribution characteristics of the wing surface in different frequency bands. Finally, based on these modal energy characteristics, the optimal fiber winding interval is intelligently deduced using fuzzy logic processing, enabling the sensor network deployment to adapt to the actual dynamic response characteristics of the wing surface, thereby improving the sensitivity and reliability of vibration monitoring.

[0052] Furthermore, by introducing a complete fuzzy reasoning mechanism, the abstract feature of vibration energy is transformed into specific and operable layout parameters. The energy levels of the dominant vibration components are fuzzily classified, and corresponding adjustment rules are intelligently activated based on their distribution ratios at different scales. This rule-based nonlinear mapping method can effectively handle the complex relationship between vibration energy and winding interval, which is difficult to describe with precise mathematical models. By reasoning and defuzzifying the activated rules, a clear optimal winding interval adjustment is finally output. This process realizes the automated and intelligent conversion from vibration signal characteristics to engineering layout parameters, avoiding the subjectivity and uncertainty brought about by relying on human experience, and ensuring the scientific nature and consistency of the optimization results.

[0053] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in 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, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 A flowchart of a method for optimizing fiber optic winding for micro-vibration sensing in unmanned aerial vehicles, provided in this application, is shown.

[0056] Figure 2 This paper presents a schematic diagram of a micro-vibration sensing fiber optic winding optimization system for unmanned aerial vehicles provided in this application.

[0057] Figure 3 A schematic diagram of the structure of a computing device provided in this application is shown. Detailed Implementation

[0058] To enable those skilled in the art to better understand the present application, the technical solution of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0059] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0060] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0061] Figure 1 This application provides a flowchart of a method for optimizing the winding of micro-vibration sensing optical fibers for unmanned aerial vehicles, as shown in the flowchart. Figure 1 As shown, the method includes:

[0062] Step 101: Collect micro-vibration strain data of the unmanned aerial vehicle wing surface.

[0063] Optionally, step 101 may specifically include the following steps:

[0064] Step 1011: When the unmanned aerial vehicle is in variable speed flight, the fiber optic grating sensor network pre-deployed at a predetermined position on the wing surface of the unmanned aerial vehicle continuously acquires the original light signal caused by the micro-vibration of the wing surface of the unmanned aerial vehicle.

[0065] Step 1012: Convert the original optical signal into a corresponding strain reading to obtain micro-vibration strain data.

[0066] In the above scheme, the unmanned aerial vehicle wing surface refers to the wing surface structure of the unmanned aerial vehicle that generates lift and is used to withstand the aerodynamic loads during flight. It is produced through aircraft manufacturing processes.

[0067] Micro-vibration strain data refers to quantitative data describing the degree of deformation of an airfoil structure caused by minute vibrations. It is used to analyze the dynamic mechanical properties of the airfoil and is obtained through fiber optic sensing signal conversion.

[0068] A fiber optic grating sensor network refers to a measurement array composed of multiple fiber optic grating sensors that are sensitive to deformation. It is used to monitor the vibration state of the wing surface of an unmanned aerial vehicle in real time. It is obtained by arranging fiber optic gratings on the wing surface of the unmanned aerial vehicle according to a preset topology.

[0069] Variable speed flight state refers to the stage in which an aircraft accelerates, decelerates, or changes its flight attitude. It is used to excite the typical vibration response of the wing surface and is obtained through the control of the flight control system.

[0070] The original optical signal refers to the reflected spectral signal generated by the vibration of the fiber optic grating surface, which is used to carry vibration deformation information and is acquired by a photodetector.

[0071] Strain readings refer to the physical quantity values ​​converted from optical signals, used to quantify vibration intensity, and obtained by processing the original optical signal through a signal demodulation algorithm.

[0072] In this scheme, firstly, through a signal sensing step, when the UAV is in a variable-speed flight state such as acceleration, turning, or climbing, the fiber optic grating sensing network, which is pre-attached tightly to key parts of the UAV's wing surface (such as the wing root and wingtip), is activated. The micro-vibration of the wing surface causes the fiber to undergo slight bending or stretching deformation, which in turn changes the optical properties of the grating written in the fiber. The sensing network continuously captures the changes in the characteristics of reflected or transmitted light caused by this deformation, thereby continuously acquiring a series of raw optical signals containing vibration information. Secondly, through a signal conversion step, the system inputs the acquired raw optical signals (usually changes in wavelength or light intensity) into a pre-calibrated signal processing unit. This signal processing unit stores the wavelength strain sensitivity coefficient of the fiber optic grating. By applying a calibration conversion algorithm, the physical change of the optical signal (such as the wavelength drift in nanometers) is converted into the corresponding strain reading in units of micro-strain. This series of strain readings arranged in chronological order constitutes the final micro-vibration strain data, providing quantitative vibration information for subsequent analysis.

[0073] For example, during a test flight of a medium-sized fixed-wing unmanned aerial vehicle (UAV), when the UAV accelerates and climbs in airspace A to enter a variable-speed flight state, its wing surface generates high-frequency micro-vibrations due to airflow disturbances. The fiber optic grating sensor network deployed on the wing's main spars and skin senses this minute deformation in real time, and the reflection center wavelength of its internal grating shifts accordingly. The network then continuously acquires a series of raw optical signals with wavelength changes. After receiving these raw optical signals, the ground station uses its built-in demodulator and calibration curve to quickly convert the wavelength shift into specific strain readings, thereby obtaining a set of micro-vibration strain data that accurately reflects the vibration intensity of the wing surface during the acceleration and climb phase.

[0074] This step directly acquires raw optical signals under critical flight conditions through a proprietary sensor network and converts them into precise strain data, providing a high-fidelity, quantified foundation of raw vibration information for the entire method. It ensures that the input data for all subsequent advanced processing can truly and accurately reflect the dynamic characteristics of the airfoil in the real working environment, avoiding errors that may be caused by using simulation or indirect data, and providing reliable data support for the final winding optimization.

[0075] Step 102: The micro-vibration strain data is processed by fusing the flight attitude parameters of the aircraft to perform bending loss compensation processing, thereby generating a compensated vibration signal.

[0076] Optionally, step 102 may specifically include the following steps:

[0077] Step 1021: Obtain the flight attitude parameters of the unmanned aerial vehicle during variable speed flight, the flight attitude parameters including the angle of attack and the acceleration.

[0078] Step 1022: Establish the dynamic correspondence between the flight attitude parameters and the background strain components in the micro-vibration strain data caused by the bending deformation of the unmanned aerial vehicle wing surface;

[0079] Step 1023: Calculate the signal attenuation component that matches the flight attitude parameters based on the dynamic correspondence.

[0080] Step 1024: Input the micro-vibration strain data into a preset signal compensation model, and use the signal attenuation component to cancel the background strain component to generate a compensated vibration signal.

[0081] In the above scheme, the fusion aircraft refers to a flight platform that collaboratively processes the aircraft's own state information and sensor measurement data to achieve comprehensive processing of multi-source information, which is obtained through system integration.

[0082] Flight attitude parameters are physical quantities that describe the spatial attitude and motion state of an aircraft. They are used to characterize the overall motion characteristics of the aircraft and are obtained through an inertial measurement unit.

[0083] Bending loss compensation refers to the correction processing for the attenuation of fiber optic sensing signals caused by macroscopic bending of the airfoil. It is used to eliminate measurement errors caused by non-vibration factors and is achieved through signal compensation algorithms.

[0084] Compensated vibration signal refers to the vibration signal after bending loss compensation processing, which is used to reflect the real micro-vibration information and is obtained through signal compensation model processing.

[0085] Angle of attack refers to the angle between the wing chord and the direction of incoming airflow. It is used to characterize the aerodynamic angle of attack of the wing surface and is measured by attitude sensors.

[0086] Flight acceleration refers to the rate of change of an aircraft's velocity, used to characterize the aircraft's maneuvering state, and is measured by an acceleration sensor;

[0087] Background strain components refer to the low-frequency strain components in micro-vibration strain data caused by macroscopic bending deformation of the airfoil. They are used to distinguish non-vibration interference signals and are obtained through signal analysis.

[0088] Dynamic correspondence refers to the real-time correlation model between flight attitude parameters and background strain components, which is used to establish the basis for compensation and is obtained through data fitting analysis.

[0089] The signal attenuation component refers to the amount of signal strength attenuation caused by the bending of the wing surface. It is used to quantify the degree of bending loss and is calculated through a corresponding relationship.

[0090] The preset signal compensation model refers to a pre-established mathematical model for signal correction, which is used to achieve bending loss compensation and is obtained through experimental calibration and theoretical modeling.

[0091] In this scheme, firstly, through the parameter acquisition step, the system calls the data reading algorithm of the inertial measurement unit (IMU) on the aircraft to acquire flight attitude parameters in real time. This data reading algorithm calculates the flight angle of attack, which characterizes the space attitude of the aircraft, and the flight acceleration, which reflects the change in motion state, by processing the raw data from the gyroscope and accelerometer. Secondly, through the relationship establishment step, the system uses a multiple linear regression algorithm to perform correlation analysis on the flight attitude parameters and micro-vibration strain data. This multiple linear regression algorithm establishes a mathematical relationship between attitude parameters and low-frequency components of strain data through least squares fitting, thereby constructing a dynamic correspondence model and identifying the background strain components caused by wing bending.

[0092] Next, through the attenuation calculation step, the system uses a parameter mapping algorithm to substitute the real-time flight attitude parameters into the model based on the regression coefficients in the established dynamic correspondence model, and outputs the corresponding signal attenuation component values. Finally, through the signal compensation step, the system uses an inverse filtering algorithm in digital signal processing to input the original micro-vibration strain data into a preset signal compensation model. This preset signal compensation model performs inverse gain compensation on the signal in the frequency domain based on the calculated signal attenuation component, effectively canceling the interference of background strain components, and finally generating a pure compensated vibration signal.

[0093] Following the specific implementation of the previous scheme, when the UAV is accelerating and climbing in airspace A, the system obtains in real time that the flight angle of attack is 8 degrees and the flight acceleration is 2 m / s². Through analysis, it is found that the wing surface bending corresponding to this flight state will generate a background strain component of about 15 micro-strain. The system calculates the current signal attenuation component according to the established dynamic correspondence, and processes the original strain data using a preset signal compensation model, successfully eliminating bending interference and obtaining a compensated vibration signal that truly reflects the micro-vibration of the wing surface.

[0094] This step effectively identifies and eliminates measurement errors introduced by macroscopic bending deformation of the wing surface by fusing flight attitude information, significantly improving the signal-to-noise ratio of micro-vibration signals and providing a high-quality vibration data foundation for subsequent accurate analysis. This compensation process ensures the accuracy of vibration feature extraction and avoids interference from non-vibration factors on the analysis results.

[0095] Step 103: Apply fiber grating wavelength demodulation processing to the compensated vibration signal to generate a wavelength change sequence.

[0096] Optionally, step 103 may specifically include the following steps:

[0097] Step 1031: Receive the compensated vibration signal and obtain the reflection spectrum corresponding to the compensated vibration signal;

[0098] Step 1032: Identify the position of the characteristic wavelength in the reflection spectrum corresponding to each sensing unit in the fiber grating sensing network;

[0099] Step 1033: Calculate the offset of the characteristic wavelength position as a result of the continuous shift caused by the micro-vibration of the wing surface of the unmanned aerial vehicle during the variable speed flight state.

[0100] Step 1034: Arrange the offset of the characteristic wavelength position corresponding to each sensing unit in time sequence to generate a wavelength change sequence.

[0101] In the above scheme, the fiber grating wavelength refers to the optical characteristic parameter of the fiber grating reflecting a specific wavelength, which is used to characterize the physical state of the grating and is determined by the periodic structure of the grating.

[0102] Wavelength change sequence refers to a dataset of characteristic wavelength offsets arranged in chronological order, used to record the dynamic wavelength change process caused by vibration, and obtained through demodulation processing;

[0103] The reflection spectrum refers to the spectrum of the intensity of the light signal reflected back by a fiber optic grating as a function of wavelength. It is used to analyze the wavelength characteristics of the grating and is obtained by measuring with a spectrometer.

[0104] Fiber Bragg grating sensor networks refer to distributed measurement systems composed of multiple fiber Bragg grating sensors, used to achieve simultaneous measurement at multiple points in space, and obtained through network deployment;

[0105] A sensing unit refers to a single fiber Bragg grating sensor in a fiber Bragg grating sensing network, used to sense changes in physical quantities at a specific location, and is achieved through a single grating element.

[0106] The characteristic wavelength position refers to the center reflection wavelength value corresponding to each fiber grating in the reflection spectrum, which is used to identify each sensing unit and is obtained by spectral peak detection.

[0107] The offset of continuous offset refers to the dynamic change of the characteristic wavelength position relative to the initial value. It is used to quantify the wavelength drift caused by vibration and is calculated through real-time wavelength tracking.

[0108] In this scheme, firstly, through the spectral acquisition step, the system uses optical spectrum analysis technology to process the compensated vibration signal. The vibration signal is split using a spectrometer to obtain reflection spectrum data containing the reflection characteristics of each fiber grating. This reflection spectrum reflects the intensity distribution of different wavelength components. Secondly, through the wavelength identification step, the system uses a peak detection algorithm to analyze the reflection spectrum. This peak detection algorithm accurately locates the characteristic wavelength position corresponding to each sensing unit by finding local maxima in the spectrum and establishes the correspondence between the characteristic wavelength and the sensing unit.

[0109] Next, through the offset calculation step, the system adopts a real-time wavelength tracking algorithm to continuously monitor the position of each characteristic wavelength. This real-time wavelength tracking algorithm calculates the offset amount caused by the continuous offset due to the micro-vibration of the airfoil by comparing the difference between the current wavelength value and the initial reference value, and records the offset value at each time point. Finally, through the sequence generation step, the system uses a time-series data integration method to arrange and organize the wavelength offsets of each sensing unit in chronological order to construct a complete wavelength change sequence. This wavelength change sequence accurately reflects the dynamic response of each measurement point during the vibration process.

[0110] Following the specific implementation of the previous scheme, after the system obtains the compensated vibration signal, it uses a high-resolution spectrometer to acquire the corresponding reflection spectrum. Through analysis, it was found that the characteristic wavelength position of the No. 3 sensor unit deployed at the wing root fluctuated from the initial 1540.250nm. During the 2-second observation period, the characteristic wavelength continuously changed within the range of 1540.248nm to 1540.253nm, with a continuous offset amount reaching 5pm. The system sorted the offset data of all 16 sensor units according to the millisecond-level timestamps, generating a complete wavelength change sequence, which provided detailed vibration time series data for subsequent analysis.

[0111] This step, through precise spectral analysis and wavelength demodulation, converts the compensated vibration signal into a high-precision wavelength change sequence, achieving a reliable conversion of vibration information from the optical domain to the time-series data domain. This processing effectively extracts the dynamic response characteristics of each sensing point, providing an accurate and complete input data foundation for subsequent modal analysis and optimization decisions, ensuring the measurement accuracy and reliability of the entire monitoring system.

[0112] Step 104: Perform empirical mode decomposition on the wavelength variation sequence to obtain multi-scale vibration modes.

[0113] Optionally, step 104 may specifically include the following steps:

[0114] Step 1041, Identification Step: Identify local extreme points in the wavelength change sequence;

[0115] Step 1042, Construction Step: Based on the local extreme points, construct the upper envelope and lower envelope of the wavelength change sequence;

[0116] Step 1043, Calculation and generation steps: Calculate the mean of the upper envelope and the lower envelope to generate an envelope mean sequence;

[0117] Step 1044, Separation step: Separate the envelope mean sequence from the wavelength change sequence to obtain the first-order vibration component;

[0118] Step 1045, Separation determination step: Determine whether the first vibration component satisfies the determination condition of the intrinsic mode function. If the determination condition of the intrinsic mode function is satisfied, then the first vibration component is taken as the first intrinsic mode function, and the first intrinsic mode function is separated from the wavelength change sequence to obtain the residual sequence.

[0119] Step 1046: Repeat the identification step, construction step, calculation generation step, separation step, and judgment separation step on the residual sequence to extract multiple intrinsic mode functions;

[0120] Step 1047: Arrange the extracted multiple intrinsic mode functions in a preset order according to their corresponding average frequencies, and use the arranged multiple intrinsic mode functions as multi-scale vibration modes.

[0121] In the above scheme, empirical mode decomposition refers to an adaptive signal decomposition method used to decompose complex signals into different frequency components through an iterative selection process;

[0122] Multi-scale vibration modes refer to the vibrational characteristic components of a signal at different time scales, used to characterize the multi-frequency band characteristics of vibration, and are obtained through mode decomposition.

[0123] Local extrema refer to the set of local highest and lowest points in a signal waveform, used to determine the fluctuation characteristics of the signal, and identified by extremum detection algorithms;

[0124] The upper envelope is a smooth curve that connects all local maxima of a signal. It is used to describe the upper boundary of the signal and is constructed using an interpolation algorithm.

[0125] The lower envelope is a smooth curve that connects all local minima of a signal. It is used to describe the lower boundary of the signal and is constructed using an interpolation algorithm.

[0126] The mean refers to the value obtained by taking the arithmetic average of the upper and lower envelopes at each point, and is used to represent the local average trend of the signal.

[0127] The envelope mean sequence is a sequence composed of the mean points of the upper and lower envelope lines. It is used to extract the trend component of a signal and is obtained by calculating the mean of the envelope lines.

[0128] The first-order vibrational component refers to the first vibrational component separated from the original signal, which is used to represent the main wave characteristics of the signal and is obtained by separating the envelope mean.

[0129] Intrinsic mode functions refer to vibration mode functions that satisfy the single-component condition. They are used to characterize the inherent vibration modes of a signal and are obtained by judging whether the vibration components satisfy the condition.

[0130] The judgment criteria refer to the criteria for determining whether a vibration component is an eigenmode function, which is used to ensure the rationality of the decomposition results. This is achieved by checking the relationship between zero crossings and extreme points.

[0131] The first intrinsic mode function refers to the first vibration component that meets the conditions, which is used to represent the highest frequency vibration mode and is obtained by judgment and separation;

[0132] The residual sequence refers to the sequence remaining after separating the intrinsic mode functions, which is used to further decompose the low-frequency components. It is obtained by subtracting the intrinsic mode functions from the original signal.

[0133] The average frequency refers to the dominant frequency characteristic of the intrinsic mode function, which is used to measure the scale characteristics of vibration modes and is calculated through spectrum analysis.

[0134] The preset order refers to the arrangement rule based on frequency from high to low or from low to high, which is used to organize multi-scale modes and can be set according to analysis requirements.

[0135] In this scheme, firstly, through the extreme value identification step, the system uses an extreme value detection algorithm to analyze the wavelength change sequence and find all local extreme points in the wavelength change sequence, including local maxima and local minima. Secondly, through the envelope construction step, the system uses a cubic spline interpolation algorithm to connect all local maxima to form an upper envelope and connect all local minima to form a lower envelope. These two envelopes completely enclose the original signal. Next, through the mean calculation step, the system uses the arithmetic mean method to calculate the mean of the upper and lower envelopes at each time point, generating an envelope mean sequence that describes the local average trend of the signal.

[0136] Then, through the component separation step, the system uses signal subtraction to subtract the envelope mean sequence from the original wavelength change sequence to obtain the first-order vibration component containing the main vibration information. Next, through the mode judgment step, the system applies the intrinsic mode function judgment criterion to check the relationship between the number of extreme points and zero crossings of the first-order vibration component. If the condition is met, it is taken as the first intrinsic mode function, and the component is separated from the original sequence through signal subtraction to obtain the residual sequence. Finally, through the iterative decomposition step, the system repeats all the above steps on the residual sequence to gradually extract multiple intrinsic mode functions, and arranges them in a preset order from high to low average frequency of each mode to form a multi-scale vibration mode set.

[0137] Following the specific implementation of the previous scheme, the wavelength change sequence containing wavelength change data of 16 sensing units is processed. The system first identifies the local extreme points in the wavelength change sequence, and then obtains the upper and lower envelopes through interpolation. After calculating the envelope mean, an envelope mean sequence is generated, from which the first-order vibration component is separated. After checking that the first-order vibration component meets the determination condition of the intrinsic mode function, it is confirmed as the first intrinsic mode function. After separating the first-order vibration component from the original sequence, the residual sequence is obtained, and further decomposed to obtain 6 modes of different frequencies. Arranged in descending order of average frequency, the multi-scale vibration modes reflecting various vibration characteristics of the airfoil are finally obtained.

[0138] This step effectively decomposes complex wavelength-varying signals into vibration modes at different time scales using an adaptive empirical mode decomposition method, clearly revealing the intrinsic frequency structure of the vibration signal. This process separates the originally mixed vibration information into multiple vibration components with clear physical meaning, providing a reliable input basis for subsequent modal energy analysis and optimization decisions, and improving the accuracy and interpretability of vibration feature extraction.

[0139] Step 105: Based on the modal energy distribution of the multi-scale vibration modes, determine the optimal winding interval of the optical fiber on the wing surface of the unmanned aerial vehicle through fuzzy logic optimization processing.

[0140] Optionally, step 105 may specifically include the following steps:

[0141] Step 1051: Calculate the modal energy corresponding to each eigenmode function in the multi-scale vibration modes;

[0142] Step 1052: Based on the modal energy, determine the dominant vibration component in the multi-scale vibration mode and the energy level corresponding to the dominant vibration component;

[0143] Step 1053: Input the energy level of the dominant vibration component into a preset fuzzy logic rule base, and obtain the adjustment amount of the fiber winding interval through fuzzy reasoning;

[0144] Step 1053 may specifically include the following steps:

[0145] The energy levels of the dominant vibration component are divided into multiple fuzzy sets, and a corresponding membership function is assigned to each fuzzy set. Based on the energy distribution ratio of the dominant vibration component at different scales, the corresponding conditional rules in the fuzzy logic rule base are activated. Fuzzy inference operations are performed on the activated conditional rules to obtain a fuzzy output set regarding the winding interval adjustment. The fuzzy output set is defuzzified to convert the fuzzy amount into the adjustment amount of the fiber winding interval.

[0146] Step 1054: Determine the optimal winding interval based on the adjustment amount of the optical fiber winding interval and the preset initial winding interval reference value.

[0147] In the above scheme, modal energy distribution refers to the proportional relationship of vibration energy contained in each intrinsic mode function in a multi-scale vibration mode, which is used to characterize the difference in vibration intensity of different frequency components and is obtained by calculating the energy value of each scale vibration mode.

[0148] Fuzzy logic is a reasoning method for dealing with uncertainty and fuzzy information. It is used to handle the nonlinear relationship between modal energy and winding interval, and is achieved through fuzzy rule reasoning.

[0149] The optimal winding interval refers to the fiber optic spacing that makes the sensing system most sensitive to the vibration response of the wing surface. It is used to optimize the performance of the sensing network and is obtained through fuzzy logic optimization.

[0150] Modal energy refers to the energy level of the vibrational component represented by each intrinsic mode function. It is used to quantify the vibration intensity of the mode and is calculated by integrating the square of the modal function.

[0151] The dominant vibration component refers to the vibration mode with the highest energy in the modal energy distribution. It is used to identify the frequency component that contributes the most to the airfoil vibration and is determined by comparing the energy values ​​of each mode.

[0152] Energy level refers to the energy magnitude of the dominant vibrational component, used to describe the intensity level of the dominant vibration, and is obtained by mapping energy values ​​to predefined levels;

[0153] The pre-set fuzzy logic rule base refers to the set of rules that store the mapping relationship between energy level and winding interval adjustment amount, which is used to realize fuzzy reasoning and is pre-established through expert experience and experimental data;

[0154] The adjustment amount of the fiber winding interval refers to the spacing value that needs to be increased or decreased based on the initial winding interval. It is used to optimize the layout parameters and is obtained through fuzzy inference.

[0155] Multiple fuzzy sets refer to categories that fuzzily divide energy levels, such as "low", "medium", and "high", and are used for input processing in fuzzy inference. They are implemented by defining membership functions.

[0156] The membership function is a function that describes the degree to which an element belongs to a certain fuzzy set. It is used to quantify fuzzy relationships and is defined by choosing an appropriate function form.

[0157] The energy distribution ratio refers to the proportion of the energy of the dominant vibrational component to the total modal energy. It is used to measure the significance of the dominant component and is calculated through the energy ratio.

[0158] Conditional rules refer to the "if then" rules in fuzzy reasoning, which are used to describe the fuzzy relationship between input and output and are predefined through a rule base;

[0159] Fuzzy inference refers to the process of deriving an output fuzzy set based on an input fuzzy set and conditional rules. It is used to implement fuzzy mapping and is achieved through fuzzy logic algorithms.

[0160] The fuzzy output set refers to the fuzzy result about the adjustment amount obtained through inference, which is used to represent the possible adjustment range and is obtained through fuzzy inference operations.

[0161] Defuzzification refers to the process of converting a fuzzy output set into precise numerical values ​​to obtain specific adjustment amounts, which is achieved through defuzzification methods such as the centroid method.

[0162] The preset initial winding interval reference value refers to the initial spacing setting value of the optical fiber layout, which serves as the basis for adjustment and is determined through structural design and preliminary experiments.

[0163] In this scheme, firstly, through the energy calculation step, the system uses a numerical integration method to perform square integral operations on each intrinsic mode function in the multi-scale vibration modes, calculating the modal energy value corresponding to each intrinsic mode function, and obtaining the complete modal energy distribution. Secondly, through the dominant component identification step, the system uses a maximum value search algorithm to compare the energy magnitude of all modes, determine the dominant vibration component with the largest energy, and map its energy value to a predefined energy level. Next, through the fuzzy inference step, the system inputs the energy level of the dominant vibration component into a preset fuzzy logic rule base. First, through fuzzy partitioning, the energy level is divided into multiple fuzzy sets, and a membership function is assigned to each fuzzy set. Then, the corresponding conditional rules are activated according to the energy distribution ratio. Next, fuzzy inference operations are performed to obtain a fuzzy output set. Finally, through defuzzification, the fuzzy output is converted into a precise adjustment amount for the fiber winding interval. Finally, through the interval determination step, the system uses arithmetic operations to add the adjustment amount of the fiber winding interval to a preset initial winding interval reference value to calculate the optimal winding interval.

[0164] Following the specific implementation of the previous scheme, modal energy calculations were performed on the six multi-scale vibration modes obtained from the decomposition. It was found that the third multi-scale vibration mode had the highest energy level and was identified as the dominant vibration component. The energy distribution ratio of this dominant vibration component (accounting for 65% of the total energy) was input into the preset fuzzy logic rule base, and the adjustment amount that needs to be reduced by 15% was obtained through fuzzy inference calculation. Combined with the preset initial winding interval reference value of 20mm, the optimal winding interval was finally determined to be 17mm.

[0165] This step analyzes the energy characteristics of vibration modes and uses fuzzy logic to handle the complex relationship between energy and layout parameters, thereby achieving intelligent optimization of fiber optic winding spacing. This method can adaptively determine the optimal layout scheme based on actual vibration characteristics, improving the sensing system's sensitivity to wing vibration and ensuring the effectiveness and reliability of vibration signal acquisition.

[0166] Figure 2 This application provides a schematic diagram of the structure of a micro-vibration sensing fiber optic winding optimization system for unmanned aerial vehicles, as shown below. Figure 2 As shown, the system includes:

[0167] The acquisition module 21 is used to acquire micro-vibration strain data of the wing surface of the unmanned aerial vehicle;

[0168] The compensation processing module 22 is used to fuse the flight attitude parameters of the aircraft to perform bending loss compensation processing on the micro-vibration strain data and generate a compensation vibration signal;

[0169] Demodulation processing module 23 is used to apply fiber grating wavelength demodulation processing to the compensated vibration signal to generate a wavelength change sequence;

[0170] Decomposition processing module 24 is used to perform empirical mode decomposition processing on the wavelength change sequence to obtain multi-scale vibration modes;

[0171] The determination module 25 is used to determine the optimal winding interval of the optical fiber on the wing surface of the unmanned aerial vehicle based on the modal energy distribution of the multi-scale vibration modes through fuzzy logic optimization processing.

[0172] Figure 2 The aforementioned micro-vibration sensing fiber optic winding optimization system for unmanned aerial vehicles can perform... Figure 1 The implementation principle and technical effects of the fiber optic winding optimization method for micro-vibration sensing in unmanned aerial vehicles (UAVs) described in the above embodiments will not be repeated here. The specific operation methods of each module and unit in the fiber optic winding optimization system for micro-vibration sensing in UAVs described in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0173] In one possible design, Figure 2 The micro-vibration sensing fiber optic winding optimization system for unmanned aerial vehicles shown in the embodiment can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0174] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0175] The processing component 32 is used for the above Figure 1 The embodiment describes a method for optimizing the winding of micro-vibration sensing optical fibers for unmanned aerial vehicles.

[0176] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0177] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0178] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0179] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0180] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0181] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0182] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown is a method for optimizing the winding of micro-vibration sensing optical fiber for unmanned aerial vehicles.

[0183] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0184] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0185] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0186] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for micro-vibration sensing fiber winding optimization for unmanned aerial vehicles, characterized by, Comprising: Collecting micro-vibration strain data of the wing surface of an unmanned aerial vehicle; Performing bending loss compensation processing on the micro-vibration strain data by fusing the flight attitude parameters of the aircraft to generate a compensated vibration signal, including: obtaining the flight attitude parameters of the unmanned aerial vehicle during variable-speed flight, the flight attitude parameters including flight attack angle and flight acceleration; establishing a dynamic correspondence relationship between the flight attitude parameters and the background strain component caused by the wing surface bending deformation in the micro-vibration strain data; calculating a signal attenuation component matching the flight attitude parameters according to the dynamic correspondence relationship; inputting the micro-vibration strain data into a preset signal compensation model, and using the signal attenuation component to cancel the background strain component to generate a compensated vibration signal; Performing fiber Bragg grating wavelength demodulation processing on the compensated vibration signal to generate a wavelength change sequence, including: receiving the compensated vibration signal and obtaining a reflection spectrum corresponding to the compensated vibration signal; identifying the characteristic wavelength positions corresponding to each sensing unit in the fiber Bragg grating sensing network in the reflection spectrum; calculating the offset of the continuous offset of the characteristic wavelength position with respect to the micro-vibration of the wing surface of the unmanned aerial vehicle during variable-speed flight; arranging the offsets of the characteristic wavelength positions corresponding to each sensing unit in chronological order to generate a wavelength change sequence; Performing empirical mode decomposition processing on the wavelength change sequence to obtain multi-scale vibration modes; Based on the modal energy distribution of the multi-scale vibration modes, determining the optimal winding interval of the optical fiber on the wing surface of the unmanned aerial vehicle through fuzzy logic optimization processing.

2. The method of claim 1, wherein, Collecting micro-vibration strain data of the wing surface of an unmanned aerial vehicle, including: Continuously obtaining the original optical signal caused by the micro-vibration of the wing surface of the unmanned aerial vehicle when the unmanned aerial vehicle is in a variable-speed flight state through a fiber Bragg grating sensing network pre-deployed at a predetermined position on the wing surface of the unmanned aerial vehicle; Converting the original optical signal into a corresponding strain reading to obtain micro-vibration strain data.

3. The method of claim 1, wherein, Performing empirical mode decomposition processing on the wavelength change sequence to obtain multi-scale vibration modes, including: Identification step: Identifying local extreme points in the wavelength change sequence; Construction step: Constructing an upper envelope and a lower envelope of the wavelength change sequence based on the local extreme points; Calculation and generation step: Calculating the mean value of the upper envelope and the lower envelope to generate an envelope mean sequence; Separation step: Separating the envelope mean sequence from the wavelength change sequence to obtain a first-order vibration component; Judgment and separation step: Judging whether the first-order vibration component meets the determination conditions of the intrinsic mode function. When the determination conditions of the intrinsic mode function are met, taking the first-order vibration component as the first intrinsic mode function and separating the first intrinsic mode function from the wavelength change sequence to obtain a residual sequence; Repeatedly executing the identification step, the construction step, the calculation and generation step, the separation step, and the judgment and separation step on the residual sequence to extract multiple intrinsic mode functions; Arrange the extracted multiple intrinsic mode functions in a preset order according to their corresponding average frequencies, and use the arranged multiple intrinsic mode functions as multi-scale vibration modes.

4. The method of claim 1, wherein, Based on the modal energy distribution of the multi-scale vibration modes, determine the optimal winding interval of the optical fiber on the wing surface of the unmanned aerial vehicle through fuzzy logic optimization processing, including: Calculate the modal energy corresponding to each intrinsic mode function in the multi-scale vibration modes; According to the modal energy, determine the dominant vibration component in the multi-scale vibration modes and the energy level corresponding to the dominant vibration component; Input the energy level of the dominant vibration component into a preset fuzzy logic rule base, and obtain the adjustment amount of the optical fiber winding interval through fuzzy inference; Based on the adjustment amount of the optical fiber winding interval and a preset initial winding interval reference value, determine the optimal winding interval.

5. The method of claim 4, wherein, Input the energy level of the dominant vibration component into a preset fuzzy logic rule base, and obtain the adjustment amount of the optical fiber winding interval through fuzzy inference, including: Divide the energy level of the dominant vibration component into multiple fuzzy sets, and assign corresponding membership functions to each fuzzy set; According to the energy distribution ratio of the dominant vibration component at different scales, activate the corresponding conditional rules in the fuzzy logic rule base; Perform fuzzy inference operations on the activated conditional rules to obtain a fuzzy output set regarding the adjustment amount of the winding interval; Perform defuzzification processing on the fuzzy output set to convert the fuzzy quantity into the adjustment amount of the optical fiber winding interval.

6. A micro-vibration sensing fiber winding optimization system for unmanned aerial vehicles, applied to the micro-vibration sensing fiber winding optimization method for unmanned aerial vehicles in any one of claims 1-5, characterized in that, Including: An acquisition module for acquiring the micro-vibration strain data of the wing surface of the unmanned aerial vehicle; A compensation processing module for performing bending loss compensation processing on the micro-vibration strain data by integrating the flight attitude parameters of the aircraft to generate a compensated vibration signal; A demodulation processing module for performing fiber grating wavelength demodulation processing on the compensated vibration signal to generate a wavelength change sequence; A decomposition processing module for performing empirical mode decomposition processing on the wavelength change sequence to obtain multi-scale vibration modes; A determination module for determining the optimal winding interval of the optical fiber on the wing surface of the unmanned aerial vehicle through fuzzy logic optimization processing based on the modal energy distribution of the multi-scale vibration modes.

7. A computing device, comprising: Including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for optimizing the winding of a micro-vibration sensing optical fiber for an unmanned aerial vehicle as described in any one of claims 1 to 5.

8. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, it implements a method for optimizing the winding of a micro-vibration sensing optical fiber for an unmanned aerial vehicle as described in any one of claims 1 to 5.

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