Real-time feedback target drone dynamic stress calibration method and system

Abstract

The invention relates to the technical field of stress calibration, in particular to a real-time feedback target drone dynamic stress calibration method and system, and the method comprises the steps: 1, dividing a dynamic stress sensitive region based on a target drone three-dimensional dynamic model; 2, original stress signals output by the multi-axis stress sensor array are synchronously collected, and preprocessed stress signals are generated; 3, inputting the preprocessed stress signal into a dynamic stress compensation model; and 4, in a target aircraft controlled loading test, comparing calibrated stress data with measurement data of reference measurement equipment, reversely correcting neural network weight parameters of the dynamic stress compensation model according to a comparison difference value, updating a weight distribution strategy of a weighted least square method, and completing dynamic iteration of calibration parameters. Through combination of the multi-axis stress sensor array and the dynamic stress compensation model, accurate measurement and compensation of the dynamic stress of the target drone are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of stress calibration, and particularly to a dynamic stress calibration method and system for a target aircraft with real-time feedback. Background Art

[0002] In the prior art, the arrangement of sensors is usually based on experience or simple geometric distribution, without fully considering the dynamic stress transmission path and stress concentration area of the target aircraft. This arrangement method may lead to insufficient stress measurement accuracy in key areas, while there are too many sensors arranged in low-stress areas, resulting in waste of resources; In addition, in the prior art, the stress signal acquisition system of the target aircraft usually adopts a fixed sampling frequency and cannot dynamically adjust according to the real-time motion state of the target aircraft.

[0003] In summary, there are many deficiencies in the prior art in the field of dynamic stress calibration. There is an urgent need for a calibration method and system that can provide real-time feedback and dynamically optimize to improve the structural safety and measurement accuracy of the target aircraft. Summary of the Invention

[0004] Based on the above purposes, the present invention provides a dynamic stress calibration method and system for a target aircraft with real-time feedback. The method includes the following steps: Step 1: Divide the dynamic stress sensitive area based on the three-dimensional dynamic model of the target aircraft, and arrange a multi-axis stress sensor array in the dynamic stress sensitive area according to a preset rule. The preset rule includes a method for determining the sensor spacing and a method for adjusting the density of the stress concentration area; Step 2: Synchronously collect the original stress signals output by the multi-axis stress sensor array, dynamically adjust the signal sampling frequency according to the real-time motion acceleration value of the target aircraft, and perform adaptive filtering processing on the original stress signals based on a noise feature library to generate preprocessed stress signals; Step 3: Input the preprocessed stress signals into a dynamic stress compensation model. The dynamic stress compensation model takes the target aircraft motion acceleration value and the target aircraft angular velocity value as input parameters, predicts the theoretical stress values at the positions of each sensor through a pre-trained neural network, calculates the dynamic error distribution matrix of the measured stress values and the theoretical stress values, iteratively optimizes the compensation coefficients by using the weighted least squares method, and triggers a closed-loop feedback mechanism according to the change rate of the compensation coefficients; Step 4: In the controlled loading test of the target aircraft, compare the calibrated stress data with the measurement data of the reference measurement device, reversely correct the neural network weight parameters of the dynamic stress compensation model according to the comparison difference value, and update the weight distribution strategy of the weighted least squares method to complete the dynamic iteration of the calibration parameters.

[0005] Preferably, the method for determining the sensor spacing in Step 1 includes: a. Collect stress attenuation data on the main stress transmission path through the target aircraft's unloaded motion test, and calculate the stress attenuation gradient at different positions. b. Determine the ratio of the equal ratio spacing according to the stress attenuation gradient, so that the adjacent sensor spacing increases according to the ratio as the stress attenuation gradient increases. The density adjustment method of the stress concentration area includes: a. Conduct finite element simulation on the connection and cross-section mutation areas of the target aircraft, and extract the maximum stress gradient value and average stress gradient value of the areas. b. Take the ratio of the maximum stress gradient value to the average stress gradient value as the sensor density increase multiple, and arrange redundant sensors in the area according to the increase multiple.

[0006] Preferably, the process of dynamically adjusting the signal sampling frequency in step 2 includes: Obtain the critical instability acceleration value corresponding to the maximum design speed of the target aircraft through the target aircraft's power calibration test, and take the preset percentage of the critical instability acceleration value as the acceleration threshold. When the real-time motion acceleration value exceeds the acceleration threshold, adopt the high-frequency sampling mode, and the frequency of the high-frequency sampling is an integer multiple of the target aircraft's structural resonance frequency, and the value of the integer multiple is determined by analyzing the relationship between the resonance frequency and the signal aliasing effect. When the real-time motion acceleration value is lower than or equal to the acceleration threshold, adopt the low-frequency sampling mode, and the frequency of the low-frequency sampling is the frequency division value of the high-frequency sampling frequency, and the frequency division value is calculated according to the reciprocal of the product of the target aircraft's current motion speed value and the structural resonance frequency.

[0007] Preferably, the adaptive filtering process in step 2 includes: Collect the baseline noise signals at the positions of each sensor in the unloaded and stationary state of the target aircraft, perform fast Fourier transform on the baseline noise signals, extract the noise spectrum characteristics at the positions of each sensor, and establish the noise characteristic library. Match the corresponding noise spectrum characteristics from the noise characteristic library according to the current sensor position, and select the filter type with the highest correlation with the noise spectrum characteristics in the filter bank. Perform time-frequency analysis on the original stress signal by using the sliding window method. The length of the sliding window is dynamically adjusted according to the autocorrelation function decay time of the target aircraft's current motion speed value. When the energy ratio of the signal in the preset frequency band exceeds the first threshold, it is determined as an effective stress component, otherwise it is determined as environmental vibration noise and filtered out.

[0008] Preferably, the establishment process of the dynamic stress compensation model in step 3 includes: The training data of the pre-trained neural network is obtained through the calibration test of the target aircraft. The calibration test includes: pasting reference strain gauges on the surface of the target aircraft, and synchronously recording the motion acceleration value, angular velocity value, temperature value of the target aircraft and the strain gauge measurement values at the positions of each sensor under various preset motion modes; The calculation process of the dynamic error distribution matrix includes: taking the difference between the measured stress value and the theoretical stress value at the position of each sensor as the initial error value, and performing spatial interpolation according to the stress influence factor at the position of each sensor, where the stress influence factor is obtained through finite element simulation calculation; The weight assignment strategy of the weighted least squares method includes: statistically calculating the standard deviation of the historical error data at the position of each sensor, and assigning a small weight value to the sensor positions where the standard deviation exceeds the second threshold.

[0009] Preferably, the specific process of iteratively optimizing the compensation coefficient in step 3 includes: Performing dynamic smoothing processing on the compensation coefficient by using the exponentially weighted moving average method, and adjusting the smoothing coefficient of the exponential weighting according to the mutation detection result of the motion state of the target aircraft, including: When it is detected that the change rate of the motion acceleration exceeds the third threshold, reducing the smoothing coefficient to accelerate the response speed, and when the motion state is stable, increasing the smoothing coefficient to improve the anti-noise ability; The triggering condition of the closed-loop feedback mechanism includes: the absolute value of the change rate of the compensation coefficient is less than the fourth threshold in N consecutive iterations, and the values of N and the fourth threshold are determined by statistically calculating the number of stress fluctuations and amplitudes within the typical motion period of the target aircraft.

[0010] Preferably, the process of reversely correcting the neural network weight parameters in step 4 includes: The calculation method of the comparison difference value is: calculating the root mean square error between the calibrated stress data and the data of the reference measurement device at the same time stamp; The correction amount of the neural network weight parameters is determined according to the non-linear function of the root mean square error, and the slope value of the non-linear function is dynamically adjusted by analyzing the relationship between the error convergence speed and stability in the historical correction process; The update process of the weight assignment strategy includes: for sensor groups with spatial correlation, synchronously adjusting their weight values according to the error consistency index of the sensors within the group, where the spatial correlation is obtained by clustering and analyzing the historical error distribution pattern.

[0011] Preferably, the specific method for detecting the mutation of the motion state includes: Calculating the difference sequence of the motion acceleration value of the target aircraft in real time, and when the number of times that the absolute value of the difference sequence continuously exceeds the fifth threshold reaches the preset value, it is determined that the motion state has mutated; The method for determining the fifth threshold is as follows: statistically analyze the standard deviation of the acceleration difference sequence in the historical motion data of the target aircraft, take three times the standard deviation as the initial value of the fifth threshold, and dynamically scale it according to the real-time motion mode.

[0012] Preferably, during the execution of steps 1 to 4: The calculation results of all dynamic adjustment parameters are stored in the parameter update queue, and the parameter update queue adopts a double-buffer mechanism to ensure data consistency during the parameter update process; The update mechanism of the key determination threshold includes: regularly collecting the operation data of the target aircraft, calculating the statistical distribution characteristics of the operation data, and triggering the threshold recalibration process when the distribution characteristics deviate from the initial calibration value by more than the sixth threshold.

[0013] Correspondingly, the embodiment of the present invention further provides a target aircraft dynamic stress calibration system with real-time feedback for running the target aircraft dynamic stress calibration method with real-time feedback according to the embodiment of the present invention, including: A dynamic stress sensitive area division module that divides the dynamic stress sensitive area based on the three-dimensional dynamics model of the target aircraft and generates sensor layout planning information. Its inputs include the three-dimensional dynamics model of the target aircraft, material property parameters, and motion condition data, and the outputs are the dynamic stress sensitive area distribution map and sensor layout planning information. This module is connected to the sensor array deployment module and transmits the sensor layout planning information to the subsequent module; A sensor array deployment module that deploys a multi-axis stress sensor array in the specified area according to the planning information provided by the dynamic stress sensitive area division module. The inputs are the dynamic stress sensitive area distribution map and sensor layout planning information, and the outputs are the topological structure data of the sensor array and the sensor position identifier. This module is connected to the signal acquisition module to provide basic data support for signal acquisition; A signal acquisition module that synchronously acquires the original stress signals output by the multi-axis stress sensor array and dynamically adjusts the signal sampling frequency according to the real-time motion acceleration value of the target aircraft. This module includes a sampling frequency adjustment unit and a signal buffer unit to ensure the efficiency and accuracy of signal acquisition. The inputs include the topological structure data of the sensor array and the real-time motion acceleration value of the target aircraft, and the outputs are the original stress signal data and sampling frequency adjustment parameters. The signal acquisition module is connected to the signal processing module to provide the original data for subsequent signal processing; The signal processing module adaptively filters the original stress signal based on the noise feature library to generate the preprocessed stress signal. The module is internally divided into a noise feature library construction unit, a filter selection unit, and a time-frequency analysis unit, which are responsible for noise feature extraction, filter selection, and signal filtering respectively. The inputs are the original stress signal data and the current moving speed value of the target aircraft, and the outputs are the preprocessed stress signal data and the filter type identifier. This module is connected to the dynamic stress compensation module to provide the processed signal for stress compensation; The dynamic stress compensation module predicts the theoretical stress value through a pre-trained neural network, calculates the dynamic error distribution matrix, and iteratively optimizes the compensation coefficient using the weighted least squares method. The module includes a neural network prediction unit, an error calculation unit, a compensation coefficient optimization unit, and a closed-loop feedback trigger unit to achieve precise calibration of stress data. The inputs are the preprocessed stress signal data, the moving acceleration value, and the angular velocity value of the target aircraft, and the outputs are the calibrated stress data, the optimized parameters of the compensation coefficient, and the closed-loop feedback trigger signal. This module is connected to the calibration verification module and the closed-loop feedback module to form a complete feedback control link; The calibration verification module compares the calibrated stress data with the measurement data of the reference measurement device and reversely corrects the parameters of the dynamic stress compensation model. The comparison analysis unit, weight correction unit, and weight allocation update unit in the module work together to ensure the continuous optimization of the calibration parameters. The inputs are the calibrated stress data and the data of the reference measurement device, and the outputs are the corrected neural network weight parameters and the updated weight allocation strategy. The calibration verification module is connected to the dynamic stress compensation module to achieve closed-loop correction of the parameters; The closed-loop feedback module triggers the closed-loop feedback mechanism according to the change rate of the compensation coefficient and dynamically adjusts the system parameters to adapt to the real-time motion state of the target aircraft. The mutation detection unit, parameter update queue management unit, and threshold calibration unit in the module are responsible for motion state monitoring, parameter storage, and threshold update respectively. The inputs are the change rate of the compensation coefficient and the real-time motion state data of the target aircraft, and the outputs are the smoothing coefficient adjustment parameters and the updated decision threshold. This module is connected to the dynamic stress compensation module and the signal acquisition module to ensure the real-time optimization of the system parameters; The system control module takes the status feedback information of each module as the input and outputs the communication instructions and task scheduling signals between each module. The system control module is connected to all modules to ensure the efficient operation of the system.

[0014] Advantages of the present invention: By combining a multi-axis stress sensor array with a dynamic stress compensation model, the precise measurement and compensation of the dynamic stress of the target aircraft are achieved. First, the stress-sensitive regions are accurately divided and the sampling frequency is dynamically adjusted according to the motion acceleration to ensure high-quality signal acquisition. Second, the compensation coefficients are iteratively optimized using a pre-trained neural network and the weighted least squares method, improving the accuracy and response speed of stress compensation. Finally, by comparing with a reference device to correct the neural network weights, the continuous optimization and dynamic adaptability of the compensation model are ensured, enhancing the real-time performance and accuracy of the stress calibration process of the target aircraft and improving the stability and reliability of the system. Brief Description of the Drawings

[0015] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] Figure 1 is the flowchart of the steps of the method of the present invention; Figure 2 is the flowchart of the steps for determining the sensor spacing in step 1 of the method of the present invention; Figure 3 is the structural block diagram of the system of the present invention. Detailed Embodiment

[0017] The following will describe the present invention in detail with reference to the drawings and specific embodiments. At the same time, it should be noted here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments. For some well-known technologies, those skilled in the art can also adopt other alternative methods for implementation; and the drawings are only for more specifically describing the embodiments and are not intended to specifically limit the present invention.

[0018] Please refer to Figures 1 - 3 , the embodiment of the present invention provides a method for calibrating the dynamic stress of a target aircraft with real-time feedback. First, the dynamic stress-sensitive regions are divided through the three-dimensional dynamic model of the target aircraft. This region is mainly concentrated in the parts of the target aircraft that are easily affected by large stress concentration or changes. According to the stress distribution characteristics of this region, a multi-axis stress sensor array is arranged according to preset rules. The spacing and density of the sensors will be dynamically adjusted according to the stress distribution of each part of the target aircraft, especially increasing the sensor density in the stress concentration region to more accurately capture the stress signal. This step lays the foundation for subsequent signal acquisition and data processing, ensuring that the stress regions that may change during the movement of the target aircraft can be covered.

[0019] Based on Step 1, the sensor array starts to synchronously collect the original stress signals output by each sensor on the surface of the target aircraft. To adapt to the different accelerations generated by the target aircraft in different motion states, the signal sampling frequency will be dynamically adjusted according to the real-time motion acceleration value of the target aircraft. During accelerated motion, the sampling frequency is increased to ensure capturing higher-frequency stress changes; while in a lower-speed or stable state, the sampling frequency is decreased to save computing resources. In addition, the collected original stress signals are subjected to adaptive filtering based on the noise feature library, which can effectively remove environmental noise and ensure the purity and effectiveness of the signals. This step guarantees the data quality for subsequent signal processing.

[0020] The preprocessed stress signals are input into the dynamic stress compensation model. This model takes the motion acceleration value and angular velocity value of the target aircraft as inputs, and predicts the theoretical stress values at the positions of each sensor through a pre-trained neural network. By calculating the error between the actually measured stress value and the theoretical stress value, a dynamic error distribution matrix is obtained. The weighted least squares method is used to iteratively optimize the compensation coefficients, and at the same time, the closed-loop feedback mechanism is triggered by the change rate of the compensation coefficients. This mechanism can ensure that the compensation coefficients are adjusted in real time with the change of the motion state, enabling the system to respond to the stress changes of the target aircraft in different motion modes in real time.

[0021] In the controlled loading test of the target aircraft, the calibrated stress data is compared with the data of the reference measuring device. By calculating the difference between the two, the neural network weight parameters in the dynamic stress compensation model are corrected backward. Further, the weight distribution strategy is adjusted by the weighted least squares method, enabling the system to improve the accuracy of the model through continuous iteration. This step not only optimizes the compensation coefficients but also ensures that the system can perform accurate stress measurements stably in the long term through dynamic correction.

[0022] Through the effective connection and interaction of these four steps, this method can achieve precise real-time calibration of the dynamic stress data of the target aircraft, with significant advantages in accuracy, real-time performance, and robustness, and is applicable to high-precision target aircraft testing and applications.

[0023] Among them, the three-dimensional dynamic model of the target aircraft is established by a mathematical model that accurately reflects the relationship between the forces and motion states of the target aircraft during operation, in order to calibrate and compensate the dynamic stress of the target aircraft. The implementation of this model not only involves the structural analysis and motion simulation of the target aircraft but also includes predicting and adjusting the real-time acceleration, angular velocity, and their corresponding stress distributions.

[0024] The three-dimensional dynamic model of the target aircraft is based on the basic principles of physical mechanics, combined with the geometric structure, mass distribution, and dynamic characteristics of the target aircraft. The model includes the following key components: Rigid body dynamics: Use Newton-Euler equations or Lagrange equations to describe the rigid body motion of the target drone. The model represents physical quantities such as the displacement, velocity, and acceleration of the target drone through these equations. Specifically, the acceleration and angular velocity of the target drone are closely related to the force acting on the target drone and can affect the dynamic response and stress distribution of the target drone.

[0025] Mass and inertia matrix: The mass distribution and inertia matrix of the target drone are important components of the dynamic model. By detailed modeling of the mass and inertia characteristics of each component of the target drone, the response behavior of each component when subjected to external forces can be determined, and then the motion state of the target drone can be predicted. The inertia matrix can reflect the rotational inertia of the target drone and is the key to describing rotational motion.

[0026] External loads and force analysis: The target drone is subject to different types of external loads (such as air resistance, gravity, etc.) during motion. These external loads act on various parts of the target drone, resulting in stress distributions in different parts. Through finite element analysis (FEM) or other numerical simulation methods, the stress states of different regions on the surface of the target drone can be calculated.

[0027] In one possible implementation, first, conduct an unloaded motion test of the target drone, that is, move the target drone without external load to simulate its motion state during actual use. The key to this process is to collect the stress attenuation data on the main stress transmission path of the target drone. The stress attenuation data can reflect the stress weakening at different positions, thereby helping to understand how the stress attenuates with distance. By calculating the stress attenuation gradient at different positions, the variation law of the stress during the transmission from the center of the target drone to the outside can be obtained.

[0028] By calculating the stress attenuation gradient, the stress change rate at different positions is obtained. A position with a larger stress attenuation gradient indicates that the stress changes faster at that position, so a higher sensor density is required to monitor the stress state in real time; while in a region with smaller stress changes, the sensor spacing can be appropriately increased. According to these attenuation gradient values, determine the ratio of the equal ratio spacing so that the spacing between adjacent sensors increases in accordance with the set ratio as the stress attenuation gradient increases. This method can minimize unnecessary sensor arrangements, optimize the monitoring efficiency, and ensure that there are sufficient sensors in the high-stress region to capture key data.

[0029] The joints and cross-sectional mutation areas of the target aircraft are usually places where stress is concentrated or where stress changes greatly. These areas are potential risk points for stress overload during the use of the target aircraft, so they require special attention. Through finite element simulation, the stress distribution of each component of the target aircraft during movement can be simulated, especially at the joints and cross-sectional mutation areas. This simulation step can extract the maximum stress gradient value and the average stress gradient value in the area, helping to evaluate the change amplitude and distribution law of stress in the area.

[0030] By calculating the ratio of the maximum stress gradient value to the average stress gradient value, the intensity of stress change in the area can be obtained. This ratio, as a multiple of the increase in sensor density, means that in areas with large stress gradients (i.e., more drastic stress changes), the sensor layout density needs to be increased. The purpose of this is to provide more redundant sensors in key areas of stress concentration to ensure the accuracy and reliability of the data. The arrangement of redundant sensors can avoid monitoring failures due to sensor failures or data errors, thereby improving the robustness of the entire target drone dynamic stress monitoring system.

[0031] The layout of sensors needs to be dynamically adjusted according to the actual motion state and stress distribution of the target drone. By combining the stress attenuation data and stress gradient analysis obtained in the no-load motion test, it can ensure that the sensor layout is more in line with the dynamic characteristics of the target drone, avoiding over-layout or under-layout. The strategy of increasing the geometric spacing can concentrate sensors in high-stress areas, improving the sensitivity to stress changes and monitoring accuracy.

[0032] By calculating the gradient information of the stress concentration area through finite element simulation and combining it with the density adjustment strategy, the stress overload area of ​​the target aircraft can be accurately identified. Placing more redundant sensors in these areas can prevent monitoring failures due to inaccurate data from individual sensors. In addition, arranging redundant sensors in the cross-sectional mutation area and the connection can effectively capture the stress mutation, avoid the stress mutation not being monitored in time, and thus provide early warning of possible structural risks.

[0033] In a possible implementation, the process of dynamically adjusting the signal sampling frequency mainly involves determining the signal sampling frequency according to the target drone's motion acceleration and the resonance characteristics of the structure. This process adjusts the sampling frequency to meet the dynamic stress monitoring needs of the target drone in different motion states, thereby improving the accuracy and real-time performance of the monitoring data.

[0034] Specifically, before starting to dynamically adjust the signal sampling frequency, it is necessary to obtain the critical instability acceleration value at the maximum designed speed of the target aircraft through a dynamic calibration test of the target aircraft. The critical instability acceleration value represents the acceleration change at which the system may become unstable when the target aircraft reaches a certain speed. This value serves as a benchmark for setting the acceleration threshold. The acceleration threshold is usually a preset percentage (e.g., 80%) of the critical instability acceleration and is the basis for dynamically adjusting the sampling frequency. By setting this threshold, it is possible to determine whether the target aircraft enters a high-acceleration state during actual movement and decide whether to switch to the high-frequency sampling mode.

[0035] Furthermore, when the real-time motion acceleration value of the target aircraft exceeds the set acceleration threshold, it indicates that the target aircraft has entered a high-speed motion or may experience a severe dynamic response. In this case, in order to more accurately capture the stress changes of the target aircraft in the high-acceleration state, a high-frequency sampling mode needs to be adopted. The frequency of high-frequency sampling needs to be set as an integer multiple of the structural resonance frequency of the target aircraft. This is because at high frequencies, if the relationship between the sampling frequency and the structural resonance frequency is inappropriate, it may lead to signal aliasing effects, that is, the high-frequency components of the stress signal cannot be correctly recorded. By analyzing the relationship between the resonance frequency and the signal aliasing effect, an appropriate integer multiple coefficient is determined to ensure that high-frequency sampling can accurately capture the dynamic response of the target aircraft during high-speed motion.

[0036] When the real-time motion acceleration value of the target aircraft is lower than or equal to the acceleration threshold, it indicates that the target aircraft is in a lower motion speed or acceleration state. At this time, the dynamic response of the target aircraft is relatively stable, and the required sampling frequency can be reduced to save storage space and processing resources. In this case, a low-frequency sampling mode is adopted. The low-frequency sampling frequency is a divided value of the high-frequency sampling frequency. The calculation of this divided value is based on the reciprocal of the product of the current motion speed of the target aircraft and the structural resonance frequency. Through this calculation, it can be ensured that in the low-acceleration state, the sampling frequency can adapt to the motion changes of the target aircraft while avoiding unnecessary calculations and storage burdens caused by too high a frequency.

[0037] In a possible implementation, in the no-load stationary state of the target aircraft, first, the baseline noise signals at various positions of the target aircraft are collected by each sensor. These baseline signals do not contain stress information but are environmental noise and the vibration noise of the equipment itself. Then, a fast Fourier transform (FFT) is performed on these collected baseline noise signals to extract the noise spectrum characteristics at each sensor position. Through FFT, the noise signal can be converted from the time domain to the frequency domain, and the noise patterns in different frequency ranges can be identified. These noise spectrum characteristics will then be stored in the noise feature library as the basis for subsequent filter selection.

[0038] In actual operation, according to the sensor position in the current motion state of the target aircraft, the noise spectrum feature most relevant to the current sensor position is matched from a pre-established noise feature library. This matching process is carried out based on the sensor position and the characteristics of the collected signals. After the noise spectrum feature matching is completed, the system will select the filter type with the highest correlation to the matched noise spectrum feature. This step is to ensure that the selected filter can most effectively remove signals similar to environmental noise, thereby more accurately retaining the effective stress signal.

[0039] After the signal filter is selected, the original stress signal is subjected to time-frequency analysis using the sliding window method. The sliding window method is a commonly used time-frequency analysis technique. By setting a sliding window on the signal, the frequency and time distribution of the signal are analyzed in real time. During this process, the length of the sliding window is dynamically adjusted according to the decay time of the autocorrelation function of the current motion speed value of the target aircraft. Specifically, when the motion speed of the target aircraft is high, the signal change speed will also increase, and at this time, the length of the sliding window will be appropriately shortened to maintain sufficient time resolution; conversely, in the low-speed motion state, the window length can be appropriately extended.

[0040] When performing time-frequency analysis, the system calculates the energy proportion of the signal within a preset frequency band. If the energy proportion of the signal within these frequency bands exceeds a preset first threshold, the signal is determined to be an effective stress component, indicating that it contains useful stress information; otherwise, the signal is determined to be environmental vibration noise. The signals determined to be noise will be filtered out to avoid interfering with the measurement of the dynamic stress of the target aircraft.

[0041] In a possible implementation manner, the acquisition of training data is the basis for establishing a dynamic stress compensation model. These data are obtained through the calibration test of the target aircraft. The core of the test is to measure the strain change of the target aircraft by pasting reference strain gauges on the surface of the target aircraft, and simultaneously record the acceleration value, angular velocity value, temperature value of the target aircraft in different motion modes, and the strain gauge measurement values at each sensor position. Different preset motion modes simulate various motion states that the target aircraft may encounter under actual working conditions, ensuring the diversity and representativeness of the training data.

[0042] This process ensures the comprehensiveness and accuracy of the training data, enabling the neural network to learn the stress change law of the target aircraft under various dynamic conditions, thereby improving the accuracy and robustness of the model.

[0043] Based on the neural network training data, the next step is to calculate the dynamic error distribution matrix. The first step in the calculation process is to use the difference between the measured stress values and the theoretical stress values at each sensor location as the initial error values. These differences reflect the measurement errors at each sensor location. Next, spatial interpolation is performed on the error values using the stress influence factors at the locations of each sensor. The stress influence factors are obtained through finite element simulation and can accurately describe the stress distribution characteristics of each part of the target aircraft.

[0044] The stress influence factors obtained through finite element simulation can accurately reflect the stress transfer characteristics of each part of the target aircraft, ensuring a high degree of consistency between the spatial distribution of errors and the actual motion state of the target aircraft. The interpolation process enables reasonable extrapolation of error values between different sensors, providing an accurate basis for dynamic compensation.

[0045] After the calculation of the error distribution matrix is completed, the next step is to apply the weighted least squares method for dynamic stress compensation. The core of the weighted least squares method is to assign weights based on the historical error data at each sensor location. By calculating the standard deviation of the historical error data at each sensor location, the sensor locations with large error fluctuations are identified and given small weight values, while the sensor locations with small error fluctuations and stable measurements are given large weight values. This ensures that in the compensation process, more attention is paid to the stable and reliable sensor data, reducing the deviation introduced by large errors in individual sensors.

[0046] The weighted least squares method can effectively suppress the influence of noise and abnormal data in the dynamic stress compensation process by assigning higher weights to sensors with smaller errors, improving the accuracy and stability of the compensation results. At the same time, through the evaluation of the standard deviation of historical error data, the weights of each sensor can be dynamically adjusted to adapt to the changes of the target aircraft in different working environments.

[0047] By integrating technologies such as integrated calibration tests, finite element simulation, and the weighted least squares method, an accurate dynamic stress compensation model is effectively established, which can significantly improve the accuracy and real-time performance of the stress calibration of the target aircraft.

[0048] In a possible implementation, the iterative optimization of the compensation coefficient is first dynamically smoothed by the exponentially weighted moving average method (EWMA). This method uses past and current compensation coefficients to calculate a new smoothed value, with more recent values given greater weight. The exponentially weighted smoothing coefficient determines the influence of historical data on the current calculation.

[0049] When the target drone is in motion and it is detected that the change rate of the motion acceleration exceeds the third threshold, it means that the motion state of the target drone has changed rapidly or mutated. At this time, in order to respond more quickly to the change in the motion state, the system reduces the smoothing coefficient and assigns more weight to the new data, thereby accelerating the adjustment of the compensation coefficient and improving the response speed.

[0050] When the motion state of the target drone becomes stable and the change rate of the motion acceleration is lower than the third threshold, it means that the motion state tends to be stable. At this time, the system increases the smoothing coefficient, making the influence of historical data on the current compensation coefficient increase, thereby improving the anti-noise ability and reducing the fluctuations caused by noise interference.

[0051] Through this dynamic adjustment mechanism, the system can quickly respond when the target drone's motion mutates, timely correct the compensation coefficient, and when the motion state is stable, reduce over-response and enhance the system's ability to suppress noise. This flexible adjustment of the smoothing coefficient not only improves the real-time responsiveness of the system but also ensures smoothness and accuracy in the stable state.

[0052] The optimization of the compensation coefficient also depends on the closed-loop feedback mechanism. In each iteration, the system will evaluate whether to trigger the feedback mechanism based on the change situation of the compensation coefficient. The specific triggering condition is: if the absolute value of the change rate of the compensation coefficient in N consecutive iterations is less than the fourth threshold, it means that the compensation coefficient has tended to be stable, and the system will consider that the current compensation coefficient has reached the optimized state and stop further adjustment.

[0053] Among them, the determination methods of the N value and the fourth threshold are as follows: N value: The N value represents the number of consecutive iteration times, which is determined by counting the stress fluctuation times of the target drone in a typical motion cycle and can be set according to the specific motion characteristics of the target drone.

[0054] Fourth threshold: The fourth threshold is the maximum allowable amplitude of the change rate of the compensation coefficient, and its value is usually determined by counting the stress fluctuation amplitude of the target drone in a typical motion cycle to ensure that the change of the compensation coefficient will not be too large and guarantee the stability of the system.

[0055] Through the closed-loop feedback mechanism, the system can automatically judge when to stop the iterative optimization of the compensation coefficient, avoid unnecessary calculations and over-adjustment, and ensure the stable operation of the system within an appropriate time. This mechanism can improve the efficiency of the compensation process, reduce the unnecessary calculation burden, and improve the overall accuracy and response speed of the system.

[0056] This method of iteratively optimizing the compensation coefficient can optimize the response speed and anti-noise ability while ensuring high-precision compensation by combining the exponential weighted moving average method and the closed-loop feedback mechanism, thereby improving the overall performance of the system.

[0057] In a possible implementation, the system evaluates the current calibration effect by calculating the root mean square error (RMSE) between the calibrated stress data and the reference measurement device data. The RMSE is a commonly used method to measure the difference between two sets of data and can reflect the overall deviation between the stress data and the measured values of the reference device.

[0058] Specifically, first, ensure that the calculation is performed at the same timestamp, that is, the time points corresponding to the calibrated stress data and the reference device data are the same. This can ensure the comparability of the data and avoid errors caused by time differences. Further, by calculating the RMSE between the calibrated data and the reference data, an error value is obtained, which is used to evaluate the accuracy of the current neural network output and reflects the degree to which the system needs to be adjusted. By quantifying the difference between the data, it helps the system identify the errors in the current stress calibration process, thereby providing a basis for subsequent weight correction.

[0059] Further, according to the calculated RMSE, the weight parameters of the neural network will be corrected. The correction amount is not simply linearly adjusted but is determined by a non-linear function.

[0060] Specifically, the RMSE is mapped through a non-linear function to determine the weight correction amount of the neural network. This can more finely control the influence of the error on the weight and avoid over-adjustment or under-adjustment. The slope value of the non-linear function (i.e., the sensitivity of the error to the correction amount) is dynamically adjusted. By analyzing the convergence speed and stability of the error in the historical correction process, the system can intelligently adjust the slope to make the weight correction more efficient and stable. For example, if the error convergence speed is slow, the slope can be appropriately increased to accelerate the correction speed. The non-linear function makes the correction process more flexible and precise, and can adjust the weights of the neural network according to the characteristics of the current error, thereby improving the calibration accuracy and efficiency of the system.

[0061] The update of the weight allocation strategy mainly involves sensor groups with spatial correlation. Spatial correlation reflects the mutual relationship between sensors in terms of physical location and the correlation of their measurement data.

[0062] Specifically, by analyzing the error consistency index within the sensor group, it can be judged whether the error distributions of the sensors within the group are consistent. If the errors of some sensors are small and the errors of other sensors are relatively large, the system will dynamically adjust the weights of these sensors to ensure the accuracy of the overall measurement.

[0063] By clustering and analyzing the historical error distribution patterns, the system can identify which sensor groups have high spatial correlation. Clustering analysis can judge the spatial correlation between sensors based on the error fluctuation trends of the sensors, and then adjust the weights of the sensors within the group.

[0064] The update of the weight allocation strategy enables the system to optimize according to the characteristics of different sensor groups, reducing the impact on the overall calibration effect caused by large errors in individual sensors. In addition, this strategy can effectively increase the weights of sensor groups with strong spatial correlation, further improving the calibration accuracy.

[0065] Dynamically optimize the dynamic stress calibration process of the target aircraft by comparing difference value calculations, non-linear weight correction, and spatial correlation analysis, so as to achieve efficient and accurate real-time feedback calibration.

[0066] In a possible implementation, first, calculate the motion acceleration value of the target aircraft in real time, and generate a difference sequence by calculating the difference of the acceleration sequence. The difference sequence represents the rate of change of acceleration at adjacent moments. Calculating the acceleration difference sequence helps to capture sudden changes in the motion state, that is, when the motion mode of the target aircraft changes drastically.

[0067] The acceleration difference sequence can reflect the instantaneous motion change of the target aircraft. When the acceleration changes suddenly, the value of the difference sequence will increase significantly, providing a data basis for subsequent motion state determination.

[0068] When the number of times the absolute value of the difference sequence continuously exceeds the set fifth threshold reaches the preset value, the system will determine that a sudden change in the motion state has occurred. Specifically, the threshold determination requires detecting whether there are points in the acceleration difference sequence that continuously exceed a specific threshold, and when the number of these points reaches a certain preset value, it will be determined that a sudden change in the motion state has occurred.

[0069] This process detects the difference sequence one by one, compares its relationship with the fifth threshold, and then determines whether a sudden change in the motion state has occurred. If the change in the difference sequence exceeds the normal fluctuation range (i.e., exceeds the fifth threshold), and this change lasts for a certain period of time (exceeds the preset number of times), it can be considered that the motion state of the target aircraft has changed drastically.

[0070] The method for determining the fifth threshold is achieved by statistically calculating the standard deviation of the acceleration difference sequence in the historical motion data of the target aircraft. The standard deviation measures the amplitude of acceleration change in the historical motion data. Taking three times the standard deviation as the initial value of the fifth threshold means that under normal circumstances, the fluctuation range of the difference sequence should be within three times the standard deviation.

[0071] As the real-time motion mode changes, the fifth threshold will be dynamically scaled according to the current motion mode. For example, when the motion of the target aircraft is more intense, the threshold may be appropriately increased to avoid false judgments caused by drastic changes; when the motion is stable, the threshold may be decreased to more sensitively detect smaller state mutations.

[0072] The initial value of the fifth threshold is set based on the standard deviation of historical data, which can ensure that the threshold is reasonable within the fluctuation range of historical data. At the same time, this threshold is adjusted in real time to adapt to the current motion mode of the target aircraft, so as to ensure that the system's response to sudden state changes is both sensitive and not overly sensitive.

[0073] By calculating the acceleration difference sequence in real time, dynamically determining the fifth threshold, and determining mutations based on the threshold, the sudden changes in the motion state of the target aircraft can be accurately and flexibly detected, and then precise data support can be provided for the dynamic stress calibration of real-time feedback.

[0074] In a possible implementation manner, during the execution of steps 1 to 4, the calculation results of all dynamically adjusted parameters are stored in the parameter update queue. To ensure the consistency and validity of data during parameter update, a double-buffer mechanism is adopted. The double-buffer mechanism means that there are two buffers in the system. One buffer is used to store the data currently in use, while the other buffer is used to store the data being updated. When new data is ready, the system swaps the current buffer and the update buffer, so that the updated data can seamlessly replace the old data while maintaining data consistency.

[0075] The introduction of the double-buffer mechanism ensures that during the dynamic adjustment process, the system will not be in a temporarily unavailable or inconsistent state due to data update. In this way, when adjusting parameters each time, whether it is the calibration data of real-time feedback or key information such as the acceleration of the target aircraft's motion, they can be updated synchronously and consistently, thereby improving the stability and reliability of the system.

[0076] During the dynamic stress calibration of the target aircraft, the key determination threshold is used to judge whether the motion state of the target aircraft has mutated, and then determine whether to trigger the stress calibration process. To ensure that these thresholds always reflect the actual operating state of the target aircraft, the system regularly collects the operating data of the target aircraft and evaluates the accuracy of the thresholds based on the statistical distribution characteristics of these data. If the statistical distribution characteristics of the operating data deviate from the initial calibration value by more than the set sixth threshold, the system will trigger the threshold recalibration process. This process recalculates and adjusts the threshold to ensure that the target aircraft can accurately reflect the changes in the motion state in real time under different operating states.

[0077] This update mechanism is closely combined with the aforementioned double-buffer mechanism. The double buffer can ensure that when the threshold is recalibrated, the switching between the old threshold and the new threshold in the parameter update queue will not affect the real-time response of the system. During the threshold recalibration process, the new threshold is calculated and stored in the update queue, and is switched to the new value during the next update to ensure the stable operation of the entire system.

[0078] Correspondingly, an embodiment of the present invention further provides a real-time feedback target drone dynamic stress calibration system for running a real-time feedback target drone dynamic stress calibration method according to an embodiment of the present invention, including: A dynamic stress sensitive area division module. The dynamic stress sensitive area division module divides the dynamic stress sensitive area based on the three-dimensional dynamic model of the target drone and generates sensor layout planning information. Its inputs include the three-dimensional dynamic model of the target drone, material property parameters, and motion condition data, and the outputs are the dynamic stress sensitive area distribution map and the sensor layout planning information. This module is connected to the sensor array deployment module and transmits the sensor layout planning information to the subsequent module; A sensor array deployment module. The sensor array deployment module deploys a multi-axis stress sensor array in a specified area according to the planning information provided by the dynamic stress sensitive area division module. The inputs are the dynamic stress sensitive area distribution map and the sensor layout planning information, and the outputs are the topological structure data of the sensor array and the sensor position identifier. This module is connected to the signal acquisition module to provide basic data support for signal acquisition; A signal acquisition module. The signal acquisition module synchronously acquires the original stress signals output by the multi-axis stress sensor array and dynamically adjusts the signal sampling frequency according to the real-time motion acceleration value of the target drone. This module includes a sampling frequency adjustment unit and a signal buffer unit to ensure the efficiency and accuracy of signal acquisition. The inputs include the topological structure data of the sensor array and the real-time motion acceleration value of the target drone, and the outputs are the original stress signal data and the sampling frequency adjustment parameters. The signal acquisition module is connected to the signal processing module to provide the original data for subsequent signal processing; A signal processing module. The signal processing module performs adaptive filtering processing on the original stress signals based on the noise feature library to generate preprocessed stress signals. The internal part of the module is divided into a noise feature library construction unit, a filter selection unit, and a time-frequency analysis unit, which are responsible for noise feature extraction, filter selection, and signal filtering respectively. The inputs are the original stress signal data and the current motion speed value of the target drone, and the outputs are the preprocessed stress signal data and the filter type identifier. This module is connected to the dynamic stress compensation module to provide the processed signals for stress compensation; A dynamic stress compensation module. The dynamic stress compensation module predicts the theoretical stress value through a pre-trained neural network, calculates the dynamic error distribution matrix, and uses the weighted least squares method to iteratively optimize the compensation coefficient. The module includes a neural network prediction unit, an error calculation unit, a compensation coefficient optimization unit, and a closed-loop feedback trigger unit to achieve precise calibration of stress data. The inputs are the preprocessed stress signal data, the motion acceleration value, and the angular velocity value of the target drone, and the outputs are the calibrated stress data, the compensation coefficient optimization parameters, and the closed-loop feedback trigger signal. This module is connected to the calibration verification module and the closed-loop feedback module to form a complete feedback control link; Calibration verification module. The calibration verification module compares the calibrated stress data with the measurement data of the reference measurement device, and reversely corrects the parameters of the dynamic stress compensation model. The comparison analysis unit, weight correction unit, and weight allocation and update unit in the module work together to ensure the continuous optimization of the calibration parameters. The input is the calibrated stress data and the reference measurement device data, and the output is the corrected neural network weight parameters and the updated weight allocation strategy. The calibration verification module is connected to the dynamic stress compensation module to achieve closed-loop correction of the parameters; Closed-loop feedback module. The closed-loop feedback module triggers the closed-loop feedback mechanism according to the change rate of the compensation coefficient, and dynamically adjusts the system parameters to adapt to the real-time motion state of the target aircraft. The mutation detection unit, parameter update queue management unit, and threshold calibration unit in the module are responsible for motion state monitoring, parameter storage, and threshold update respectively. The input is the change rate of the compensation coefficient and the real-time motion state data of the target aircraft, and the output is the smoothing coefficient adjustment parameter and the updated decision threshold. This module is connected to the dynamic stress compensation module and the signal acquisition module to ensure the real-time optimization of the system parameters; System control module. The input is the status feedback information of each module, and the output is the communication instructions and task scheduling signals between each module. The system control module is connected to all modules to ensure the efficient operation of the system.

[0079] The present invention covers any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of the present invention. To enable the public to have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention. However, those skilled in the art can fully understand the present invention without the description of these details. In addition, well-known methods, processes, procedures, components, and circuits are not described in detail to avoid unnecessary confusion to the essence of the present invention.

[0080] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A target drone dynamic stress calibration method with real-time feedback, characterized in that: The following steps are involved: Step 1: Divide the dynamic stress sensitive area based on the three-dimensional dynamic model of the target aircraft, and arrange the multi-axis stress sensor array in the dynamic stress sensitive area according to preset rules, wherein the preset rules include a method for determining the sensor spacing and a method for adjusting the density of the stress concentration area; Step 2: synchronously collect the original stress signal output by the multi-axis stress sensor array, dynamically adjust the signal sampling frequency according to the real-time motion acceleration value of the target aircraft, and perform adaptive filtering processing on the original stress signal based on the noise feature library to generate a pre-processed stress signal; Step 3: Input the preprocessed stress signal into a dynamic stress compensation model. The dynamic stress compensation model uses the target drone motion acceleration value and the target drone angular velocity value as input parameters, predicts the theoretical stress value of each sensor position through a pre-trained neural network, calculates the dynamic error distribution matrix of the measured stress value and the theoretical stress value, uses the weighted least squares method to iteratively optimize the compensation coefficient, and triggers a closed-loop feedback mechanism according to the compensation coefficient change rate; Step 4: In the controlled loading test of the target aircraft, the calibrated stress data is compared with the measurement data of the reference measuring device, and the neural network weight parameters of the dynamic stress compensation model are reversely corrected according to the comparison difference value, and the weight allocation strategy of the weighted least squares method is updated to complete the dynamic iteration of the calibration parameters.

2. The method for calibrating target drone dynamic stress with real-time feedback according to claim 1, characterized in that: The method for determining the sensor spacing in step 1 includes: a. Collect stress attenuation data on the main stress transfer path through the target drone no-load motion test and calculate the stress attenuation gradient at different positions; b. determining the ratio of the proportional spacing according to the stress attenuation gradient, so that the spacing between adjacent sensors increases according to the ratio as the stress attenuation gradient increases; The density adjustment method of the stress concentration area includes: a. Perform finite element simulation on the target drone connection and cross-section mutation area to extract the maximum stress gradient value and the average stress gradient value of the area; b. Using the ratio of the maximum stress gradient value to the average stress gradient value as a sensor density increase factor, and arranging redundant sensors in the area according to the increase factor.

3. The method for calibrating target drone dynamic stress with real-time feedback according to claim 2, characterized in that: The process of dynamically adjusting the signal sampling frequency in step 2 includes: The critical instability acceleration value corresponding to the maximum design speed of the target aircraft is obtained through a target aircraft dynamic calibration test, and a preset percentage of the critical instability acceleration value is used as an acceleration threshold; When the real-time motion acceleration value exceeds the acceleration threshold, a high-frequency sampling mode is adopted, and the frequency of the high-frequency sampling is an integer multiple of the resonance frequency of the target aircraft structure, and the value of the integer multiple is determined by analyzing the relationship between the resonance frequency and the signal aliasing effect; When the real-time motion acceleration value is lower than or equal to the acceleration threshold, a low-frequency sampling mode is adopted, and the frequency of the low-frequency sampling is the divided value of the frequency of the high-frequency sampling, and the divided value is calculated based on the inverse of the product of the current motion speed value of the target aircraft and the structural resonance frequency.

4. The method for calibrating target drone dynamic stress with real-time feedback according to claim 3, characterized in that: The adaptive filtering process in step 2 includes: Collecting baseline noise signals at each sensor position when the target drone is in an unloaded and stationary state, performing fast Fourier transform on the baseline noise signals, extracting noise spectrum features at each sensor position, and establishing the noise feature library; Matching corresponding noise spectrum features from the noise feature library according to the current sensor position, and selecting a filter type in the filter group with the highest correlation with the noise spectrum features; The sliding window method is used to perform time-frequency analysis on the original stress signal. The sliding window length is dynamically adjusted according to the attenuation time of the autocorrelation function of the current motion speed value of the target aircraft. When the energy proportion of the signal in the preset frequency band exceeds the first threshold, it is determined to be an effective stress component, otherwise it is determined to be environmental vibration noise and filtered out.

5. The method for calibrating target drone dynamic stress with real-time feedback according to claim 4, characterized in that: The process of establishing the dynamic stress compensation model in step 3 includes: The training data of the pre-trained neural network is obtained through a target drone calibration test, which includes: pasting a reference strain gauge on the surface of the target drone, and synchronously recording the target drone's motion acceleration value, angular velocity value, temperature value and strain gauge measurement values ​​at each sensor position under multiple preset motion modes; The calculation process of the dynamic error distribution matrix includes: taking the difference between the measured stress value and the theoretical stress value at each sensor position as the initial error value, and performing spatial interpolation according to the stress influence factor at the position of each sensor, wherein the stress influence factor is obtained by finite element simulation calculation; The weight allocation strategy of the weighted least square method includes: calculating the standard deviation of the historical error data of each sensor position, and assigning a small weight value to the sensor position whose standard deviation exceeds a second threshold.

6. The method for calibrating target drone dynamic stress with real-time feedback according to claim 5, characterized in that: The specific process of iteratively optimizing the compensation coefficient in step 3 includes: The compensation coefficient is dynamically smoothed by using an exponentially weighted moving average method, wherein the exponentially weighted smoothing coefficient is adjusted according to the sudden change detection result of the target drone's motion state, including: When it is detected that the rate of change of motion acceleration exceeds a third threshold, the smoothing coefficient is reduced to speed up the response speed, and when the motion state is stable, the smoothing coefficient is increased to improve the anti-noise ability; The triggering conditions of the closed-loop feedback mechanism include: the absolute value of the compensation coefficient change rate in N consecutive iterations is less than a fourth threshold value, and the value of N and the fourth threshold value are determined by counting the number and amplitude of stress fluctuations in a typical motion cycle of the target aircraft.

7. The method for calibrating target drone dynamic stress with real-time feedback according to claim 6, characterized in that: The process of reversely correcting the neural network weight parameters of the dynamic stress compensation model in step 4 includes: The method for calculating the comparison difference value is: at the same time stamp, calculating the root mean square error between the calibrated stress data and the reference measurement device data; The correction amount of the neural network weight parameter is determined according to the nonlinear function of the root mean square error, and the slope value of the nonlinear function is dynamically adjusted by analyzing the relationship between the error convergence speed and stability in the historical correction process; The updating process of the weight allocation strategy includes: for a sensor group with spatial correlation, synchronously adjusting its weight value according to the error consistency index of the sensors in the group, wherein the spatial correlation is obtained by clustering analysis of the historical error distribution pattern.

8. The method for calibrating target drone dynamic stress with real-time feedback according to claim 6, characterized in that: The specific method for detecting the sudden change of the motion state includes: Calculate the differential sequence of the target drone's motion acceleration value in real time, and when the absolute value of the differential sequence exceeds the fifth threshold value for a number of times that reaches a preset value, determine that the motion state is suddenly changed; The method for determining the fifth threshold is: statistically analyzing the standard deviation of the acceleration difference sequence in the historical motion data of the target aircraft, taking three times the standard deviation as the initial value of the fifth threshold, and dynamically scaling it according to the real-time motion mode.

9. The method for calibrating target drone dynamic stress with real-time feedback according to claim 1, characterized in that: During the execution of steps 1 to 4: The calculation results of all dynamically adjusted parameters are stored in the parameter update queue, which adopts a double buffer mechanism to ensure data consistency during the parameter update process; The updating mechanism of the key judgment threshold includes: regularly collecting the target machine operation data, calculating the statistical distribution characteristics of the operation data, and triggering the threshold recalibration process when the distribution characteristics deviate from the initial calibration value and exceed the sixth threshold.

10. A target drone dynamic stress calibration system with real-time feedback, used to run a target drone dynamic stress calibration method with real-time feedback according to any one of claims 1 to 9, characterized in that: include: Dynamic stress sensitive area division module: The dynamic stress sensitive area division module divides the dynamic stress sensitive area based on the three-dimensional dynamic model of the target aircraft and generates sensor layout planning information. Its input includes the three-dimensional dynamic model of the target aircraft, material property parameters and motion condition data, and its output is the dynamic stress sensitive area distribution map and sensor layout planning information. This module is connected to the sensor array deployment module to pass the sensor layout planning information to the subsequent modules; The sensor array deployment module deploys the multi-axis stress sensor array in the specified area according to the planning information provided by the dynamic stress sensitive area division module according to the preset rules. The input is the dynamic stress sensitive area distribution map and the sensor layout planning information. The output is the topological structure data of the sensor array and the sensor position identification. This module is connected to the signal acquisition module to provide basic data support for signal acquisition. Signal acquisition module, the signal acquisition module synchronously acquires the original stress signal output by the multi-axis stress sensor array, and dynamically adjusts the signal sampling frequency according to the real-time motion acceleration value of the target aircraft. The module includes a sampling frequency adjustment unit and a signal buffer unit to ensure the efficiency and accuracy of signal acquisition. The input includes the sensor array topology data and the real-time motion acceleration value of the target aircraft, and the output is the original stress signal data and the sampling frequency adjustment parameter. The signal acquisition module is connected to the signal processing module to provide original data for subsequent signal processing; Signal processing module: The signal processing module performs adaptive filtering processing on the original stress signal based on the noise feature library to generate a preprocessed stress signal. The module is divided into a noise feature library construction unit, a filter selection unit and a time-frequency analysis unit, which are responsible for noise feature extraction, filter selection and signal filtering respectively. The input is the original stress signal data and the current motion speed value of the target aircraft, and the output is the preprocessed stress signal data and the filter type identifier. This module is connected to the dynamic stress compensation module to provide a processed signal for stress compensation; Dynamic stress compensation module: The dynamic stress compensation module predicts theoretical stress values ​​through pre-trained neural networks, calculates dynamic error distribution matrices, and iteratively optimizes compensation coefficients using weighted least squares method. The module includes a neural network prediction unit, an error calculation unit, a compensation coefficient optimization unit, and a closed-loop feedback trigger unit to achieve accurate calibration of stress data. The input is pre-processed stress signal data, target drone motion acceleration value, and angular velocity value. The output is calibrated stress data, compensation coefficient optimization parameters, and closed-loop feedback trigger signals. The module is connected to the calibration verification module and the closed-loop feedback module to form a complete feedback control link. Calibration verification module: The calibration verification module compares the calibrated stress data with the measurement data of the reference measurement device, and reversely corrects the parameters of the dynamic stress compensation model. The comparison analysis unit, weight correction unit and weight distribution update unit in the module work together to ensure the continuous optimization of the calibration parameters. The input is the calibrated stress data and the reference measurement device data, and the output is the corrected neural network weight parameters and the updated weight distribution strategy. The calibration verification module is connected to the dynamic stress compensation module to achieve closed-loop correction of the parameters; Closed-loop feedback module: The closed-loop feedback module triggers the closed-loop feedback mechanism according to the compensation coefficient change rate, and dynamically adjusts the system parameters to adapt to the real-time motion state of the target aircraft. The mutation detection unit, parameter update queue management unit and threshold calibration unit in the module are responsible for motion state monitoring, parameter storage and threshold update respectively. The input is the compensation coefficient change rate and the real-time motion state data of the target aircraft, and the output is the smoothing coefficient adjustment parameter and the updated judgment threshold. This module is connected with the dynamic stress compensation module and the signal acquisition module to ensure the real-time optimization of system parameters. The system control module takes as input the status feedback information of each module and outputs the communication instructions and task scheduling signals between modules. The system control module is connected to all modules to ensure the efficient operation of the system.

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