An automatic synchronization method for airborne flight participation and non-airborne measured load time

Abstract

The present application belongs to the technical field of aircraft structure load spectrum measurement, and particularly relates to a method for automatically synchronizing airborne flight parameters and non-airborne measured load time; the flight parameter data of the airborne system and the load measurement data of the non-airborne load test system are filtered through a unified sampling rate, and the remaining main peak and valley points are used to search for the corresponding airborne flight parameter normal overload point of the non-airborne measured maximum load from a macro perspective according to the minimum mean square error (MSE) of the linear regression of the airborne flight parameter normal overload and the non-airborne measured load, so as to complete overall feature matching; subsequently, the airborne flight parameter normal overload point corresponding to the non-airborne measured maximum load is further searched in the x-second range near the searched overall optimal normal overload point, so as to complete the automatic time synchronization between the airborne flight parameters and the non-airborne measured load. The time synchronization problem of the data of the two systems is effectively solved, high-quality flight parameter and load data samples are obtained, and the prediction accuracy of the flight parameter-load model is improved.

Description

Technical Field

[0001] This invention belongs to the field of aircraft structural load spectrum measurement technology, specifically relating to an automatic synchronization method for airborne and non-airborne measured load time. Background Technology

[0002] The aircraft structural integrity program requires that, after delivery to the user, at least one production aircraft must be retrofitted to conduct load spectrum measurements under actual operating conditions. Based on the acquired measurement data, a measured load spectrum is compiled, the design load spectrum is revised, and the durability and damage tolerance analysis results of the aircraft airframe are updated. This provides support for rationally determining the aircraft's service life and developing the aircraft maintenance program. Because the added load spectrum testing system is independent of the onboard system, the lack of communication between the two systems leads to time asynchrony between various flight parameter signals from the onboard system and the measured load signals from the load spectrum testing system, and the sampling rates are also different. To solve this problem, the traditional approach is to install a three-axis overload sensor near the aircraft's center of gravity, collect normal overload history data through the load spectrum testing system, and then compare it with the normal overload history data in the onboard flight parameters. Time synchronization is achieved by finding the same marker points between the two. The traditional method has several problems: First, it requires reading the normal overload data from two separate systems and then manually selecting the same marker points, which is inefficient and may result in significantly different time synchronization effects if different marker points are chosen. Second, it requires the installation of overload sensors, which increases testing costs and data volume. Third, the normal overload of airborne flight parameters is generally located at the cockpit, while the load spectrum testing system measures the normal overload near the center of gravity. The two differ, making it impossible to strictly guarantee the best time synchronization effect. Even if the system is converted to the same theoretical position for time synchronization, the phase difference between the overload and the measured load will still prevent the achievement of the optimal synchronization effect. Summary of the Invention

[0003] The purpose of this invention is to propose an automatic time synchronization method for airborne flight parameters and non-airborne measured loads. By utilizing the linear correlation between airborne flight parameter normal overload and non-airborne measured loads, and comprehensively employing global feature matching and local optimal matching time optimization, this method effectively solves the time synchronization problem between airborne systems and non-airborne load testing systems, obtains high-quality flight parameter and load data samples, and improves the prediction accuracy of the flight parameter-load model.

[0004] The technical solution of this invention: This invention proposes an automatic time synchronization method for airborne flight parameter data and non-airborne measured load data. By unifying the sampling rate and filtering the airborne system flight parameter data and the non-airborne load test system load measured data, based on the remaining major peaks and valleys after filtering, and using the minimum mean square error (MSE) of the linear regression between the airborne flight parameter normal overload and the non-airborne measured load as an indicator, the method macroscopically searches for the airborne flight parameter normal overload point corresponding to the maximum non-airborne measured load, thus completing overall feature matching. Subsequently, within a range of x seconds near the found overall optimal normal overload point, the method further searches locally for the airborne flight parameter normal overload point corresponding to the maximum non-airborne measured load, thereby completing the automatic time synchronization between the airborne flight parameter and the non-airborne measured load.

[0005] Furthermore, the specific synchronization process of the time synchronization method described above includes the following steps:

[0006] Step S1: Airborne flight parameter data extraction and non-airborne measured load calculation: Extract the airborne time and normal overload data from the data recorded by the airborne system; use the strain-load equation established by ground load calibration, and calculate the bending moment, shear force, and torque load history of key aircraft components based on the load bridge data recorded by the non-airborne load test system;

[0007] Step S2: Unify sampling rate: Unify the sampling rate of airborne flight parameters and non-airborne measured loads, and retain their respective time information;

[0008] Step S3: Filtering: Perform intermediate value filtering and small amplitude filtering on the airborne normal overload and the non-airborne measured load respectively, retain the main peak and valley points, and sort them by size;

[0009] Step S4: Calculate the time t when the i-th airborne normal overload occurs sequentially. i The time t when the non-airborne measured load with the largest absolute value occurs max The time difference △th between i =t i -t max i = 1, 2, 3, ...;

[0010] Step S5: Based on the time difference Δth i After unifying the sampling rate in step S2, the airborne flight parameter normal overload during all takeoffs and landings is synchronized with the non-airborne measured load in time.

[0011] Step S6: After time synchronization, calculate the mean square error [MSE] of the linear regression between the airborne flight parameter normal overload and the non-airborne measured load for all takeoffs and landings after the unified sampling rate in Step S2. Gi ;

[0012] Step S7: From n [MSE] Gi Find the minimum value [MSE] in the given condition.Gk The corresponding overload point is the point where the maximum non-airborne measured load occurs, and the time when it occurs in the airborne system is denoted as t. k ;

[0013] Step S8: In the airborne flight parameter normal overload process where the sampling rate was unified in step S2, at t k Within a range of x seconds before and after, calculate the time difference Δth between each normal overload point and the moment when the maximum measured load occurs. j =t k ±j×t step -t max Based on this, the airborne flight parameter normal overload and non-airborne measured load of the full takeoff and landing after the unified sampling rate in step S2 are synchronized in time.

[0014] Step S9: After time synchronization, calculate the mean square error [MSE] of the linear regression between the airborne flight parameter normal overload and the non-airborne measured load for all takeoffs and landings after the unified sampling rate in Step S2. Lj ;

[0015] Step S10: Find the minimum [MSE] Lj The corresponding time is the time t when the maximum measured load is best matched. best According to the time difference Δth = t best -t max After unifying the sampling rate in step S2, the airborne flight parameter normal overload during all takeoffs and landings is synchronized with the non-airborne measured load in time.

[0016] Step S11: Plot the history of the changes of the airborne flight parameter normal overload and the measured load over time after time synchronization, and the correlation scatter plot of the two. Check whether the change patterns of the two are consistent after time synchronization. If they are inconsistent, check whether the filtering parameters in step S3 are reasonable, or adjust the range of x in step S8.

[0017] Step S12: After confirming that the time of the airborne flight parameters and the non-airborne measured loads are accurately synchronized, the relevant data is used as data samples for constructing the "flight parameter-load" model.

[0018] Furthermore, in step S2, the histories of airborne flight parameters and non-airborne measured loads are regenerated at the same sampling rate using two-point linear interpolation.

[0019] Furthermore, in step S3, the same method is used to perform intermediate value filtering on airborne flight parameters and non-airborne measured loads, and then small-amplitude filtering is performed using different parameters so that both retain only the main peak and valley points, and the remaining peak and valley points are sorted by size.

[0020] Furthermore, in step S4, it is assumed that the i-th overload point after filtering in step S3 is the overload point that causes the maximum measured load. Based on this, the time difference Δth between the computer-borne system and the non-airborne load testing system is calculated. i =t i -t max ; and in step S5, press △th i Synchronize the data between the two systems in time.

[0021] Furthermore, in steps S6 and S7, the mean square error of the linear regression between the full takeoff and landing airborne normal overload and the measured load after the unified sampling rate in step S2 is calculated, and the airborne normal overload point that causes the maximum measured load is determined by the minimum mean square error.

[0022] Furthermore, in steps S8, S9, and S10, during the airborne flight parameter normal overload process in step S2 where the sampling rate was unified, at t k Within a range of x seconds before and after, with t step Using the time step as the criterion and minimizing the mean square error (MSE) as the criterion, the normal overload point that best matches the maximum measured load is found locally. The value of x is related to the aircraft sampling rate and is generally taken as 1 to 5.

[0023] Furthermore, in step S11, the airborne flight parameter normal overload and the measured load change over time are used to determine whether the two are truly synchronized physically; the scatter plot of the two can be used to check whether the two have a good correlation.

[0024] Furthermore, in step S12, airborne flight parameters and non-airborne measured load data samples after time synchronization are used to construct a "flight parameter-load" model for components such as the wing and vertical tail.

[0025] The beneficial effects of this invention are:

[0026] This invention establishes an automatic time synchronization method for airborne flight parameters and non-airborne measured loads. This method eliminates the need to install overload sensors in the load spectrum testing system, saving costs and reducing data volume. It is highly efficient and accurate, avoiding the low efficiency of manually selecting the same marker points and the significant differences in time synchronization effects when different marker points are selected. It eliminates the phase difference between overload and load. By utilizing the correlation between the airborne flight parameter normal overload and the measured load, and comprehensively employing global feature matching and local optimal matching time optimization, it effectively solves the time synchronization problem of the two systems, obtains high-quality flight parameter and load data samples, and improves the prediction accuracy of the flight parameter-load model. Attached Figure Description

[0027] Figure 1 Flowchart of an automatic time synchronization method for airborne flight participants and non-airborne measured payloads

[0028] Figure 2 The airborne flight parameter normal overload history and the non-airborne measured wing bending moment history are shown in a certain load measurement mission during a takeoff and landing.

[0029] Figure 3 For a takeoff and landing data in a certain load measurement mission, after time synchronization is completed according to the present invention, the normalized history diagram of airborne flight parameter normal overload and non-airborne wing bending moment and the correlation scatter plot are shown.

[0030] Figure 4 The prediction performance of the "flight parameter-load" model constructed after obtaining flight parameter and load data samples through time synchronization using only overall feature matching;

[0031] Figure 5 The prediction performance of the "flight parameter-load" model constructed after using the flight parameter and load data samples acquired in time synchronization by integrating overall feature matching and local optimization in this invention is shown. Specific Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0033] Traditional methods involve installing a three-dimensional overload sensor near the aircraft's center of gravity, collecting normal overload history data using a load spectrum testing system, and then comparing it with normal overload history data from airborne flight parameters. Time synchronization is achieved by finding the same marker point between the two systems. However, this approach has several significant problems: First, it requires reading normal overload data from both systems separately and manually selecting the same marker point, which is inefficient and can lead to significantly different time alignment results if different marker points are chosen. Second, the addition of overload sensors increases testing costs and data volume. Third, the normal overload data from airborne flight parameters is generally from the cockpit, while the load spectrum testing system measures normal overload near the center of gravity; these differences cannot guarantee optimal time synchronization. Even when synchronizing at the same theoretical location, the phase difference between the overload and the measured load can still prevent achieving optimal synchronization. The overall technical process of this invention is as follows: Figure 1As shown, for the flight parameter data of the airborne system and the load measurement data of the non-airborne load test system, by unifying the sampling rate and filtering, based on the main peaks and valleys remaining after filtering, the minimum mean square error (MSE) of the linear regression between the airborne flight parameter normal overload and the non-airborne measured load is used as the index to macroscopically search for the airborne flight parameter normal overload point corresponding to the maximum non-airborne measured load, thus completing the overall feature matching; subsequently, within a range of x seconds near the found overall optimal normal overload point, the airborne flight parameter normal overload point corresponding to the maximum non-airborne measured load is further searched locally, thereby completing the automatic time synchronization between the airborne flight parameter and the non-airborne measured load.

[0034] In a specific implementation case, the above design is used to achieve automatic time synchronization of airborne flight participants with non-airborne measured payloads. The specific time synchronization process includes the following steps:

[0035] Step 1: Airborne flight parameter data extraction and non-airborne measured load calculation. Taking takeoff and landing as the unit, extract the airborne time and normal overload data from the data recorded by the airborne system; use the "strain-load" equation established based on the ground load calibration test data, substitute it with the load bridge measured data recorded by the non-airborne load test system, and calculate the bending moment, shear force, and torque history of components such as the wing and vertical tail;

[0036] Step 2: Unify the sampling rate: Using the same sampling rate (not less than 16Hz), the sampling rate of airborne flight parameters and non-airborne measured load data is unified by two-point linear interpolation, while retaining their respective time information;

[0037] Step 3: Perform intermediate value filtering and small amplitude filtering on the airborne normal overload and the non-airborne measured load respectively. The parameters for the small amplitude filtering are different for the two. After filtering, the main peak and valley points are retained and sorted by size.

[0038] Step 4: Suppose that the i-th overload point after filtering is the overload point that causes the maximum measured load. Calculate the time t at which the airborne normal overload occurs for the i-th (i = 1, 2, 3, ..., n) sorted point. i The time t when the non-airborne measured load with the largest absolute value occurs max The time difference △th between i =t i -t max ;

[0039] Step 5: Based on the time difference △th i After standardizing the sampling rate in step two, the airborne flight parameter normal overload and the off-airborne measured load during all takeoffs and landings are synchronized in time. In actual operation, the airborne systems are powered on immediately after the aircraft starts, and the off-airborne test systems are activated only afterward; that is, the airborne systems collect data before the off-airborne systems. Therefore, the time history of the airborne systems is uniformly reduced by Δth. iBy retaining the corresponding flight parameter data that is greater than 0 and less than the end time of the non-airborne system acquisition, the time synchronization between airborne flight parameters and non-airborne measured loads is completed.

[0040] Step Six: After time synchronization, calculate the mean square error [MSE] of the linear regression between the airborne flight parameter normal overload and the non-airborne measured load for all takeoffs and landings after the unified sampling rate in Step Two. Gi ;

[0041] Step 7: From n [MSE] Gi Find the minimum value [MSE] in the given condition. Gk The corresponding overload point is the point where the maximum non-airborne measured load occurs, and the time when it occurs in the airborne system is denoted as t. k ;

[0042] Step 8: In the airborne flight parameter normal overload process where the sampling rate was unified in Step 2, at t k Within a range of x seconds before and after, calculate the time difference Δth between each normal overload point and the moment when the maximum measured load occurs. j =t k ±j×t step -t max Based on this, the airborne flight parameter normal overload and non-airborne measured load of the full takeoff and landing after the unified sampling rate in step two are synchronized in time.

[0043] Step Nine: After time synchronization, calculate the mean square error [MSE] of the linear regression between the airborne flight parameter normal overload and the non-airborne measured load for all takeoffs and landings after the unified sampling rate in Step Two. Lj ;

[0044] Step 10: Find the minimum [MSE] Lj The corresponding time is the time t that best matches the maximum measured load. best According to the time difference Δth = t best -t max After unifying the sampling rate in step two, the airborne flight parameter normal overload and the non-airborne measured load are synchronized in time.

[0045] Step 11: Plot the time history of the airborne flight parameter normal overload and measured load after time synchronization, and the correlation scatter plot of the two. Check whether the change patterns of the two are consistent after time synchronization. If they are inconsistent, check whether the filtering parameters in Step 3 are reasonable, or adjust the range of x in Step 8. The value of x is related to the aircraft sampling rate, and is generally taken as 1 to 5.

[0046] Step 12: After confirming that the time of the airborne flight parameters and the non-airborne measured loads are accurately synchronized, the flight parameter data such as the total weight, altitude, Mach number, three-dimensional overload, three-dimensional angular velocity, three-dimensional angular acceleration, and control surface deflection angle, together with the non-airborne measured load data, constitute the data sample for constructing the "flight parameter-load" model.

[0047] In a specific implementation example of this invention, for step one, there is no communication between the aircraft's airborne system and the non-airborne load testing system, their time scales are not synchronized, and their sampling rates are different; the non-airborne load testing system is installed before the aircraft leaves the factory and is independent of the airborne system.

[0048] In a specific implementation example of this invention, in step two of the above time synchronization method, the histories of airborne flight parameters and non-airborne measured loads are regenerated using two-point linear interpolation at the same sampling rate (e.g., 16); the data lengths of the two systems are unified, and the data volume is compressed.

[0049] In the specific implementation process, the same method is used to perform intermediate value filtering on airborne flight parameters and non-airborne measured loads. Then, different parameters are used for small-amplitude filtering so that both retain only the main peak and valley points. The remaining peak and valley points are sorted by size. This can further compress the amount of data and reduce the workload of subsequent data processing.

[0050] In step four of the time synchronization method, let the i-th overload point after filtering in step three be the overload point that causes the maximum measured load. Based on this, the time difference Δth between the computer-borne system and the non-airborne load testing system is calculated. i =t i -t max ; and in step 5, press △th i Synchronize the data between the two systems in time;

[0051] In addition, in steps six and seven, the root mean square error of the linear regression between the full takeoff and landing airborne normal overload and the measured load after the unified sampling rate in step two is calculated. The airborne normal overload point that causes the maximum measured load is determined by the minimum root mean square error, and the time when it occurs in the airborne system is denoted as t. k This approach allows for the simultaneous feature synchronization of the two systems from a macroscopic perspective.

[0052] In steps eight, nine, and ten of the time synchronization method, during the airborne flight parameter normal overload process in step two, where the sampling rate was unified, at t k Within a range of x seconds before and after, with t step Using the time step as the criterion and minimizing the mean square error (MSE), the normal overload point that best matches the maximum measured load is found locally, and its time in the airborne system is denoted as t. bestThis approach can find the optimal time synchronization feature points between the two systems through local search.

[0053] In step ten, the time difference Δth = t between the computer-borne system and the non-airborne load test system. best -t max Based on this, the airborne flight data and non-airborne measured data are synchronized in time.

[0054] In step eleven, by using the history plots of airborne flight parameter normal overload and measured load over time, it can be determined whether the two are truly synchronized physically; by using the scatter plots of the two, it can be checked whether the two have a good correlation.

[0055] In step 12, time-synchronized airborne flight parameters and non-airborne measured load data samples are used to construct "flight parameter-load" models for components such as wings and vertical tail.

[0056] Implementation Case: Taking the takeoff and landing data from a load measurement mission as an example, such as... Figure 2 As shown, the time series recorded by the airborne system is completely different from that recorded by the non-airborne load test system. The airborne system starts collecting data earlier and ends collecting data later than the non-airborne load test system.

[0057] Through steps one through three, the main peaks and valleys of the remaining airborne flight parameter normal overload and non-airborne measured wing bending moment after filtering are as follows: Figure 2 As shown, the sampling rate is uniformly reduced to 16, which effectively compresses the amount of data and ensures that features are not lost. After filtering, the amount of data is further compressed and the main peaks and valleys are retained.

[0058] After completing steps four through ten to synchronize the time of airborne flight participation with non-airborne measured loads from both macroscopic feature matching and local optimization perspectives, a scatter plot of the correlation between the airborne flight participation and the non-airborne measured wing bending moment of the takeoff and landing aircraft is obtained, along with their normalized history curves over time. Figure 3 As can be seen, after time synchronization, the changing patterns of the two are highly consistent, indicating that the method of combining overall feature matching and local optimization in steps four through ten is correct and effective. Specifically, after finding the macroscopic time synchronization feature point, the optimal time synchronization point is searched locally within ±1 second of that feature point.

[0059] Traditional time synchronization methods, which rely solely on overall feature matching, produce a poor wing bending moment prediction "flight parameter-load" model trained based on acquired flight parameter and load samples. The predicted values ​​exhibit significant dispersion compared to measured values. Figure 4 As shown; the wing bending moment "flight parameter-load" model trained using the flight parameter and load data samples obtained by the method of this invention has a very good predictive effect on wing bending moment, as shown. Figure 5 As shown.

[0060] The present invention specifically proposes an automatic time synchronization method for airborne flight parameters and non-airborne measured loads. It eliminates the need to install overload sensors in the load spectrum testing system. By utilizing the correlation between the airborne flight parameter normal overload and the measured load, and comprehensively employing global feature matching and local optimal matching time optimization, it effectively solves the time synchronization problem of the two systems, obtains high-quality flight parameter and load data samples, and improves the prediction accuracy of the flight parameter-load model.

[0061] The above description is merely one specific embodiment of the present invention, providing a detailed description of the invention. Parts not covered herein are conventional techniques. However, the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for automatic time synchronization of airborne flight participants and non-airborne measured loads, characterized in that: For the flight parameter data of the airborne system and the measured load data of the non-airborne load test system, by unifying the sampling rate and filtering, and based on the main peaks and valleys remaining after filtering, the minimum mean square error (MSE) of the linear regression between the airborne flight parameter normal overload and the non-airborne measured load is used as an indicator to macroscopically search for the airborne flight parameter normal overload point corresponding to the maximum non-airborne measured load, thus completing the overall feature matching; subsequently, within a range of x seconds near the found overall optimal normal overload point, the airborne flight parameter normal overload point corresponding to the maximum non-airborne measured load is further searched locally, thereby completing the automatic time synchronization between the airborne flight parameter and the non-airborne measured load; Time synchronization methods include the following steps: Step S1: Airborne flight parameter data extraction and non-airborne measured load calculation: Extract the airborne time and normal overload data from the data recorded by the airborne system; use the "strain-load" equation established by ground load calibration, and calculate the bending moment, shear force, and torque load history of key aircraft components based on the load bridge data recorded by the non-airborne load test system; Step S2: Unify sampling rate: Unify the sampling rate of airborne flight parameters and non-airborne measured loads, and retain their respective time information; Step S3: Filtering: Perform intermediate value filtering and small amplitude filtering on the airborne normal overload and the non-airborne measured load respectively, retain the main peak and valley points, and sort them by size; Step S4: Calculate the time t when the i-th airborne normal overload occurs sequentially. i The time t when the non-airborne measured load with the largest absolute value occurs max Time difference between △th i =t i -t max ;i=1,2,3,…; Step S5: Based on the time difference Δth i After unifying the sampling rate in step S2, the airborne flight parameter normal overload during all takeoffs and landings is synchronized with the non-airborne measured load in time. Step S6: After time synchronization, calculate the mean square error [MSE] of the linear regression between the airborne flight parameter normal overload and the non-airborne measured load for all takeoffs and landings after the unified sampling rate in Step S2. Gi ; Step S7: From n [MSE] Gi Find the minimum value [MSE] in the given condition. Gk The corresponding overload point is the point where the maximum non-airborne measured load occurs, and the time when it occurs in the airborne system is denoted as t. k ; Step S8: In the airborne flight parameter normal overload process where the sampling rate was unified in step S2, at t k Within a range of x seconds before and after, calculate the time difference Δth between each normal overload point and the moment when the maximum measured load occurs. j =t k ±j×t step -t max Based on this, the airborne flight parameter normal overload and non-airborne measured load of the full takeoff and landing after the unified sampling rate in step S2 are synchronized in time. Step S9: After time synchronization, calculate the mean square error [MSE] of the linear regression between the airborne flight parameter normal overload and the non-airborne measured load for all takeoffs and landings after the unified sampling rate in Step S2. Lj ; Step S10: Find the minimum [MSE] Lj The corresponding time is the time t when the maximum measured load is best matched. best According to the time difference Δth=t best -t max After unifying the sampling rate in step S2, the airborne flight parameter normal overload during all takeoffs and landings is synchronized with the non-airborne measured load in time. Step S11: Plot the history of the changes of the airborne flight parameter normal overload and the measured load over time after time synchronization, and the correlation scatter plot of the two. Check whether the change patterns of the two are consistent after time synchronization. If they are inconsistent, check whether the filtering parameters in step S3 are reasonable, or adjust the range of x in step S8. Step S12: After confirming that the time of the airborne flight parameters and the non-airborne measured loads are accurately synchronized, the relevant data is used as data samples for constructing the "flight parameter-load" model.

2. The automatic time synchronization method for airborne flight participants and non-airborne measured loads according to claim 1, characterized in that: In step S2, the histories of airborne flight parameters and non-airborne measured loads are regenerated using two-point linear interpolation at the same sampling rate.

3. The automatic time synchronization method for airborne flight participants and non-airborne measured loads according to claim 1, characterized in that: In step S3, the same method is used to perform intermediate value filtering on airborne flight parameters and non-airborne measured loads. Then, different parameters are used for small-amplitude filtering so that both retain only the main peak and valley points, and the remaining peak and valley points are sorted by size.

4. The automatic time synchronization method for airborne flight participants and non-airborne measured loads according to claim 1, characterized in that: In step S4, it is assumed that the i-th overload point after filtering in step S3 is the overload point that causes the maximum measured load. Based on this, the time difference Δth between the computer-borne system and the non-airborne load testing system is calculated. i =t i -t max ; and in step S5, press △th i Synchronize the data between the two systems in time.

5. The automatic time synchronization method for airborne flight participants and non-airborne measured loads according to claim 1, characterized in that: In steps S6 and S7, the mean square error of the linear regression between the full takeoff and landing airborne normal overload and the measured load after the unified sampling rate in step S2 is calculated, and the airborne normal overload point that causes the maximum measured load is determined by the minimum mean square error.

6. The automatic time synchronization method for airborne flight participants and non-airborne measured loads according to claim 1, characterized in that: In steps S8, S9, and S10, during the airborne flight parameter normal overload process in step S2 where the sampling rate was unified, at t k Within a range of x seconds before and after, with t step Using the time step as the criterion and the minimum mean square error (MSE) as the indicator, the normal overload point that best matches the maximum measured load is found locally.

7. The automatic time synchronization method for airborne flight participants and non-airborne measured loads according to claim 1 or 6, characterized in that: The value of x is related to the aircraft sampling rate and ranges from 1 to 5.

8. The automatic time synchronization method for airborne flight participants and non-airborne measured loads according to claim 1, characterized in that: In step S11, the airborne flight parameter normal overload and the measured load change over time are used to determine whether the two are truly synchronized physically; the scatter plot of the two can be used to check whether the two have a good correlation.

9. The method according to claim 1, characterized in that: In step S12, the time-synchronized airborne flight parameters and non-airborne measured load data samples are used to construct the "flight parameter-load" model of the wing and vertical tail components.

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