A method for compiling a ground severe taxiing severity spectrum of an aircraft based on a Gaussian process

Abstract

The application discloses a kind of based on Gaussian process's aircraft ground serious taxiing severity spectrum compilation method, comprising the following steps: S1, clear aircraft mission profile: according to the use requirement of aircraft, obtain the composition and proportion of mission profile;For each mission profile, clear the composition, order and proportion of the task section constituting mission profile and parameters;S2, load data preparation, pre-processing is carried out to measured load data, including filtering, overload standardization;S3, the root mean square intensity σ w of ground taxiing overload time is calculated;S4, the root mean square intensity of each landing ground taxiing is analyzed, and the root mean square intensity σ w of reliability 90% is determined;S5, ground serious taxiing severity spectrum is compiled.

Description

Technical Field

[0001] This invention relates to the field of load spectrum compilation technology, and more specifically to a method for compiling the severity spectrum of severe taxiing of an aircraft on the ground based on a Gaussian process. Background Technology

[0002] Currently, the ground taxiing load spectrum refers to the spectrum compiled from the load time history experienced by the aircraft during ground taxiing missions. It reflects the fatigue load borne by the entire aircraft and its various components. The overload spectrum, on the other hand, is the smallest basic unit of the load spectrum, representing the most specific load state response of the aircraft during various missions and directly reflecting the severity of the loads borne. In the later stages of aircraft model design finalization, at least one aircraft needs to be selected from the small batch of aircraft undergoing lead flight testing. Through specialized testing modifications and flight tests, a measured flight load spectrum is compiled to determine and verify the aircraft's design service life, serving as a prerequisite for determining and extending the lifespan of aircraft structures due to fatigue. The ground taxiing spectrum is the primary source of damage in the aircraft's ground spectrum; therefore, compiling a measured ground taxiing overload spectrum is of great significance for extending the lifespan of military and civilian aircraft.

[0003] However, both the average spectrum and the severity spectrum are typical representatives of the load spectrum of an aircraft fleet. Their purpose is to be used in the design phase for aircraft structural durability analysis and testing, to verify whether the aircraft structure meets the design service life requirements, and to identify critical hazardous parts and maintenance plans. The severity spectrum has the advantage of exposing the aircraft's own failure characteristics and reducing testing time; therefore, its application in the compilation of measured spectra is increasing. The concept, compilation method, and life analysis method under the average spectrum are relatively mature, but several key technologies regarding the severity spectrum remain to be solved, and there is no publicly available research data on it, either domestically or internationally.

[0004] Therefore, proposing a method for compiling the severity spectrum of ground taxiing of aircraft that fully considers ground dispersion while ensuring safety and economy is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a method for compiling a severe spectrum of aircraft ground taxiing based on a Gaussian process; by providing a method for compiling a measured severe spectrum of aircraft ground taxiing that takes into account the dispersion of ground taxiing and the characteristics of the ground taxiing load spectrum (random fluctuations around 1g), the method can reduce the fatigue test time when compiling the severe spectrum, and the dispersion coefficient used when determining the life only needs to consider the dispersion coefficient of the structure.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for compiling the severity spectrum of severe taxiing on the ground for aircraft based on Gaussian processes includes the following steps:

[0008] S1. Define the aircraft mission profile: Based on the aircraft's operational requirements, obtain the composition and proportion of the mission profile; for each mission profile, define the composition, order, proportion, and parameters of the mission segments that constitute the mission profile.

[0009] S2. Load data preparation: preprocess the measured load data, including filtering and overload standardization.

[0010] S3. Calculate the root mean square strength σ of the ground sliding overload time. w ;

[0011] S4. Analyze the distribution characteristics of the root mean square strength of each landing surface during taxiing, and determine the root mean square strength σ with a reliability of 90%. w ;

[0012] S5. Compile a severe spectrum of ground skidding.

[0013] Preferably, step S1 specifically includes:

[0014] Determine the aircraft's mission profile, mission profile scale, and mission profile composition. Provide mission profile parameters, including: mission segments, mission segment altitude, speed, weight, flight distance, and flight time. Divide the measured data into mission segments according to the following principles:

[0015] 1) The takeoff taxiing start point is determined by the speed being greater than 0 after the engine test is completed;

[0016] 2) The takeoff taxiing end point is determined by the complete departure of all three landing gears from the ground;

[0017] 3) The landing taxiing start point is determined by the main landing gear contacting the ground;

[0018] 4) The landing taxiing end point is determined by a yaw angle greater than 10°.

[0019] Preferably, step S2 specifically includes:

[0020] S21, Regarding overload data n y Standardization processing

[0021] Δn y0 =Δn yi *(G i / G0)

[0022] Where, Δn y0 The overload value is the corrected value according to the standard task section specifications; Δn yi G represents the measured overload value. i For true mass, G0 represents the standard aircraft mass;

[0023] S22. Perform peak-valley value detection on the measured overload value and determine whether the following conditions are met. If so, take the value once:

[0024] or

[0025] S23. Filter and compress the collected peak and valley values:

[0026] like Then take

[0027] like and Remove And on and Perform linear interpolation;

[0028] like and Remove

[0029] Specifically, the first valley value of the collected ground sliding overload is taken as the first... That is, filtering and discrimination are performed starting from k=2.

[0030] Preferably, step S3 specifically includes:

[0031] The taxiing load time history for each mission segment is divided into segments based on speed, in m / s. Assuming the ground taxiing overload history within a specified speed range is simplified to an independent stationary Gaussian random process, then for each taxi of the aircraft, the root mean square strength σ... w To describe the degree of deviation from the average:

[0032]

[0033] Preferably, step S4 specifically includes:

[0034] Let σ be the specified speed range for each task segment. w The distribution follows one of the following: normal distribution, log-normal distribution, or two-parameter Weibull distribution. Based on the measured data sample, the goodness-of-fit test of the distribution function is performed using the probability coordinate regression method to determine the optimal distribution.

[0035] σ under the specified task segment w The samples are arranged in ascending order as σ w (i = 1, 2, ..., n), according to the rank statistics method, σ wi The corresponding probability is

[0036]

[0037] Where i represents σ w The i-th sample after sorting from smallest to largest; n represents the number of samples;

[0038] 1) Normal distribution

[0039] σ wi The corresponding empirical frequency value P i and standard normal distribution quantile μ p,i ;(σ wi ,μ p,i The data pairs can be linearized as shown below.

[0040] σ w =μ+μ p σ

[0041] Based on (σ) wi ,μ p,i The data were fitted to calculate the correlation coefficient r, and the distribution characteristics were tested.

[0042] 2) Log-normal distribution

[0043] Get lgσ wi The corresponding empirical probability value P i and quantile μ p,i ;(lgσ wi ,μ p,i The data pairs can be linearized as shown below.

[0044] lgσ wi =μ i +μ p σ i

[0045] By calculating (lgσ) wi ,μ p,i The correlation coefficient r of the data pairs was tested for distribution characteristics.

[0046] 3) Weibull distribution

[0047] The two-parameter Weibull is shown below.

[0048] lg[1-lg(1-P)]=αlgσ w -αlgβ

[0049] By calculating (lg[1-lg(1-P)],lgσ) wi The correlation coefficient r of the data pairs was tested for distribution characteristics.

[0050] Comparing the correlation coefficients r under the three distributions, and considering the distribution function graphs, the distribution function with the largest correlation coefficient is selected as the optimal distribution, which clearly indicates σ. w Distribution characteristics;

[0051] With σ w Taking a normal distribution as an example, calculate σ for a coverage rate of 90%. w :

[0052]

[0053] Where, μ i σ i σ of level i w The corresponding log-median and standard deviation of the exceedance number; σ corresponding to 90% coverage. w Subsequently, a Gaussian process regression model for the standard time series under this mission segment was established to predict the severe ground skidding overload spectrum under this mission segment.

[0054] Power spectral density analysis was performed on the ground taxiing overload history of each speed range under each mission segment. The corresponding power spectral density image was plotted using the periodogram method, and the frequency with the highest (peak) power spectral density was selected. The frequency of the overload time history within this speed range is used to determine the number of severe spectral load cycles, k.

[0055] Preferably, step S5 specifically includes:

[0056] Let T0 be the standard time for a certain speed range in each task segment of ground taxiing, where T0 = kt / 2. Then, the time series of severe ground taxiing overload for each task segment (Δn) is as follows. yi (i = 1, 2, ..., k), where Δn yi ~N(0,σ w,i For each Δn yi Randomly select a random number u that follows a standard normal distribution. p ~N(0,1),

[0057] ΔN i =μ pi *σ w90,i

[0058] Then the ground skidding overload spectrum in this speed range is (|Δn yi |-|Δn yi+1 |)(i=1,3,5,…..k), repeat the above process until the ground taxiing overload spectrum of all sections under this task segment is compiled, and then the overload spectrum of all task segments is compiled.

[0059] As can be seen from the above technical solutions, compared with the prior art, the present invention discloses a method for compiling the severity spectrum of aircraft ground severe taxiing based on Gaussian process; by providing a method for compiling the measured severity spectrum of aircraft ground taxiing that considers the dispersion of ground taxiing and the characteristics of the ground taxiing load spectrum (random fluctuations around 1g), the method can reduce the fatigue test time when compiling the severity spectrum, and the dispersion coefficient used when determining the life only needs to consider the dispersion coefficient of the structure. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0061] Figure 1 The attached figure is a schematic diagram of the method flow structure provided in an embodiment of the present invention.

[0062] Figure 2 The attached figure shows the σ provided in the embodiment of the present invention. w A schematic diagram of the QQ structure.

[0063] Figure 3 The attached figure is a schematic diagram of the severe skidding spectrum structure in the landing skidding (160-110) speed range provided in an embodiment of the present invention. Detailed Implementation

[0064] 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] This invention discloses a method for compiling the severity spectrum of severe taxiing on the ground for aircraft based on Gaussian processes, comprising the following steps:

[0066] S1. Define the aircraft mission profile: Based on the aircraft's operational requirements, obtain the composition and proportion of the mission profile; for each mission profile, define the composition, order, proportion, and parameters of the mission segments that constitute the mission profile.

[0067] S2. Load data preparation: preprocess the measured load data, including filtering and overload standardization.

[0068] S3. Calculate the root mean square strength σ of the ground sliding overload time. w ;

[0069] S4. Analyze the distribution characteristics of the root mean square strength of each landing surface during taxiing, and determine the root mean square strength σ with a reliability of 90%. w ;

[0070] S5. Compile a severe spectrum of ground skidding.

[0071] To further optimize the above technical solution, step S1 specifically includes:

[0072] Determine the aircraft's mission profile, mission profile scale, and mission profile composition. Provide mission profile parameters, including: mission segments, mission segment altitude, speed, weight, flight distance, and flight time. Divide the measured data into mission segments according to the following principles:

[0073] 1) The takeoff taxiing start point is determined by the speed being greater than 0 after the engine test is completed;

[0074] 2) The takeoff taxiing end point is determined by the complete departure of all three landing gears from the ground;

[0075] 3) The landing taxiing start point is determined by the main landing gear contacting the ground;

[0076] 4) The landing taxiing end point is determined by a yaw angle greater than 10°.

[0077] To further optimize the above technical solution, step S2 specifically includes:

[0078] S21, Regarding overload data n y Standardization processing

[0079] Δn y0 =Δn yi *(G i / G0)

[0080] Where, Δn y0 The overload value is the corrected value according to the standard task section specifications; Δn yi G represents the measured overload value. i For true mass, G0 represents the standard aircraft mass;

[0081] S22. Perform peak-valley value detection on the measured overload value and determine whether the following conditions are met. If so, take the value once:

[0082] or

[0083] S23. Filter and compress the collected peak and valley values:

[0084] like Then take

[0085] like and Remove And on and Perform linear interpolation;

[0086] like and Remove

[0087] Specifically, the first valley value of the collected ground sliding overload is taken as the first... That is, filtering and discrimination are performed starting from k=2.

[0088] To further optimize the above technical solution, step S3 specifically includes:

[0089] The taxiing load time history for each mission segment is divided into segments based on speed, in m / s. Assuming the ground taxiing overload history within a specified speed range is simplified to an independent stationary Gaussian random process, then for each taxi of the aircraft, the root mean square strength σ... w To describe the degree of deviation from the average:

[0090]

[0091] To further optimize the above technical solution, step S4 specifically includes:

[0092] Let σ be the specified speed range for each task segment. w The distribution follows one of the following: normal distribution, log-normal distribution, or two-parameter Weibull distribution. Based on the measured data sample, the goodness-of-fit test of the distribution function is performed using the probability coordinate regression method to determine the optimal distribution.

[0093] σ under the specified task segment w The samples are arranged in ascending order as σ w (i = 1, 2, ..., n), according to the rank statistics method, σ wi The corresponding probability is

[0094]

[0095] Where i represents σ w The i-th sample after sorting from smallest to largest; n represents the number of samples;

[0096] 1) Normal distribution

[0097] σ wi The corresponding empirical frequency value P i and standard normal distribution quantile μ p,i ;(σ wi ,μ p,iThe data pairs can be linearized as shown below.

[0098] σ w =μ+μ p σ

[0099] Based on (σ) fi ,μ p,i The data were fitted to calculate the correlation coefficient r, and the distribution characteristics were tested.

[0100] 2) Log-normal distribution

[0101] Get lgσ wi The corresponding empirical probability value P i and quantile μ p,i ;(lgσ wi ,μ p,i The data pairs can be linearized as shown below.

[0102] lgσ wi =μ i +μ p σ i

[0103] By calculating (lgσ) wi ,μ p,i The correlation coefficient r of the data pairs was tested for distribution characteristics.

[0104] 3) Weibull distribution

[0105] The two-parameter Weibull is shown below.

[0106] lg[1-lg(1-P)]=αlgσ w -αlgβ

[0107] By calculating (lg[1-lg(1-P)],lgσ) wi The correlation coefficient r of the data pairs was tested for distribution characteristics.

[0108] Comparing the correlation coefficients r under the three distributions, and considering the distribution function graphs, the distribution function with the largest correlation coefficient is selected as the optimal distribution, which clearly indicates σ. w Distribution characteristics;

[0109] With σ w Taking a normal distribution as an example, calculate σ for a coverage rate of 90%. w :

[0110]

[0111] Where, μ i σ i σ of level i wThe corresponding log-median and standard deviation of the exceedance number; σ corresponding to 90% coverage. w Subsequently, a Gaussian process regression model for the standard time series under this mission segment was established to predict the severe ground skidding overload spectrum under this mission segment.

[0112] Power spectral density analysis was performed on the ground taxiing overload history of each speed range under each mission segment. The corresponding power spectral density image was plotted using the periodogram method, and the frequency with the highest (peak) power spectral density was selected. The frequency of the overload time history within this speed range is used to determine the number of severe spectral load cycles, k.

[0113] To further optimize the above technical solution, step S5 specifically includes:

[0114] Let T0 be the standard time for a certain speed range in each task segment of ground taxiing, where T0 = kt / 2. Then, the time series of severe ground taxiing overload for each task segment (Δn) is as follows. yi (i = 1, 2, ..., k), where Δn yi ~N(0,σ w For each Δn yi Randomly select a random number u that follows a standard normal distribution. p ~N(0,1),

[0115] ΔN i =μ pi *σ w90,i

[0116] Then the ground skidding overload spectrum in this speed range is (|Δn yi |-|Δn yi+1 |)(i=1,3,5,…..k), repeat the above process until the ground taxiing overload spectrum of all sections under this task segment is compiled, and then the overload spectrum of all task segments is compiled.

[0117] The typical mission profile of a certain type of aircraft is taken as medium-altitude flight. The calculation is based on 1000 takeoffs and landings.

[0118] Table 1 shows the task sections of the hollow profile.

[0119] takeoff taxiing 2.1 landing and gliding 2.6

[0120] A total of 30 takeoffs and landings of a certain aircraft were measured, and the flight data provided included time, flight altitude, Y-axis overload at the center of gravity, remaining fuel in the left / right engines, flap deflection angle, and elevator deflection angle.

[0121] The standard aircraft mass data for the mission segment is shown in the table below. The actual load mass data is calculated by subtracting fuel consumption from the aircraft weight. Fuel consumption is calculated as an overall average (the average fuel consumption is calculated from the start of takeoff taxiing to the end of landing impact). The aircraft center of gravity y-axis overload data is standardized according to the method in Section 4.2.1.

[0122] Table 2 Standard Quality for Each Task Segment

[0123] takeoff taxiing 25000 landing and gliding 13000

[0124] Before performing the counting statistics, the peak and valley values ​​of the statistical parameters were detected according to the method in Section 4.2.3 to obtain the peak and valley value data pairs of ground skid overload (Δn). y峰 ,Δn y谷 ) i .

[0125] The method in 4.3.1 was used to perform statistical processing on all speed ranges under all mission segments. The root mean square intensity value of the load time history for the landing taxiing mission segments of each takeoff and landing in the range of (160~110 m / s) is shown in Appendix 1.

[0126] Appendix 1 0.073572 0.095518 0.09663 0.078104 0.081356 0.085149 0.089314 0.088591 0.087243 0.09277 0.07585 0.072047 0.076071 0.081297 0.072104 0.082013 0.064367 0.093947 0.081619 0.069835 0.07843 0.077504 0.086558 0.071963 0.073708 0.074238 0.0638 0.069232 0.073595

[0156] The root mean square strength σ of the load time history for each velocity range of all mission segments w Perform distribution characteristic tests to select the optimal distribution.

[0157] The landing taxiing mission speed (in m / s) is represented by the root mean square intensity σ of the load time history within the range of (160–110). w The correlation coefficients of the various distributions are shown in the table below:

[0158] Table 3 Standard Quality for Each Task Segment

[0159] r 0.9908 0.9844 0.9226

[0160] From the table above, we can see that σ w The optimal distribution is a normal distribution, and the corresponding QQ plot is shown in the figure below.

[0161] Using the method in 4.3.3, the root mean square intensity distribution parameters of each speed range for all mission segments were calculated. For the landing taxiing mission segment with speeds (in m / s) in the range of (160~110), μ and σ were 0.0795 and 0.0090, respectively.

[0162] Based on the distribution function and distribution parameters given in 5.4.3, calculate σ for a coverage rate of 90%. w The value is 0.0910.

[0163]

[0164] Where, μ i σ i σ of level i w The corresponding log-median and standard deviation of the exceedance number. This is used to obtain the σ corresponding to 90% coverage. w Subsequently, a Gaussian process regression model for the standard time series under this mission segment was established to predict the severe ground skidding overload spectrum for that mission segment. Following this method, severe ground skidding overload spectra for all mission segments were compiled.

[0165] Power spectral density analysis was performed on the ground taxiing overload history of each speed range under each mission segment, and the frequency with the highest corresponding power spectral density was selected. This frequency serves as the overload time history within that speed range.

[0166] Power spectral density analysis was performed on the ground taxiing overload history for landing speeds (in m / s) in the range of 160–110. The peak power spectral density occurred at ω = 0.302.

[0167] Using the method in 4.4.3, a severe ground skidding spectrum was compiled, where the standard time for the landing skidding mission segment speed (m / s) range (160–110) was 30 s. From 4.4.3, k = 18. The severe ground skidding spectrum for this speed range obtained through random sampling is shown below. Figure 3 As shown.

[0168] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0169] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for compiling the severity spectrum of severe taxiing on aircraft based on Gaussian processes, characterized in that, Includes the following steps: S1. Define the aircraft mission profile: Based on the aircraft's operational requirements, obtain the composition and proportion of the mission profile; for each mission profile, define the composition, order, proportion, and parameters of the mission segments that constitute the mission profile. S2. Load data preparation: preprocess the measured load data, including filtering and overload standardization. S3. Calculate the root mean square strength of the ground sliding overload time. ; Step S3 specifically includes: The taxiing load time history for each mission segment is divided into segments based on speed, in m / s. Assuming the ground taxiing overload history within a specified speed range is simplified to an independent stationary Gaussian random process, then for each taxi of the aircraft, the root mean square strength... To describe the degree of deviation from the average: ; S4. Analyze the distribution characteristics of the root mean square strength of each landing surface during taxiing, and determine the root mean square strength with a reliability of 90%. ; Step S4 specifically includes: Let each task segment have a specified speed range. The distribution follows one of the following: normal distribution, log-normal distribution, and two-parameter Weibull distribution. Based on the measured data sample, the goodness-of-fit test of the distribution function is performed using the probability coordinate regression method to determine the optimal distribution. Under the specified task segment The samples are arranged in ascending order. According to the rank statistical method, The corresponding probability is Where i represents the The i-th sample after sorting from smallest to largest; n represents the number of samples; 1) Normal distribution get Corresponding empirical frequency value and standard normal distribution quantiles ; The data pairs can be linearized as shown below. ； based on The correlation coefficient r of the data was fitted and its distribution characteristics were tested. 2) Log-normal distribution get Corresponding empirical probability value and quantiles ; The data pairs can be linearized as shown below. ； Through calculation The distribution characteristics of the correlation coefficient r between the data pairs were tested. 3) Weibull distribution The two-parameter Weibull is shown below. ； Through calculation The distribution characteristics of the correlation coefficient r between the data pairs were tested. Comparing the correlation coefficients r under the three distributions, and considering the distribution function graphs, the distribution function with the largest correlation coefficient is selected as the optimal distribution, which clearly indicates σ. w Distribution characteristics; The calculation coverage is 90%. : ； in, σ of level i w The corresponding log-median and standard deviation of the exceedance number; σ corresponding to 90% coverage. w Subsequently, a Gaussian process regression model for the standard time series under this mission segment was established to predict the severe ground skidding overload spectrum under this mission segment. Power spectral density analysis was performed on the ground taxiing overload history of each speed range under each mission segment. The corresponding power spectral density image was plotted using the periodogram method, and the frequency with the highest (peak) power spectral density was selected. The frequency of the overload time history within this speed range is used to determine the number of severe spectral load cycles, k. S5. Compile a severe spectrum of ground skidding.

2. The method for compiling the severity spectrum of severe taxiing on the ground based on Gaussian processes according to claim 1, characterized in that, Step S1 specifically includes: Determine the aircraft's mission profile, mission profile scale, and mission profile composition. Provide mission profile parameters, including: mission segments, mission segment altitude, speed, weight, flight distance, and flight time. Divide the measured data into mission segments according to the following principles: 1) The takeoff taxiing start point is determined by the speed being greater than 0 after the engine test is completed; 2) The takeoff taxiing end point is determined by the complete departure of all three landing gears from the ground; 3) The landing taxiing start point is determined by the main landing gear contacting the ground; 4) The landing taxiing end point is determined by a yaw angle greater than 10°.

3. The method for compiling the severity spectrum of severe taxiing on the ground based on Gaussian processes according to claim 1, characterized in that, Step S2 specifically includes: S21, Overload data Standardization processing ； in, The overload value is the corrected value according to the standard task section specification; This is the measured overload value. For true quality, Standard aircraft mass; S22. Perform peak-valley value detection on the measured overload value and determine whether the following conditions are met. If so, take the value once: or ; S23. Filter and compress the collected peak and valley values: like Then take ; like and Then remove , and for (t2, ) and (t3, Perform linear interpolation; like and Then remove , ; Specifically, the first valley value of the collected ground sliding overload is taken as the first... That is, filtering and discrimination are performed starting from k=2.

4. The method for compiling the severity spectrum of severe taxiing on the ground based on Gaussian processes according to claim 1, characterized in that, Step S5 specifically includes: Let T0 be the standard time for a certain speed range in each task segment of ground taxiing, where T0 = kt / 2. Then, the time series of severe ground taxiing overload for each task segment (Δn) is as follows. yi (i=1,2,…,k), where Δn yi ~N(0, σ w90,i For each Δn yi Randomly select a random number u that follows a standard normal distribution. p ~N(0,1), ； Then the ground skidding overload spectrum in this speed range is ( (i=1,3,5,…..k), repeat the above process until the ground taxiing overload spectrum of all sections under this task segment is compiled, and then the overload spectrum of all task segments is compiled.

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