Impact environment waveform reconstruction method and system for aerial gun simulation test

Abstract

This invention provides a method and system for reconstructing waveforms of an impact environment in an aircraft cannon simulation test. The method includes the following steps: S1, calculating the initial predicted spectrum of the period and random signal based on the basic parameters of the aircraft cannon; S2, constructing a sinusoidal signal and a pseudo-random signal; S3, generating a true random signal; S4, superimposing the sinusoidal signal constructed in S2 with the true random signal constructed in S3, and modulating the superimposed time-domain signal to obtain a modulated time-domain waveform signal C; S5, calculating the error E between the estimated spectrum and the predicted spectrum of the period and random signal in S1; S6, compensating the modulated time-domain waveform signal C in step S4 to obtain a compensated time-domain waveform signal; S7, repeating step S6 until the error in the error sequence E is lower than a set error threshold to obtain the final time-domain waveform signal. This method can effectively reproduce the repetitive impact characteristics of the actual artillery impact environment and has a very good simulation effect.

Description

Technical Field

[0001] This invention relates to the field of aircraft cannon technology, and specifically to a method and system for reconstructing shock environment waveforms for aircraft cannon simulation tests. Background Technology

[0002] The impact environment generated by the continuous firing of small-caliber aircraft cannons is one of the important factors affecting the structure and function of equipment around the cannon. The impact environment, induced by the continuous firing of aircraft cannons, is characterized by high-magnitude, short-duration, and repetitive impacts from the muzzle pressure and the mechanical motion of the cannon. The maximum acceleration can reach hundreds of Gs, leading to numerous malfunctions such as structural fractures, disconnected electrical connectors, electrical performance failures, hydraulic failures, conduit cracking, and broken or falling pointers on instrument mounting components. These issues severely impact the safe use and operation of the equipment.

[0003] Equipment equipped with aircraft cannons undergoes severe artillery impact during training and use. Therefore, it is necessary to conduct laboratory tests on equipment exposed to artillery impact during the equipment development and finalization stages to assess its ability to withstand such environments and to make design improvements to address weaknesses. Currently, domestic testing primarily follows GJB 150.20-86 "Environmental Test Methods for Military Equipment: Aircraft Gun Vibration Test" and GJB 150.20A-2009 "Laboratory Environmental Test Methods for Military Equipment: Part 20: Artillery Vibration Test" Procedure IV, using sinusoidal vibration plus random signal (SOR) or narrowband random plus broadband random (NOR) signals. However, these methods generate test waveforms based on stationary random vibration, resulting in a near-steady-state test environment. This fails to simulate the time-varying, short-period, non-stationary, and non-Gaussian repetitive impact characteristics of artillery response, significantly deviating from the real artillery environment. Consequently, insufficient testing often leads to inadequate assessment, resulting in frequent malfunctions in actual field use even after passing laboratory tests using these methods. Summary of the Invention

[0004] The purpose of this invention is to provide a waveform reconstruction method for impact environment simulation tests of aircraft guns. This method enables the simulation of artillery impact to reproduce the time-varying, short-period, non-stationary, and non-Gaussian repetitive impact characteristics of the actual artillery impact environment, thus providing a better simulation effect for the actual artillery impact environment.

[0005] Specifically, the present invention provides a waveform reconstruction method for an impact environment used in an aircraft cannon simulation test, comprising the following steps:

[0006] S1. Calculate the period and the initial predicted spectrum of the random signal based on the basic parameters of the aircraft cannon;

[0007] S2. Construct a sinusoidal signal based on the periodic characteristics in the initial predicted spectrum of the periodic and random signals, and construct a pseudo-random signal based on the random characteristics using inverse Fourier transform.

[0008] S3. Perform time-domain randomization processing on the pseudo-random signal to optimize and improve the signal phase information and generate a true random signal.

[0009] S4. Superimpose the sinusoidal signal constructed in S2 with the true random signal constructed in S3. Use the time-domain windowed pulse method to select an appropriate window function to modulate the waveform of the superimposed time-domain signal, and obtain the modulated time-domain waveform signal C.

[0010] S5. Estimate the power spectral density of the modulated time-domain waveform signal to obtain the estimated spectrum, and calculate the error E between the estimated spectrum and the predicted spectrum of the periodic and random signals in S1. This includes the following sub-steps:

[0011] S51. Define the periodic and random predicted spectrum as the spectrum over the frequency bandwidth [f1, f2]. n The amplitude is [B1, B] n If a finite sequence is composed of f1, f2, ..., f3, then the estimated spectrum is given by the frequency bandwidth [f1, f3, ..., f4]. n The amplitude is [A1, A] n A finite sequence consisting of [a number of units], where the frequency f is in Hz and the amplitude is in g. 2 / Hz;

[0012] S52, For frequency bandwidth [f1, f n Any frequency point f within ] s (1≤S≤n), calculate f s The corresponding estimated spectral amplitude A s Relative to periodic and random predicted spectral amplitude B s Error E s The calculation formula is:

[0013] E s =10*lg(A) s / B s );

[0014] S53. Calculate the frequency bandwidth [f1, f1] one by one according to step S52. n The errors of the estimated spectral amplitudes for all frequency points within the range relative to the predicted spectral amplitudes of periodic and random signals are calculated, and the error sequence E is established as follows:

[0015] E = (E1, E2, ... E n )

[0016] In the formula, E1, E2, ... E nThese represent the errors of the estimated spectral amplitudes relative to the predicted spectral amplitudes of periodic and random signals, respectively, for the first, second...nth frequency points.

[0017] S6. When the error at certain frequency points in the error sequence E exceeds the set error threshold, the time-domain waveform signal C modulated in step S4 is compensated to obtain the compensated time-domain waveform signal. This specifically includes the following sub-steps:

[0018] S61. Calculate the adjustment ratio of the amplitude of all frequency points less than or equal to the error threshold relative to the amplitude of the corresponding frequency points in the predicted spectrum of the periodic and random signals, and establish the set of modulation ratio values ​​K as follows:

[0019] K = (K L ,....Km);

[0020] In the formula, K L K, ..., m are the adjustment ratios of the amplitude of the Lth to the mth frequency points relative to the amplitude of the corresponding frequency points in the predicted spectrum of the periodic and random signals, respectively, where L, m ≤ n;

[0021] S62. Modulate the time-domain waveform signal C obtained in step S4 within the frequency bandwidth [f1, f2]. n Expand into a Fourier series;

[0022] S63. Multiply the magnitude of the Fourier component of the error that is greater than the error threshold by the corresponding proportional value in the proportional value set K to obtain the compensated Fourier series C'.

[0023] S64. Perform an inverse Fourier transform on C' to obtain the compensated time-domain waveform signal;

[0024] S7. Repeat step S6 until the error sequence E between the estimated spectrum and the predicted spectrum of the periodic and random signals is lower than the set error threshold, and obtain the final time-domain waveform signal.

[0025] Preferably, the basic parameters of the aircraft cannon include eight basic parameters: number of cannons, firing frequency, explosive energy, projected distance, cannon position, equipment weight, depth parameter, and vector distance.

[0026] Preferably, the time-domain randomization process is a process of using a randomization algorithm to process a pseudo-random signal to obtain a true random sequence.

[0027] Preferably, the window function is an exponential window, and the attenuation rate of the exponential window is determined based on the simulated waveform characteristics.

[0028] Preferably, the error threshold is ±3dB.

[0029] Preferably, the formula for expanding into a Fourier series in step S62 is as follows:

[0030]

[0031] In the formula, t is time and A0 is a constant.

[0032] Preferably, in another aspect, the present invention provides a waveform reconstruction system for an aircraft gun impact environment, which includes a predicted spectrum calculation unit, a sine signal construction unit, a pseudo-random signal construction unit, a true random signal calculation unit, a time-domain signal modulation unit, an error sequence construction unit, and a time-domain waveform signal compensation unit;

[0033] The predicted spectrum calculation unit is used to calculate the initial predicted spectrum of the periodic and random signals. The sine signal construction unit is used to construct the sine signal of the predicted spectrum. The pseudo-random signal construction unit is used to construct the pseudo-random signal of the predicted spectrum. The true random signal calculation unit is used to calculate the true random signal based on the pseudo-random signal. The time-domain signal modulation unit is used to superimpose the sine signal and the true random signal, and use the time-domain windowed pulse method to modulate the waveform of the superimposed time-domain signal. The error sequence construction unit is used to construct the error sequence E between the estimated spectrum of the modulated time-domain signal waveform and the predicted spectrum of the periodic and random signals in S1. The time-domain waveform signal compensation unit is used to compensate the error sequence E to obtain the compensated time-domain waveform signal.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0035] (1) The waveform reconstruction method for the impact environment of aircraft guns provided by this invention can obtain time-domain waveform signals that are closer to the actual artillery impact environment. Compared with the waveforms that tend to be in a stable random state generated by the traditional artillery impact test method based on composite vibration, the waveform reconstruction of this invention can well reproduce the typical characteristics of the actual artillery impact environment, such as time-varying, short-period, non-stationary, and non-Gaussian repetitive impact, and has a better simulation effect for the actual artillery impact environment.

[0036] (2) The waveform reconstruction method of the shelling impact environment provided by the present invention is closer to the real environmental effect when the test product is evaluated in the laboratory test, which reduces the risk of under-evaluation of the test product and has a significant improvement effect on the test results.

[0037] (3) The impact environment waveform reconstruction method of the present invention can be applied not only to the impact environment waveform reconstruction of aircraft cannons, but also to the shelling environment waveform reconstruction of other small-caliber rapid-fire cannons, and has good application prospects. Attached Figure Description

[0038] Figure 1This is a schematic diagram of the shock environment waveform reconstruction method for aircraft gun simulation tests according to the present invention.

[0039] Figure 2 This is a schematic diagram of the waveform reconstruction system for the impact environment of an aircraft cannon according to the present invention;

[0040] Figure 3 This is a schematic diagram of the predicted spectrum obtained in Embodiment 1 of the present invention;

[0041] Figure 4 This is a schematic diagram of the time-domain waveform (1s), power spectral density, and probability density distribution in Embodiment 1 of the present invention;

[0042] Figure 5 This is a schematic diagram of the time-domain waveform and line spectrum in Embodiment 1 of the present invention;

[0043] Figure 6 This is a schematic diagram of the synthesized original time-domain waveform in Embodiment 1 of the present invention;

[0044] Figure 7 This is a schematic diagram of the shape of the index window in Embodiment 1 of the present invention;

[0045] Figure 8 This is a schematic diagram comparing the time-domain waveforms and power spectra of the signal before and after windowing in Embodiment 1 of the present invention;

[0046] Figure 9 This is a schematic diagram showing the calculation results of the amplitude spectrum ratio in Embodiment 1 of the present invention;

[0047] Figure 10 This is a schematic diagram comparing the original synthesized signal and the final pulse modulation signal in Embodiment 1 of the present invention;

[0048] Figure 11 This is a schematic diagram of the experimental installation in Embodiment 2 of the present invention;

[0049] Figure 12 This is a schematic diagram of the experimental principle in Embodiment 2 of the present invention;

[0050] Figures 13a-13c These are comparative schematic diagrams of vibration test waveforms in Embodiment 2 of the present invention. Figure 13a This is a schematic diagram of the actual impact waveform of artillery fire. Figure 13b This is a schematic diagram of the shell impact waveform simulated by traditional testing methods. Figure 13c The improved test method simulates the generated shell impact waveform;

[0051] Figure 14 This is a schematic diagram of the fatigue damage equivalent comparison analysis of the waveform in Embodiment 2 of the present invention. Detailed Implementation

[0052] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings.

[0053] Specifically, this invention provides a waveform reconstruction method for an impact environment used in aircraft cannon simulation tests, such as... Figure 1 As shown, it includes the following steps:

[0054] S1. Calculate the period and initial predicted spectrum of the random signal based on the basic parameters of the aircraft cannon. The basic parameters of the aircraft cannon include eight basic parameters: number of guns, firing frequency, explosive energy, projected distance, gun position, equipment weight, depth parameter, and vector distance.

[0055] S2. A sinusoidal signal is constructed based on the periodic characteristics in the initial predicted spectrum of the periodic and random signals. A pseudo-random signal is constructed using inverse Fourier transform based on the random characteristics. Time-domain randomization is the process of processing the pseudo-random signal using a randomization algorithm to obtain a truly random sequence.

[0056] S3. Perform time-domain randomization processing on the pseudo-random signal to optimize and improve the signal phase information and generate a true random signal.

[0057] S4. Superimpose the sinusoidal signal constructed in S2 with the true random signal constructed in S3. Using the time-domain windowed pulse method, select an appropriate window function to modulate the superimposed time-domain signal, obtaining the modulated time-domain waveform signal C. The window function is generally an exponential window, but rectangular windows, Hanning windows, and wavelets can also be selected as needed. The attenuation rate of the exponential window is determined based on the characteristics of the simulated waveform.

[0058] S5. Estimate the power spectral density of the modulated time-domain waveform signal to obtain the estimated spectrum, and calculate the error E between the estimated spectrum and the predicted spectrum of the periodic and random signals in S1. This includes the following sub-steps:

[0059] S51. Define the periodic and random predicted spectrum as the spectrum over the frequency bandwidth [f1, f2]. n The amplitude is [B1, B] n If a finite sequence is composed of f1, f2, ..., f3, then the estimated spectrum is given by the frequency bandwidth [f1, f3, ..., f4]. n The amplitude is [A1, A] n A finite sequence consisting of [a number of units], where the frequency f is in Hz and the amplitude is in g. 2 / Hz.

[0060] S52, For frequency bandwidth [f1, f n Any frequency point f within ] s (1≤S≤n), calculate f s The corresponding estimated spectral amplitude A s Relative to periodic and random predicted spectral amplitude B s Error Es The calculation formula is:

[0061] E s =10*lg(A) s / B s ).

[0062] S53. Calculate the frequency bandwidth [f1, f1] one by one according to step S52. n The errors of the estimated spectral amplitudes for all frequency points within the range relative to the predicted spectral amplitudes of periodic and random signals are calculated, and the error sequence E is established as follows:

[0063] E = (E1, E2, ... E n )

[0064] In the formula, E1, E2, ... E n These represent the errors of the estimated spectral amplitudes for the first, second, ..., nth frequency points relative to the predicted spectral amplitudes for periodic and random signals, respectively.

[0065] S6. Set the error threshold to ±3dB. When the error at certain frequency points in the error sequence E exceeds the set error threshold, compensate the time-domain waveform signal C modulated in step S4 to obtain the compensated time-domain waveform signal. This specifically includes the following sub-steps:

[0066] S61. Calculate the adjustment ratio of the amplitude of all frequency points less than or equal to the error threshold relative to the amplitude of the corresponding frequency points in the predicted spectrum of the periodic and random signals, and establish the set of modulation ratio values ​​K as follows:

[0067] K = (K L ,....Km);

[0068] In the formula, K L K, ..., Km are the adjustment ratios of the amplitude of the Lth to the mth frequency points relative to the amplitude of the corresponding frequency points in the predicted spectrum of the periodic and random signals, respectively, where L, m ≤ n.

[0069] S62. Modulate the time-domain waveform signal C obtained in step S4 within the frequency bandwidth [f1, f2]. n Expand into a Fourier series. The formula for expanding into a Fourier series is as follows:

[0070]

[0071] In the formula, t is time and A0 is a constant.

[0072] S63. Multiply the magnitude of the Fourier component of the error that is greater than the error threshold by the corresponding proportional value in the proportional value set K to obtain the compensated Fourier series C'.

[0073] S64. Perform an inverse Fourier transform on C' to obtain the compensated time-domain waveform signal.

[0074] S7. Repeat step S6 until the error sequence E between the estimated spectrum and the predicted spectrum of the periodic and random signals is lower than the set error threshold, and obtain the final time-domain waveform signal.

[0075] On the other hand, such as Figure 2 As shown, the present invention provides a waveform reconstruction system for an aircraft gun impact environment, which includes a predicted spectrum calculation unit 1, a sine signal construction unit 2, a pseudo-random signal construction unit 3, a true random signal calculation unit 4, a time-domain signal modulation unit 5, an error sequence construction unit 6, and a time-domain waveform signal compensation unit 7.

[0076] The prediction spectrum calculation unit 1 is used to calculate the initial prediction spectrum of periodic and random signals; the sine signal construction unit 2 is used to construct the sine signal of the prediction spectrum; the pseudo-random signal construction unit 3 is used to construct the pseudo-random signal of the prediction spectrum; the true random signal calculation unit 4 is used to calculate the true random signal based on the pseudo-random signal; the time-domain signal modulation unit 5 is used to superimpose the sine signal and the true random signal, and use the time-domain windowed pulse method to modulate the waveform of the superimposed time-domain signal; the error sequence construction unit 6 is used to construct the error sequence E between the estimated spectrum of the modulated time-domain signal waveform and the prediction spectrum of the periodic and random signals in S1; the time-domain waveform signal compensation unit is used to compensate the error sequence E to obtain the compensated time-domain waveform signal. Specific Implementation Example 1

[0078] This invention provides a method for reconstructing waveforms in a shelling environment. The following examples illustrate the invention in detail.

[0079] The following method was obtained according to Appendix C of GJB 150.20A: Figure 3 The predicted spectrum is shown, with a fundamental frequency F1 of 66.6 Hz and a maximum analysis frequency of F. max =2000Hz.

[0080] The frequencies and values ​​of the initial design spectrum are shown in Table 1.

[0081] Table 1 Initial Design Spectrum Frequency and Value

[0082] Inflection point frequency (Hz) 30 300 600 700 2000 <![CDATA[Test quantity value (g 2 / Hz)]]> 0.028 0.156 0.392 0.392 0.028 Sine frequency (Hz) 66.6 133.2 199.8 266.4 <![CDATA[Sine peak value (g 2 / Hz)]]> 1.0 1.2 1.5 1.8

[0083] Generate the original SOR time-domain waveform. Assume a total of 300 shells were fired, and a conventional vibration test is used.

[0084] Waveform generation technology generates Gaussian random vibrations and sinusoidal vibrations of corresponding time lengths, and then synthesizes them into the original experimental waveform. The calculation method is as follows.

[0085] 1) Using traditional time-domain randomization techniques, a stationary random band-limited Gaussian time history is generated based on the power spectral density (PSD) of the predicted random portion of the spectrum. The time history can be constructed from the PSD using inverse Fourier transform (IFT) techniques. Specifically, the Fourier two-sided amplitude spectrum is calculated based on the PSD, with the phase randomly generated according to a uniform distribution between [-π / 2π / 2]. After obtaining the Fourier spectrum with random phase, an IFT is performed to obtain the desired time history. The time-domain waveform (1 s), power spectral density, and probability density distribution are obtained using the above method (see Appendix). Figure 4 .

[0086] 2) Based on the sinusoidal frequencies and spectral values ​​in the predicted spectrum, generate the sinusoidal vibration time-domain waveform, with the phase of each frequency component set to 0. The time-domain waveform and line spectrum are shown in the appendix. Figure 5 .

[0087] 3) Add the broadband random vibration component R(t) and the sinusoidal vibration component S(t) to synthesize the original time-domain waveform TD(t), as shown in the appendix. Figure 6 As shown.

[0088] Generate an exponential window. The exponential window has the form of formula (1). In this example, the duration of the exponential window is the same as the period of the artillery pulse, so the number of points in the exponential window is also N = 500. The attenuation rate 'a' of the exponential window can be selected as needed. In this example, the value is 4. The shape of the exponential window is shown in the appendix. Figure 7 .

[0089]

[0090] Calculate the time-domain windowed signal. Divide the original synthesized time-domain waveform into Np points into a data block, apply an exponential window to each data block, and calculate the time-domain windowed signal TDW(t). See the appendix for a comparison of the time-domain waveforms and power spectra before and after windowing. Figure 8 After windowing, the time-domain waveform exhibits repetitive pulse characteristics.

[0091] Calculate the ratio R(f) of the discrete Fourier transform amplitude spectrum. Perform Fourier transforms on signals TD(t) and TDW(t), then calculate the ratio of their amplitude spectra, and finally remove the DC component from R(f). The calculation results are shown in the appendix. Figure 9 .

[0092] The Fourier spectrum of the time-windowed pulse signal is obtained by multiplying R(f). A discrete Fourier inverse transform is then performed to obtain the windowed modulated signal TDWN(t). A comparison between the original synthesized signal and the final pulse modulated signal is shown in the appendix. Figure 10 Compared with the original signal, the modulated signal has repetitive impulse characteristics in the time domain, while the power spectral density in the frequency domain remains unchanged. Specific Implementation Example 2

[0094] To verify the feasibility and rationality of various artillery impact tests, laboratory tests were conducted to verify the feasibility and beneficial effects of this application. In this embodiment 2, a certain type of airborne equipment significantly affected by artillery impact was selected as the test specimen, and the traditional test method and the improved test method were compared. A schematic diagram of the test installation is shown below. Figure 11 As shown in the schematic diagram, the principle is as follows: Figure 12 As shown. Figure 12 As shown, the entire simulation test system includes a first host computer 11 for generating impulse responses, a vibration / strain acquisition instrument 12 for acquiring vibration and strain data, a vibration controller 13 for vibration control, a second host computer 14 for generating reference artillery pulses, a vibration table 15, and a power amplifier 16. A test piece 17 is mounted on the vibration table 15, and the test piece 17 is equipped with vibration control measuring point V1, vibration response measuring point V2, bottom strain measuring point S1, and top strain measuring point S2. The power amplifier 16 is connected to both the vibration table 15 and the vibration controller 14. Vibration control measuring point V1 and vibration response measuring point V2 are connected to the vibration controller 13, and bottom strain measuring point S1 and top strain measuring point S2 are connected to the vibration / strain acquisition instrument 12. The output of the second host computer 14 is connected to the vibration controller 13, and the output of the first host computer 14 is connected to the vibration / strain acquisition instrument 12.

[0095] In this Example 2, the test specimen was a control box of a certain type near a certain aircraft cannon, and the vibration table was an 8.9-ton induction ring type electric vibration table. Three methods were used in the test, and the specific descriptions of the three methods are shown in Table 2.

[0096] Table 2. Description of Test Items and Test Conditions

[0097]

[0098] During the experiment, the acceleration and strain responses of key parts of the specimen were measured, with the acceleration data sampling rate at 12.8 kHz.

[0099] Figure 13 shows a comparison between the actual shelling impact waveform and the shelling impact waveform simulated by the test methods before and after the improvement. Figure 13a , Figure 13b , Figure 13c The following are schematic diagrams of experimental waveforms for several methods, in order: original artillery pulse, combined vibration (SOR), and time-domain windowed pulse (TDWP). Figures 13a-13c It can be seen that the traditional composite vibration test method of GJB 150.20A basically cannot show the repetitive pulse characteristics, while the improved method can produce obvious pulse characteristics.

[0100] Based on the comparative analysis of experimental conditions, rainflow counting was used to further analyze the stress response of each experimental method. In the case study, the material of the test specimen was Q235 structural steel with an elastic modulus of 206 GPa. The slope of the S / N curve was taken as 8, and the number of life cycles was taken as 2 × 10⁶. The stress was calculated based on the strain at the key locations obtained from the experiment. Then, the fatigue damage was calculated using the rainflow counting method and the S / N curve. The calculation formula is as follows:

[0101] σ=ε×E

[0102] S b ×N=C

[0103]

[0104] In the formula, σ is stress, ε is strain, E is elastic modulus; S and N are the stress value and number of cycles in the S / N curve, b is the slope of the curve, C is a constant value; D is the damage equivalent, ni is the number of cycles obtained from rainflow counting statistics, and i is the number of groups.

[0105] The calculation results of fatigue damage for each test method are shown in the figure. Figure 14 Using TWR as the benchmark for real artillery impact environments, it can be found that the stress characteristic values ​​of the original standard program and the traditional IV SOR method differ most significantly from reality, failing to accurately simulate real artillery impacts. In contrast, the Time-Domain Windowed Pulse Modulation (TDWP) method, as described in this application, closely approximates reality and can effectively reconstruct the waveform of a real artillery impact.

[0106] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for reconstructing impact environment waveforms for aerial gun simulation tests, characterized in that: It comprises the following steps: S1, calculating the initial estimated spectrum of periodic and random signals according to the basic parameters of the aerial gun; S2, constructing a sinusoidal signal based on the periodic characteristics in the initial estimated spectrum of periodic and random signals, and constructing a pseudo-random signal based on the random characteristics using inverse Fourier transform; S3, performing time-domain randomization processing on the pseudo-random signal to optimize and improve the signal phase information, and generating a true random signal; S4, superimposing the sinusoidal signal constructed in S2 and the true random signal constructed in S3, selecting a corresponding window function to modulate the waveform of the superimposed time-domain signal using the time-domain windowed pulse method, and obtaining a modulated time-domain waveform signal C; S5, performing power spectrum density estimation on the modulated time-domain waveform signal to obtain an estimated spectrum, and calculating the error E between the estimated spectrum and the estimated spectrum of periodic and random signals in S1, specifically including the following sub-steps: S51, define the periodic and random prediction spectrum as a finite sequence consisting of amplitudes [B1, B n ] over a frequency bandwidth [f1, f n ] then the estimated spectrum is a finite sequence consisting of amplitudes [A1, A n ] over a frequency bandwidth [f1, f n ] where the frequency f is in Hz and the amplitude in ; S52, for any frequency point f n within the frequency bandwidth [f1, f s n], calculate f s The corresponding estimated spectral amplitude A s The error E s of the periodic and random predicted spectral amplitude B s , the calculation formula is: ； S53, calculate the error of the estimated spectral amplitude corresponding to each frequency point in the frequency bandwidth [f1, fN] relative to the expected spectral amplitude of the periodic and random signals according to step S52, and establish an error sequence E as follows: n E = {e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12, e13, e14, e15, e16, e17, e18, e19, e20, e21, e22, e23, e ； wherein respectively, are the errors of the estimated spectral amplitudes of the first, second,... n-th frequency points with respect to the expected spectral amplitudes of the periodic and random signals. S6, when the error of some frequency points in the error sequence E is greater than the set error threshold, compensating the time-domain waveform signal C modulated in step S4 to obtain a compensated time-domain waveform signal, specifically including the following sub-steps: S61, calculating the adjustment ratio value of the amplitude of all frequency points less than or equal to the error threshold relative to the amplitude of the corresponding frequency points in the estimated spectrum of periodic and random signals, and establishing a modulation ratio value set K as follows: K = (K L ,....Km); In the formula, K L ,....Km are respectively the adjustment proportion values of the amplitude of the Lth...mth frequency point relative to the amplitude of the corresponding frequency point in the periodic and random signal prediction spectrum, wherein L, m≤n; S62, spreading the modulated time-domain waveform signal C in step S4 into a Fourier series in a frequency bandwidth [f1, f2]; n ] S63, multiplying the amplitude of the Fourier component of the error greater than the error threshold by the corresponding ratio value in the ratio value set K to obtain the compensated Fourier series C'; S64, performing inverse Fourier transform on C' to obtain the compensated time-domain waveform signal; S7, repeating step S6 until the error contained in the error sequence E between the estimated spectrum and the estimated spectrum of periodic and random signals is lower than the set error threshold, and obtaining the final time-domain waveform signal.

2. The impact environment waveform reconstruction method for aviation machine gun simulation test according to claim 1, characterized in that: The basic parameters of the aerial gun include 8 basic parameters of the number of gun doors, firing frequency, explosion energy, projection distance, gun position, equipment weight, depth parameter, and vector distance.

3. The impact environment waveform reconstruction method for aviation machine gun simulation test according to claim 1, characterized in that: The time-domain randomization processing is a process of obtaining a true random sequence by processing a pseudo-random signal using a randomization algorithm.

4. The impact environment waveform reconstruction method for aviation machine gun simulation test according to claim 1, characterized in that: The window function is an exponential window, and the decay rate of the exponential window is determined according to the simulated waveform characteristics.

5. The impact environment waveform reconstruction method for aviation machine gun simulation test according to claim 1, characterized in that: The error threshold is 3 dB.

6. The impact environment waveform reconstruction method for aviation machine gun simulation test according to claim 1, characterized in that: The formula expanded into a Fourier series in step S62 is as follows: ； where t is time, is a constant.

7. An aircraft cannon impact environment waveform reconstructing system for the impact environment waveform reconstructing method for the simulation test of an aircraft cannon as claimed in claim 1, characterized in that: It comprises an estimated spectrum calculation unit, a sinusoidal signal construction unit, a pseudo-random signal construction unit, a true random signal calculation unit, a time-domain signal modulation unit, an error sequence construction unit, and a time-domain waveform signal compensation unit; The estimated spectrum calculation unit is used to calculate the initial estimated spectrum of periodic and random signals, the sinusoidal signal construction unit is used to construct the sinusoidal signal of the estimated spectrum, the pseudo-random signal construction unit is used to construct the pseudo-random signal of the estimated spectrum, the true random signal calculation unit is used to calculate the true random signal based on the pseudo-random signal, and the time-domain signal modulation unit is used to superimpose the sinusoidal signal and the true random signal, and modulate the waveform of the superimposed time-domain signal using the time-domain windowed pulse method. The error sequence construction unit is configured to construct an error sequence E between an estimated spectrum of the modulated time-domain signal waveform and a periodic and random signal expected spectrum in S1; and the time-domain waveform signal compensation unit is configured to compensate the error sequence E to obtain a compensated time-domain waveform signal.

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