Aircraft sound detonation intensity prediction method and device, medium and product

Abstract

The invention discloses an aircraft acoustic detonation intensity prediction method and device, a medium and a product, and relates to the technical field of acoustic detonation prediction, and the method comprises the steps: obtaining surface pressure distribution data of an aircraft in different flight states based on the number and arrangement positions of pressure measurement holes in the surface of the aircraft; acquiring historical surface pressure distribution data and a data set of ground acoustic explosion intensity corresponding to the historical surface pressure distribution data; based on the data set, training a sound explosion prediction model, and determining a trained sound explosion prediction model; and inputting to-be-detected surface pressure distribution data obtained in real time in the current flight state into the trained acoustic explosion prediction model, and determining the ground acoustic explosion intensity corresponding to the to-be-detected surface pressure distribution data. The method can predict the ground sound explosion intensity in real time, and shortens the prediction time of the sound explosion intensity.

Description

Technical Field

[0001] The present application relates to the technical field of sonic boom prediction, and in particular to a method, device, medium and product for predicting the intensity of sonic boom of an aircraft. Background Art

[0002] When the speed of an aircraft exceeds the speed of sound, its flow field will generate shock waves and expansion waves. When these waves propagate to the ground, they form a sonic boom consisting of two strong shock waves, which will cause harm to people, animals and buildings on the ground. For example, when a passenger plane cruises at supersonic speed, the sonic boom intensity is as high as 108PLdB, which is enough to shatter the glass of a building.

[0003] In order to reduce the intensity of the sonic boom of the aircraft, the international community has proposed the nose silent cone sonic boom suppression technology, which installs a retractable long rod-shaped mechanism composed of multiple sections of columns and cones on the nose, and extends the mechanism during supersonic flight to convert the strong bow shock wave at the nose into several weak shock waves. However, due to the lack of feedback mechanism in this process and the long prediction time of the sonic boom intensity, the sonic boom intensity cannot be predicted in real time and fed back to the control system, which may lead to the inability to effectively suppress the sonic boom and reduce the impact on ground residents and the environment. Summary of the invention

[0004] The purpose of the present application is to provide a method, device, medium and product for predicting the sonic boom intensity of an aircraft, which can solve the problem in the prior art that the sonic boom prediction time is long and the sonic boom suppression effect is poor.

[0005] To achieve the above objectives, this application provides the following solutions: In a first aspect, the present application provides a method for predicting the sonic boom intensity of an aircraft, comprising: obtaining surface pressure distribution data of the aircraft under different flight states based on the number and arrangement positions of pressure measuring holes on the aircraft surface; the parameters of the flight state include Mach number, Reynolds number, angle of attack and sideslip angle; obtaining a data set of historical surface pressure distribution data and ground sonic boom intensity corresponding to the historical surface pressure distribution data; based on the data set, taking the historical surface pressure distribution data as input and the ground sonic boom intensity corresponding to the historical surface pressure distribution data as output, training a sonic boom prediction model to determine the trained sonic boom prediction model; the sonic boom prediction model is a deep neural network; inputting the surface pressure distribution data to be detected obtained in real time under the current flight state into the trained sonic boom prediction model to determine the ground sonic boom intensity corresponding to the surface pressure distribution data to be detected.

[0006] In a second aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the aircraft sonic boom intensity prediction method described above.

[0007] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for predicting the aircraft sonic boom intensity.

[0008] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned aircraft sonic boom intensity prediction method.

[0009] According to the specific embodiments provided in this application, this application discloses the following technical effects: The present application provides a method, device, medium and product for predicting the sonic boom intensity of an aircraft. First, the historical surface pressure distribution data of the aircraft under different flight states is obtained in real time through the pressure measuring holes on the aircraft surface; then, the historical surface pressure distribution data and the data set of the ground sonic boom intensity corresponding to the historical surface pressure distribution data are obtained. Furthermore, the sonic boom prediction model can be effectively trained through the data set to obtain a trained prediction model. Based on the trained prediction model, an accurate mapping relationship between the historical surface pressure distribution data and the ground sonic boom intensity corresponding to the historical surface pressure distribution data is established to improve the accuracy of subsequent ground sonic boom intensity prediction results; finally, the surface pressure distribution data to be detected obtained in real time under the current flight state and the trained sonic boom prediction model can be used to accurately and quickly obtain the ground sonic boom intensity corresponding to the surface pressure distribution data to be detected, and the real-time predicted sonic boom intensity is fed back to the control system, thereby effectively suppressing the sonic boom. It can be seen that the present application can predict the ground sonic boom intensity in real time, significantly shortening the prediction time of the sonic boom intensity. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0011] Figure 1 The present invention is a flowchart of a method for predicting the sonic boom intensity of an aircraft provided in an embodiment of the present application.

[0012] Figure 2 A schematic diagram of the installation position of a pressure measuring hole on the surface of an aircraft provided in an embodiment of the present application.

[0013] Figure 3 This is a schematic diagram of the installation position of a pressure measuring hole on the nose and wing of an aircraft provided in an embodiment of the present application.

[0014] Figure 4It is a structural schematic diagram of a sonic boom prediction model provided in an embodiment of the present application. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0016] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0017] like Figure 1 As shown, the present application provides a method for predicting the intensity of an aircraft sonic boom, including steps 101 to 104.

[0018] Step 101: Based on the number and arrangement positions of pressure measuring holes on the aircraft surface, surface pressure distribution data of the aircraft under different flight states are obtained; the parameters of the flight state include Mach number, Reynolds number, angle of attack and sideslip angle.

[0019] In some embodiments, before step 101, it also includes: for any aircraft configuration, determining the number and layout positions of pressure measuring holes on the aircraft surface corresponding to the aircraft configuration; wherein the aircraft configuration includes aircraft layout, wing shape and fuselage size; and pressure sensors are arranged in the pressure measuring holes.

[0020] Wherein, the number of the pressure measuring holes is at least 5.

[0021] Exemplarily, the pressure measuring holes are distributed in a cross shape at the nose of the aircraft, and one pressure measuring hole is arranged on each of the upper and lower surfaces of the leading edge of the wing.

[0022] In practical applications, 5 to 25 pressure measuring holes can be arranged on the nose and leading edge of the wing of the aircraft. Figure 2 and Figure 3 There is a pressure hole on the surface of the aircraft, marked by numbers 1 to 13. Among them, pressure hole 1 is located at the top of the nose. The diameter of the pressure hole is about 2 mm , which has little effect on the surface pressure of the aircraft. The surface pressure is measured by a pressure sensor installed in the pressure measuring hole. In general, at least 5 pressure measuring holes are required. Considering the need for redundant measurement, the number of pressure measuring holes is appropriately increased to improve fault tolerance. For aircraft with complex configurations, additional pressure measuring holes can be arranged on the leading edge of the aircraft wing to improve the prediction accuracy. Exemplarily, the pressure measuring holes are distributed in a cross shape above and below the aircraft nose and the leading edge of the wing.

[0023] Step 102: Acquire the historical surface pressure distribution data and a data set of ground sonic boom intensity corresponding to the historical surface pressure distribution data.

[0024] In some embodiments, step 102 specifically includes steps 201 to 203 .

[0025] Step 201: Acquire the historical surface pressure distribution data and the sound pressure distribution data of the ground sonic boom corresponding to the historical surface pressure distribution data.

[0026] Step 202: Determine the intensity of the ground sonic boom corresponding to the historical surface pressure distribution data based on the sound pressure distribution data of the ground sonic boom corresponding to the historical surface pressure distribution data and the Stevens Mark VII model.

[0027] Step 203: constructing the data set based on the historical surface pressure distribution data and the ground sonic boom intensity corresponding to the historical surface pressure distribution data.

[0028] In some embodiments, step 201 specifically includes: based on a flight test, obtaining the historical surface pressure distribution data and a data set of ground sonic boom intensity corresponding to the historical surface pressure distribution data; or, based on a wind tunnel test or a computational fluid dynamics method, obtaining the historical surface pressure distribution data and a data set of ground sonic boom intensity corresponding to the historical surface pressure distribution data.

[0029] In practical applications, a large amount of historical surface pressure distribution data including the aircraft surface and the corresponding ground sonic boom intensity is obtained. Each data sample can be expressed as ,in, Indicates The first test or simulation record of the group flight state The pressure value at each pressure measuring hole, is the number of pressure measuring holes; In some embodiments, step 201 further comprises obtaining the historical surface pressure distribution data and the sound pressure distribution data of the ground sonic boom corresponding to the historical surface pressure distribution data based on a wind tunnel test or a computational fluid dynamics method, specifically comprising steps 301 to 303.

[0030] Step 301: define a near field area and a far field area; the near field area is a space three times the fuselage length of the aircraft; the far field area is a space outside the near field area.

[0031] Step 302: In the near-field region, the historical surface pressure distribution data and the near-field sound pressure distribution data of the near-field region are obtained through the wind tunnel test or the computational fluid dynamics.

[0032] Step 303: Based on the near-field sound pressure distribution data and the acoustic model, determine the sound pressure distribution data of the ground sonic boom corresponding to the historical surface pressure distribution data.

[0033] In some embodiments, step 303 specifically includes: determining the sound pressure distribution data of the ground sonic boom corresponding to the historical surface pressure distribution data according to the near-field sound pressure distribution data and the generalized Burgers equation; wherein the acoustic model includes the generalized Burgers equation.

[0034] The calculation formula of the generalized Burgers equation is: .

[0035] in, It is the sound pressure distribution data of ground sonic boom; is the area of ​​the acoustic tube; is the length of the sound ray; and are the ambient atmospheric density and the speed of sound, respectively; is the nonlinear coefficient; is the diffusion coefficient; For the The sound velocity increment of the molecule; is the delay time of the sound pressure waveform; is the integration variable; For the The relaxation time of a molecule.

[0036] Specifically, a combination of multiple flight states and different aircraft configurations is selected as initial conditions to obtain pressure distribution at pressure measuring holes on the aircraft surface under different flight states and corresponding sound pressure distribution data.

[0037] Among them, the pressure distribution at the pressure measuring holes under the selected flight state and the sound pressure distribution data of the ground sonic boom are measured by flight tests; a hybrid prediction method that considers the near field and the far field separately can also be used. Specifically, the surface pressure distribution data and the near-field sound pressure distribution data at the pressure measuring holes on the surface of the aircraft are obtained by wind tunnel tests or computational fluid dynamics methods in the near-field area, and the near-field sound pressure distribution data are used as the input of the far-field acoustic model, and then the acoustic models such as the generalized Burgers equation or the generalized Tricomi equation are solved to obtain the sound pressure distribution data of the ground sonic boom.

[0038] Among them, under maneuvering flight conditions, the generalized Tricomi equation is used to solve the problem, which takes into account the focused sonic boom phenomenon generated during maneuvering.

[0039] In some embodiments, step 202 specifically includes: based on the Stevens Mark VII model, performing Fourier transform on the sound pressure distribution data of the ground sonic boom corresponding to the historical surface pressure distribution data to determine the sound pressure level of each frequency band; according to the sound pressure level of each frequency band, using a loudness index curve to determine the loudness of each frequency band; according to the , determine the total loudness; where, is the maximum value in the loudness index; For the frequency band loudness; is the total loudness; F is the shielding factor; , determine the total loudness level; where, is the total response level; and the total loudness level is used as the ground sonic boom intensity corresponding to the historical surface pressure distribution data.

[0040] Among them, Stevens' Mark VII model uses the 1 / 3 octave band sound pressure level with a center frequency of 3150Hz as a reference, and the loudness produced by 32dB at the center frequency as the basic unit of measurement. The loudness increases with the 2 / 3 power of the sound pressure. Every time the sound pressure level increases by 9dB, the loudness value doubles.

[0041] Among them, the two values ​​of 32 and 9 are artificially set according to experimental data. The values ​​of 32 and 9 are based on the following: ① Stevens' Mark VII method uses the 1 / 3 octave band sound pressure level with a center frequency of 3150Hz as a reference, and the loudness produced by 32dB at the center frequency as the basic unit of measurement. ② Loudness increases with the 2 / 3 power of sound pressure. Every time the sound pressure level increases by 9dB, the loudness value doubles.

[0042] Specifically, in order to evaluate the intensity of ground sonic booms, the Stevens Mark VII model is used to convert the sound pressure distribution data of ground sonic booms into the total loudness level. First, based on the sound pressure distribution data of ground sonic booms, the time domain signal of ground sonic booms is obtained, and the time domain signal of ground sonic booms is transformed by fast Fourier transform to convert the time domain signal into the frequency domain signal to obtain the sound pressure level in each frequency band; according to the sound pressure level of the center frequency of each frequency band, the loudness index curve is used to determine the total loudness level of the first frequency band. Frequency band loudness , and then the total loudness can be calculated according to the total loudness calculation formula.

[0043] Step 103: Based on the data set, taking the historical surface pressure distribution data as input and the ground sonic boom intensity corresponding to the historical surface pressure distribution data as output, a sonic boom prediction model is trained to determine a trained sonic boom prediction model.

[0044] Among them, the sonic boom prediction model is built based on deep learning.

[0045] In some embodiments, step 103 specifically includes: inputting the historical surface pressure distribution data and a data set of ground sonic boom intensity corresponding to the historical surface pressure distribution data into the sonic boom prediction model, training the parameters of the sonic boom prediction model with the goal of minimizing the loss value, and determining the trained sonic boom prediction model; wherein the loss value is determined based on the predicted value and the true value; the predicted value is the predicted ground sonic boom intensity corresponding to the historical surface pressure distribution data output after the sonic boom prediction model inputs the historical surface pressure distribution data and a data set of ground sonic boom intensity corresponding to the historical surface pressure distribution data; the true value is the ground sonic boom intensity corresponding to the historical surface pressure distribution data in the data set; the parameters of the sonic boom prediction model include weights and biases.

[0046] Specifically, a deep neural network (DNN) is used as a sonic boom prediction model. A deep neural network is a deep learning model composed of multiple neural layers. The weights and biases are the weights and biases of the neural layer. Each neural layer receives the output of the previous layer as input and calculates the output through a series of nonlinear transformations and weight adjustments. The neural network takes the pressure value at the pressure measuring hole as input, totaling variables, is the number of pressure measuring holes.

[0047] Among them, a deep neural network is selected as a method for building a deep learning model. The deep neural network includes an input layer, multiple hidden layers and an output layer, such as Figure 4 As shown in the figure, the mapping from the pressure distribution on the aircraft surface to the sonic boom intensity on the ground can be achieved through multi-level nonlinear mapping. The deep neural network uses the training set for feature learning. During the training process, the training rounds and batch size are set and adjusted as needed to ensure effective model learning and avoid overfitting.

[0048] The specific process of training the sonic boom prediction model and establishing the mapping relationship between the aircraft surface pressure distribution and the ground sonic boom intensity is as follows.

[0049] Firstly, the obtained data set is standardized and normalized to eliminate the influence of different dimensions and data ranges on the training of the sonic boom prediction model.

[0050] Then, the sonic boom prediction model is trained using the data set. Deep learning can establish a mapping relationship between input and output by learning a large amount of data. During the training process, the predicted value is first output based on the forward propagation, and then the loss is calculated using the mean square error. As a loss function, its expression is: .

[0051] in, It indicates the ground sonic boom intensity (true value) obtained in the data set from the previous simulation or experiment. represents the predicted value output by the sonic boom prediction model, represents the number of samples, Indicates samples. The lower the value, the higher the prediction accuracy of the deep neural network on the training set, and the better the model generalization ability without overfitting.

[0052] Furthermore, after calculating the loss value, the deep neural network uses the back-propagation algorithm to update the weights. First, the gradient of the loss function relative to each weight is calculated according to the chain rule to understand the sensitivity of the loss to the change of weights, and the weights are updated using the gradient descent algorithm. By inputting a sufficient number of training sets and repeating the above learning process, the weights are continuously adjusted to minimize During the training process, hyperparameters such as learning rate, number of network layers, and number of neurons in each layer can be adjusted to optimize the performance of the model.

[0053] like Figure 4 As shown in the figure, by arranging pressure measuring holes at different positions on the head of the aircraft, the airflow is introduced to the surface of the pressure sensor inside the aircraft, and the pressure sensor measures the data of the pressure distribution on the aircraft surface. The surface pressure distribution data is passed into the deep neural network through the input layer, and the trained prediction model is used to output the predicted sonic boom intensity.

[0054] Step 104: inputting the surface pressure distribution data to be detected acquired in real time under the current flight state into the trained sonic boom prediction model to determine the ground sonic boom intensity corresponding to the surface pressure distribution data to be detected.

[0055] In recent years, deep learning has been widely used in the field of aerodynamics. For example, a study has established a mapping relationship between a large amount of aircraft surface pressure distribution and flight status data, thereby achieving the goal of using the aircraft surface pressure distribution to solve the current flight status. It can be seen that by arranging pressure measuring holes on the aircraft surface to obtain surface pressure distribution data, and combining a large number of aircraft surface pressure distributions with the ground sonic boom intensity under the corresponding state, it is entirely possible to build an efficient prediction model from aircraft surface pressure distribution to ground sonic boom intensity based on deep learning, so as to significantly shorten the time required for sonic boom prediction.

[0056] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the above method is implemented when the processor executes the computer program.

[0057] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the above method when executed by a processor.

[0058] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements the above method when executed by a processor.

[0059] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive memory (ReRdM), magnetic random access memory (MRdM), ferroelectric random access memory (FRdM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RdM) or external cache memory, etc. By way of illustration and not limitation, RdM may be in various forms, such as static random access memory (SRdM) or dynamic random access memory (DRdM).

[0060] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0061] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0062] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for predicting the intensity of an aircraft sonic boom, characterized in that: include: Based on the number and arrangement positions of pressure measuring holes on the aircraft surface, historical surface pressure distribution data of the aircraft under different flight states are obtained; the parameters of the flight state include Mach number, Reynolds number, angle of attack and sideslip angle; Acquire the historical surface pressure distribution data and a data set of ground sonic boom intensity corresponding to the historical surface pressure distribution data; Based on the data set, taking the historical surface pressure distribution data as input and the ground sonic boom intensity corresponding to the historical surface pressure distribution data as output, training a sonic boom prediction model, and determining a trained sonic boom prediction model; The pressure distribution data of the surface to be detected acquired in real time under the current flight state is input into the trained sonic boom prediction model to determine the ground sonic boom intensity corresponding to the pressure distribution data of the surface to be detected.

2. The method for predicting aircraft sonic boom intensity according to claim 1, characterized in that: Based on the number and arrangement positions of the pressure measuring holes on the aircraft surface, before obtaining the surface pressure distribution data of the aircraft under different flight states, the following steps are also included: For any aircraft configuration, determine the number and arrangement positions of pressure measuring holes on the aircraft surface corresponding to the aircraft configuration; Wherein, the aircraft includes a variety of aircraft configurations; the aircraft configuration includes aircraft layout, wing shape and fuselage size; and a pressure sensor is arranged in the pressure measuring hole.

3. The method for predicting aircraft sonic boom intensity according to claim 1, characterized in that: Acquiring the historical surface pressure distribution data and a data set of ground sonic boom intensity corresponding to the historical surface pressure distribution data specifically includes: Acquire the historical surface pressure distribution data and the sound pressure distribution data of the ground sonic boom corresponding to the historical surface pressure distribution data; Determining the intensity of the ground sonic boom corresponding to the historical surface pressure distribution data based on the sound pressure distribution data of the ground sonic boom corresponding to the historical surface pressure distribution data and the Stevens Mark VII model; The data set is constructed based on the historical surface pressure distribution data and the ground sonic boom intensity corresponding to the historical surface pressure distribution data.

4. The method for predicting aircraft sonic boom intensity according to claim 3, characterized in that: Acquiring the historical surface pressure distribution data and the sound pressure distribution data of the ground sonic boom corresponding to the historical surface pressure distribution data specifically includes: Based on the flight test, acquiring the historical surface pressure distribution data and the sound pressure distribution data of the ground sonic boom corresponding to the historical surface pressure distribution data; or, Based on a wind tunnel test or a computational fluid dynamics method, obtaining the historical surface pressure distribution data and the sound pressure distribution data of the ground sonic boom corresponding to the historical surface pressure distribution data specifically includes: A near field area and a far field area are defined; the near field area is a space three times the fuselage length of the aircraft; the far field area is a space outside the near field area; In the near field area, obtaining the historical surface pressure distribution data and the near field sound pressure distribution data of the near field area through the wind tunnel test or the computational fluid dynamics; Based on the near-field sound pressure distribution data and the acoustic model, the sound pressure distribution data of the ground sonic boom corresponding to the historical surface pressure distribution data is determined.

5. The method for predicting aircraft sonic boom intensity according to claim 1, characterized in that: Based on the data set, taking the historical surface pressure distribution data as input and taking the ground sonic boom intensity corresponding to the historical surface pressure distribution data as output, training a sonic boom prediction model, and determining a trained sonic boom prediction model specifically includes: Inputting the historical surface pressure distribution data and a data set of ground sonic boom intensity corresponding to the historical surface pressure distribution data into the sonic boom prediction model, training the parameters of the sonic boom prediction model with the goal of minimizing the loss value, and determining the trained sonic boom prediction model; Wherein, the loss value is determined based on the predicted value and the true value; the predicted value is the predicted ground sonic boom intensity corresponding to the historical surface pressure distribution data output after the sonic boom prediction model inputs the historical surface pressure distribution data and the data set of the ground sonic boom intensity corresponding to the historical surface pressure distribution data; the true value is the ground sonic boom intensity corresponding to the historical surface pressure distribution data in the data set; the parameters of the sonic boom prediction model include weights and biases.

6. The method for predicting aircraft sonic boom intensity according to claim 3, characterized in that: Determining the intensity of the ground sonic boom corresponding to the historical surface pressure distribution data based on the sound pressure distribution data of the ground sonic boom corresponding to the historical surface pressure distribution data and the Stevens Mark VII model, specifically includes: Based on the Stevens Mark VII model, Fourier transform the sound pressure distribution data of the ground sonic boom corresponding to the historical surface pressure distribution data to determine the sound pressure level of each frequency band; According to the sound pressure level of each frequency band, the loudness of each frequency band is determined by using the loudness index curve; according to , determine the total loudness; where, is the maximum value in the loudness index; For the frequency band loudness; is the total loudness; F is the masking factor; according to , determine the total loudness level; where, is the overall response level; The total loudness level is used as the ground sonic boom intensity corresponding to the historical surface pressure distribution data.

7. The method for predicting aircraft sonic boom intensity according to claim 4, characterized in that: Determining the sound pressure distribution data of the ground sonic boom corresponding to the historical surface pressure distribution data based on the near-field sound pressure distribution data and the acoustic model specifically includes: Determining the sound pressure distribution data of the ground sonic boom corresponding to the historical surface pressure distribution data according to the near-field sound pressure distribution data and the generalized Burgers equation; wherein the acoustic model includes the generalized Burgers equation; The calculation formula of the generalized Burgers equation is: ； in, It is the sound pressure distribution data of ground sonic boom; is the area of ​​the acoustic tube; is the length of the sound ray; and are the ambient atmospheric density and the speed of sound, respectively; is the nonlinear coefficient; is the diffusion coefficient; For the The sound velocity increment of the molecule; is the delay time of the sound pressure waveform; is the integration variable; For the The relaxation time of a molecule.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the aircraft sonic boom intensity prediction method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting the intensity of an aircraft sonic boom according to any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for predicting the intensity of an aircraft sonic boom according to any one of claims 1 to 7 is implemented.

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