Method and device for calculating and predicting airport aircraft noise

Abstract

The present invention relates to the field of noise calculation and prediction technology, and specifically to a method and device for calculating and predicting airport aircraft noise. The method can obtain aircraft track data and real-time meteorological data, construct a dynamic noise source physical model, a sound source propagation attenuation physical model, a terrain influence mapping physical model, and a meteorological dynamic correction physical model. Based on the constructed physical model, the method calculates the dynamic sound power level, lateral diffraction correction factor, geometric attenuation, terrain attenuation, and atmospheric attenuation of the noise source, and then calculates the predicted sound pressure level at the receiving point. The method can also match the measured values ​​with the predicted values ​​in time and space to optimize the parameters of the sound propagation path in each physical model. The technical solution shown in the present invention dynamically couples the measured values ​​with the predicted values, and at the same time, considers multiple complex factors, significantly improving the prediction accuracy in complex environments, providing technical support for civil aviation noise control.

Description

Technical Field

[0001] The present invention relates to the technical field of noise calculation and prediction, and in particular to a method and device for calculating and predicting airport aircraft noise. Background Art

[0002] With the rapid development of the global civil aviation industry, airport aircraft noise pollution has become a significant factor restricting airport expansion and urban planning. Existing airport noise prediction methods mainly rely on simplified physical sound field models (such as ray tracing and geometric acoustic models) or empirical statistical models. These have the following significant drawbacks in practical applications:

[0003] Traditional models have serious deficiencies in dealing with the acoustic field distortion effects of terrain reflection, time-varying weather characteristics, and real-time aircraft attitudes.

[0004] The lack of an effective dynamic coupling mechanism between measured data and prediction models makes closed-loop noise field correction impossible. This means that in practical applications, even if a large amount of measured noise data is obtained, it is impossible to feed this data into the prediction model in real time to optimize and correct the model. This makes it difficult to promptly correct deviations between the prediction results and the actual noise situation, reducing the reliability and practicality of the prediction.

[0005] In complex terrain scenarios, traditional models take a long time to calculate three-dimensional sound field propagation.

[0006] Therefore, the current method of using traditional models to calculate and predict noise has the problems of insufficient consideration of complex factors and lack of dynamic coupling mechanism. Summary of the Invention

[0007] In view of this, the purpose of the present invention is to provide a method and device for calculating and predicting airport aircraft noise, so as to solve the problems in the existing noise calculation and prediction methods, such as insufficient consideration of complex factors and lack of dynamic coupling mechanism.

[0008] According to a first aspect of an embodiment of the present invention, a method for calculating and predicting airport aircraft noise is provided, comprising:

[0009] Obtain aircraft track data and real-time weather data;

[0010] Based on the engine model, thrust level and flight phase, a dynamic noise source physical model is established; based on the dynamic noise source physical model, the dynamic sound power level of the noise source is calculated according to the aircraft track data;

[0011] A physical model of sound source propagation attenuation, including both spherical and cylindrical wave models, is constructed. When the aircraft altitude is greater than a preset altitude, the spherical wave model is used; otherwise, the cylindrical wave model is used. Based on the physical model of sound source propagation attenuation, the geometric attenuation is calculated according to the sound propagation distance, and the lateral diffraction correction factor is calculated according to the aircraft roll angle, diffraction edge length, and incident angle.

[0012] Constructing a terrain impact mapping physical model based on a pre-acquired three-dimensional digital elevation model; calculating an acoustic path difference based on the terrain impact mapping physical model, and calculating a terrain attenuation based on the acoustic path difference, the wavelength of the sound wave, the straight-line distance from the sound source to the receiving point, and the ground reflection coefficient;

[0013] Constructing a meteorological dynamic correction physical model based on the real-time meteorological data; calculating the atmospheric attenuation based on the meteorological dynamic correction physical model according to the atmospheric absorption coefficient and the straight-line distance from the sound source to the receiving point;

[0014] The predicted sound pressure level at the receiving point is calculated based on the dynamic sound power level, geometric attenuation, atmospheric attenuation, terrain attenuation and lateral diffraction correction factor of the noise source;

[0015] The actual monitoring data of the ground noise at the receiving point is obtained, the actual monitoring data is matched with the predicted sound pressure level value in time and space, and the parameters of the sound propagation path in each physical model are optimized according to the matching results.

[0016] Preferably, the sound source propagation attenuation physical model, the terrain impact mapping physical model and the meteorological dynamic correction physical model all include sound propagation paths;

[0017] Before calculating the lateral diffraction correction factor and terrain attenuation, it also includes: calculating the Fresnel number of the sound propagation path based on the obstacle height, the sound wave wavelength and the total propagation distance; if the Fresnel number is greater than a preset value, the sound propagation path is an effective diffraction path, and the lateral diffraction correction factor is calculated; otherwise, the lateral diffraction correction factor is not calculated.

[0018] Preferably, the actual monitoring data is matched with the predicted sound pressure level value in time and space, and the parameters of the sound propagation path in each physical model are optimized according to the matching result, including:

[0019] Using a Kalman filter algorithm to perform spatiotemporal matching between the actual monitoring data and the sound pressure level prediction value;

[0020] Comparing the matched actual monitoring data with the sound pressure level prediction value, and generating a dynamic correction gain matrix according to the comparison result;

[0021] Calculating a corrected state vector according to the actual monitoring data, the sound pressure level prediction value and the dynamic correction gain matrix;

[0022] The parameters of the sound propagation path in the sound source propagation attenuation physical model, the terrain influence mapping physical model and the meteorological dynamic correction physical model are updated according to the state vector.

[0023] Preferably, the method further comprises:

[0024] Constructing a convolutional neural network, inputting historical noise spectra, historical meteorological parameters, and terrain feature maps into the convolutional neural network, and outputting a sound field inversion weight matrix;

[0025] When constructing a sound source propagation attenuation physical model, a sound source propagation attenuation physical model, a terrain influence mapping physical model, and a meteorological dynamic correction physical model, the sound field inversion weight matrix is ​​used to adjust model parameters.

[0026] Preferably, the dynamic sound power level of the noise source is calculated by the following formula:

[0027]

[0028]

[0029] in, is the first i The reference sound power level corresponding to each thrust level; It is the real-time thrust level of the engine; For the engine i thrust levels; is the speed correction term; is the aircraft's real-time airspeed; is the reference airspeed; is the dynamic sound power level.

[0030] Preferably, calculating the geometric attenuation according to the sound propagation distance includes:

[0031] If the aircraft altitude is greater than the preset altitude, the spherical wave attenuation calculation formula is:

[0032]

[0033] If the aircraft altitude is lower than the preset altitude, the cylindrical wave attenuation calculation formula is:

[0034]

[0035] in, is the geometric attenuation; and They represent different propagation distances respectively.

[0036] Preferably, the lateral diffraction correction factor is calculated by the following formula:

[0037]

[0038] in, is the lateral diffraction correction factor; β is the aircraft roll angle; is the diffraction edge length; is the angle of incidence.

[0039] Preferably, calculating terrain attenuation includes:

[0040] The sound path difference is calculated using the following formula:

[0041]

[0042] in, is the sound path difference; is the horizontal distance from the sound source to the obstacle; is the horizontal distance from the obstacle to the receiving point; is the vertical height from the obstacle vertex to the propagation path;

[0043] The barrier attenuation is calculated according to the following formula:

[0044]

[0045] in, is the wavelength of the sound wave; is the barrier attenuation;

[0046] The reflection attenuation is calculated according to the following formula:

[0047]

[0048] in, is the ground reflection coefficient; is the straight-line distance from the sound source to the receiving point; is the reflection attenuation;

[0049] The sum of barrier attenuation and reflection attenuation is taken as terrain attenuation.

[0050] Preferably, calculating the atmospheric attenuation includes:

[0051] The atmospheric absorption coefficient is calculated using the line-by-line integration model:

[0052]

[0053] in, is the atmospheric absorption coefficient; is the absorption coefficient of the gas component; is the density correction function;

[0054] The atmospheric attenuation is calculated using the following formula:

[0055]

[0056] in, is the atmospheric attenuation; is the straight-line distance from the sound source to the receiving point.

[0057] According to a second aspect of an embodiment of the present invention, there is provided an airport aircraft noise calculation and prediction device, comprising:

[0058] A main controller, and a memory connected to the main controller;

[0059] The memory stores program instructions;

[0060] The main controller is used to execute program instructions stored in the memory and perform any of the above methods.

[0061] The technical solutions provided by the embodiments of the present invention may have the following beneficial effects:

[0062] It is understood that the technical solution presented in this invention can obtain aircraft track data and real-time meteorological data, construct a dynamic noise source physical model, a sound source propagation attenuation physical model, a terrain impact mapping physical model, and a meteorological dynamic correction physical model. Based on the constructed physical models, the dynamic sound power level, lateral diffraction correction factor, geometric attenuation, terrain attenuation, and atmospheric attenuation of the noise source are calculated, and the sound pressure level prediction value at the receiving point is calculated. The technical solution presented in this invention can also match the measured values ​​with the predicted values ​​in time and space to optimize the parameters of the sound propagation path in each physical model. The technical solution presented in this invention dynamically couples the measured values ​​with the predicted values, and at the same time, considers a variety of complex factors, significantly improving the prediction accuracy in complex environments and providing technical support for civil aviation noise control.

[0063] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0065] Figure 1 This is a schematic diagram showing the steps of a method for calculating and predicting airport aircraft noise according to an exemplary embodiment;

[0066] Figure 2The present invention is a schematic flow chart of a method for calculating and predicting airport aircraft noise according to an exemplary embodiment. DETAILED DESCRIPTION

[0067] Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent like or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present invention, as detailed in the appended claims.

[0068] In one embodiment, see Figure 1 and Figure 2 , provides a method for calculating and predicting airport aircraft noise, including:

[0069] Step S11: Acquire aircraft track data and real-time weather data.

[0070] Aircraft track data includes the trajectory of the aircraft during operation and speed data during operation; real-time meteorological data mainly includes data that can have a significant impact on aircraft noise propagation, including temperature and humidity, wind speed gradient data, etc.

[0071] Step S12: establishing a dynamic noise source physical model based on the engine model, thrust level, and flight phase; and calculating the dynamic sound power level of the noise source based on the dynamic noise source physical model and the aircraft track data.

[0072] Aircraft operating states and noise generation mechanisms vary significantly during different flight phases, such as takeoff, cruising, and landing. Furthermore, different engine models, due to differences in design structure, operating principles, and technological capabilities, produce distinct noise characteristics during operation. Thrust rating refers to the current engine thrust level, such as 70%, 85%, or 100%. Therefore, when constructing a dynamic noise source physical model, it is necessary to tailor it to the engine model, thrust rating, and flight phase.

[0073] It should be noted that the dynamic sound power level of the noise source is calculated using the following formula:

[0074]

[0075]

[0076] in, is the first i The reference sound power level corresponding to each thrust level; It is the real-time thrust level of the engine; For the engine i thrust levels; is the speed correction term; is the aircraft's real-time airspeed; is the reference airspeed; is the dynamic sound power level.

[0077] Step S13: Construct a sound source propagation attenuation physical model including a spherical wave model and a cylindrical wave model. When the aircraft altitude is greater than a preset altitude, the spherical wave model is used; otherwise, the cylindrical wave model is used. Based on the sound source propagation attenuation physical model, the geometric attenuation is calculated according to the sound propagation distance, and the lateral diffraction correction factor is calculated according to the aircraft roll angle, the diffraction edge length, and the incident angle.

[0078] The geometric attenuation of sound propagation is the core of aircraft noise prediction. Its essence is to calculate the diffusion loss of sound energy with distance through the geometric characteristics of sound wave propagation.

[0079] First, when constructing the physical model for sound source propagation attenuation, a dynamic switching mechanism was introduced. The preset altitude can be set to 300 meters. When the aircraft altitude is greater than 300 meters, it is considered to be in the high-altitude domain. When the aircraft altitude is 300 meters or less, it is considered to be in the low-altitude domain. Therefore, when the aircraft altitude is greater than 300 meters, the spherical wave model is activated. This is because at high altitudes, sound waves diffuse approximately evenly in all directions, which conforms to the propagation characteristics of spherical waves. This formula can be used to calculate the sound pressure level attenuation caused by the diffusion of sound energy on the spherical surface as the propagation distance increases in the high-altitude domain. At altitudes ≤300 meters, the cylindrical wave model is switched to. In low-altitude environments, due to the influence of factors such as the ground, the propagation of sound waves more closely resembles the characteristics of cylindrical waves. Compared with the spherical wave attenuation model, the cylindrical wave attenuation more accurately reflects the change in sound pressure level as sound waves diffuse with distance in low-altitude environments, improving the accuracy of near-field predictions.

[0080] The real-time adjustment of the model according to the actual flight altitude can better fit the actual situation of sound wave propagation at different altitudes and ensure the accuracy of the geometric attenuation calculation.

[0081] It should be noted that the geometric attenuation is calculated based on the sound propagation distance, including:

[0082] If the aircraft altitude is greater than the preset altitude, the spherical wave attenuates , which means that every time the distance doubles , the sound pressure level is attenuated by 6dB.

[0083] If the aircraft altitude is lower than the preset altitude, the cylindrical wave attenuates , which means that every time the distance doubles , the sound pressure level is attenuated by 3dB.

[0084] in, is the geometric attenuation; and They represent different propagation distances respectively.

[0085] Preferably, in another embodiment, before calculating the lateral diffraction correction factor, the Fresnel number of the sound propagation path can also be calculated based on the obstacle height, the sound wave wavelength and the total propagation distance; if the Fresnel number is greater than a preset value, the sound propagation path is an effective diffraction path, and the lateral diffraction correction factor is calculated; otherwise, the lateral diffraction correction factor is not calculated.

[0086] This is because the aircraft roll angle β To change the sound wave diffraction path, it is necessary to determine whether the sound propagation path in the physical model is a valid diffraction path.

[0087] The Fresnel number is calculated by the following formula :

[0088]

[0089] In this formula, is the height of obstacles (such as buildings and mountains); is the total propagation distance; is the wavelength of the sound wave, calculated using the following formula:

[0090]

[0091] is the speed of sound, is the frequency.

[0092] After calculating the Fresnel number, if If ≥0.6, it means that the sound propagation path is an effective diffraction path and the contribution of diffracted sound energy is retained; otherwise, it is considered to be completely blocked.

[0093] After determining it as a valid diffraction path, calculate the lateral diffraction correction factor:

[0094] It should be noted that the lateral diffraction correction factor is calculated using the following formula:

[0095]

[0096] in, is the lateral diffraction correction factor; β is the aircraft roll angle; is the diffraction edge length; is the angle of incidence. The diffraction edge length refers to the critical edge length of the obstacle that is relevant to diffraction when a sound wave encounters an obstacle during propagation. The angle of incidence is the angle between the direction of sound wave propagation and the normal of the obstacle surface at the diffraction point.

[0097] Traditional geometric attenuation models ignore diffraction and wavefront distortion during sound wave propagation when calculating aircraft noise propagation. However, in real-world situations, especially in low-altitude, complex terrain environments, these factors significantly impact noise propagation. Therefore, this embodiment calculates a lateral diffraction correction factor to more accurately calculate noise propagation in complex environments.

[0098] Step S14: construct a terrain impact mapping physical model based on the pre-acquired three-dimensional digital elevation model; calculate the sound path difference based on the terrain impact mapping physical model, and calculate the terrain attenuation according to the sound path difference, the wavelength of the sound wave, the straight-line distance from the sound source to the receiving point, and the ground reflection coefficient.

[0099] A three-dimensional digital elevation model (DEM) is a model that digitally expresses the surface morphology of the terrain.

[0100] It should be noted that the calculation of terrain attenuation includes:

[0101] The sound path difference is calculated using the following formula:

[0102]

[0103] in, The acoustic path difference refers to the difference in length of different propagation paths during the propagation of sound waves; is the horizontal distance from the sound source to the obstacle; is the horizontal distance from the obstacle to the receiving point; is the vertical height from the obstacle vertex to the propagation path.

[0104] Use the Fresnel number to determine whether it is a valid diffraction path:

[0105]

[0106] in, , is the total propagation distance. When , the diffraction path is retained, otherwise it is considered to be completely blocked.

[0107] The barrier attenuation is calculated according to the following formula:

[0108]

[0109] in, is the wavelength of the sound wave; is the barrier attenuation. In this formula, , represents the barrier attenuation when there is a single obstacle; , which represents the barrier attenuation when there are multiple obstacles.

[0110] The reflection attenuation is calculated according to the following formula:

[0111]

[0112] in, is the ground reflection coefficient, which is 0.1 for asphalt pavement, 0.4 for grass, and 0.9 for water surface; is the straight-line distance from the sound source to the receiving point; is the reflection attenuation.

[0113]

[0114] The barrier attenuation and reflection attenuation The sum of .

[0115] Step S15: constructing a meteorological dynamic correction physical model according to the real-time meteorological data; and calculating the atmospheric attenuation based on the meteorological dynamic correction physical model according to the atmospheric absorption coefficient and the straight-line distance from the sound source to the receiving point.

[0116] Real-time fusion of temperature, humidity and wind speed gradient data, through the atmospheric absorption coefficient And refraction effect model to correct the sound field propagation loss.

[0117] It should be noted that the calculation of atmospheric attenuation includes:

[0118] The atmospheric absorption coefficient is calculated using the line-by-line integration model:

[0119]

[0120] in, is the atmospheric absorption coefficient; is the absorption coefficient of the gas component; is the density correction function; and From the HITRAN spectral database.

[0121] The atmospheric attenuation is calculated using the following formula:

[0122]

[0123] in, is the atmospheric attenuation; is the straight-line distance from the sound source to the receiving point.

[0124] Step S16: Calculate the predicted sound pressure level at the receiving point based on the dynamic sound power level of the noise source, the geometric attenuation, the atmospheric attenuation, the terrain attenuation, and the lateral diffraction correction factor.

[0125] The calculation formula is:

[0126]

[0127] in, Predicted sound pressure level at the receiving point, is the sound power level; is the geometric attenuation; is the atmospheric attenuation; is the terrain attenuation; is the lateral diffraction correction factor.

[0128] Step S17: Acquire actual ground noise monitoring data at the receiving point, perform spatiotemporal matching between the actual monitoring data and the predicted sound pressure level value, and optimize the parameters of the sound propagation path in each physical model according to the matching results.

[0129] It is understood that the technical solution presented in this invention can obtain aircraft track data and real-time meteorological data, construct a dynamic noise source physical model, a sound source propagation attenuation physical model, a terrain impact mapping physical model, and a meteorological dynamic correction physical model. Based on the constructed physical models, the dynamic sound power level, lateral diffraction correction factor, geometric attenuation, terrain attenuation, and atmospheric attenuation of the noise source are calculated, and the sound pressure level prediction value at the receiving point is calculated. The technical solution presented in this invention can also match the measured values ​​with the predicted values ​​in time and space to optimize the parameters of the sound propagation path in each physical model. The technical solution presented in this invention dynamically couples the measured values ​​with the predicted values, and at the same time, considers a variety of complex factors, significantly improving the prediction accuracy in complex environments and providing technical support for civil aviation noise control.

[0130] It should be noted that the sound source propagation attenuation physical model, the terrain impact mapping physical model and the meteorological dynamic correction physical model all include sound propagation paths.

[0131] Optimize the parameters of the sound propagation path, including:

[0132] Step S21: Using a Kalman filter algorithm to perform spatiotemporal matching between the actual monitoring data and the sound pressure level prediction value.

[0133] In specific practice, track tracking radars, ground noise monitoring arrays and meteorological sensors are deployed to match multi-source data with predicted data in time and space to perform multi-source data fusion.

[0134] Step S22: Compare the matched actual monitoring data with the sound pressure level prediction value, and generate a dynamic correction gain matrix based on the comparison result. .

[0135] Step S23: Calculate the corrected state vector according to the actual monitoring data, the sound pressure level prediction value and the dynamic correction gain matrix:

[0136]

[0137] in, is the corrected state vector; is the predicted value, is the measured value of the ground noise monitoring array; is the observation matrix, which is determined by the sensor layout topology and maps the state vector to the observation space according to the sensor layout.

[0138] State vector Contains parameters such as sound source intensity and propagation loss. As time goes by and new observation data are incorporated, the state vector Continuously updated to more accurately reflect the actual state of the noise field.

[0139] Step S24: updating the parameters of the sound propagation path in the sound source propagation attenuation physical model, the terrain influence mapping physical model, and the meteorological dynamic correction physical model according to the state vector.

[0140] It can be understood that the technical solution shown in this embodiment adopts multi-source data fusion technology to integrate and analyze data of different types and sources, correct the parameters of the sound propagation path in the physical model, improve the integrity, accuracy and reliability of the data, provide richer and higher-quality data support for noise prediction, and help improve the accuracy of the prediction results.

[0141] It should be noted that the method further includes:

[0142] A convolutional neural network is constructed, and historical noise spectra, historical meteorological parameters, and terrain feature maps are input into the convolutional neural network to output a sound field inversion weight matrix. When constructing a sound source propagation attenuation physical model, a sound source propagation attenuation physical model, a terrain influence mapping physical model, and a meteorological dynamic correction physical model, the sound field inversion weight matrix is ​​used to adjust the model parameters.

[0143] The constructed convolutional neural network (CNN) receives historical noise spectrum, meteorological parameters and terrain feature maps as input layer, and outputs the sound field inversion weight matrix WThe convolutional neural network structure includes: feature extraction layer: 3×3 convolution kernel extracts spatial frequency features; pooling layer: maximum pooling reduces the dimension to 1 / 4 resolution; fully connected layer: generates weight matrix to dynamically adjust model parameters.

[0144] CNN input and output: ,in, is the historical noise spectrum data (dimensions: time, frequency, sound pressure level); is the meteorological parameter matrix (temperature, humidity, wind speed); is the terrain feature tensor (3D elevation data).

[0145] In another embodiment, a device for calculating and predicting airport aircraft noise is provided, comprising:

[0146] A main controller, and a memory connected to the main controller;

[0147] The memory stores program instructions;

[0148] The main controller is used to execute program instructions stored in the memory and perform any of the above methods.

[0149] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0150] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" is at least two.

[0151] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0152] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0153] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0154] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0155] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0156] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0157] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for calculating and predicting airport aircraft noise, characterized in that: include: Obtain aircraft track data and real-time weather data; Based on the engine model, thrust level and flight phase, a dynamic noise source physical model is established; based on the dynamic noise source physical model, the dynamic sound power level of the noise source is calculated according to the aircraft track data; A physical model of sound source propagation attenuation, including both spherical and cylindrical wave models, is constructed. When the aircraft altitude is greater than a preset altitude, the spherical wave model is used; otherwise, the cylindrical wave model is used. Based on the physical model of sound source propagation attenuation, the geometric attenuation is calculated according to the sound propagation distance, and the lateral diffraction correction factor is calculated according to the aircraft roll angle, diffraction edge length, and incident angle. Constructing a terrain impact mapping physical model based on a pre-acquired three-dimensional digital elevation model; calculating an acoustic path difference based on the terrain impact mapping physical model, and calculating a terrain attenuation based on the acoustic path difference, the wavelength of the sound wave, the straight-line distance from the sound source to the receiving point, and the ground reflection coefficient; Constructing a meteorological dynamic correction physical model based on the real-time meteorological data; calculating the atmospheric attenuation based on the meteorological dynamic correction physical model according to the atmospheric absorption coefficient and the straight-line distance from the sound source to the receiving point; The predicted sound pressure level at the receiving point is calculated based on the dynamic sound power level, geometric attenuation, atmospheric attenuation, terrain attenuation and lateral diffraction correction factor of the noise source; The actual monitoring data of the ground noise at the receiving point is obtained, the actual monitoring data is matched with the predicted sound pressure level value in time and space, and the parameters of the sound propagation path in each physical model are optimized according to the matching results.

2. The method according to claim 1, characterized in that The sound source propagation attenuation physical model, the terrain impact mapping physical model and the meteorological dynamic correction physical model all include sound propagation paths; Before calculating the lateral diffraction correction factor and terrain attenuation, it also includes: calculating the Fresnel number of the sound propagation path based on the obstacle height, the sound wave wavelength and the total propagation distance; if the Fresnel number is greater than a preset value, the sound propagation path is an effective diffraction path, and the lateral diffraction correction factor is calculated; otherwise, the lateral diffraction correction factor is not calculated.

3. The method according to claim 2, characterized in that Performing spatiotemporal matching of the actual monitoring data with the predicted sound pressure level value, and optimizing the parameters of the sound propagation path in each physical model according to the matching result, including: Using a Kalman filter algorithm to perform spatiotemporal matching between the actual monitoring data and the sound pressure level prediction value; Comparing the matched actual monitoring data with the sound pressure level prediction value, and generating a dynamic correction gain matrix according to the comparison result; Calculating a corrected state vector according to the actual monitoring data, the sound pressure level prediction value and the dynamic correction gain matrix; The parameters of the sound propagation path in the sound source propagation attenuation physical model, the terrain influence mapping physical model and the meteorological dynamic correction physical model are updated according to the state vector.

4. The method according to claim 1, wherein Also includes: Constructing a convolutional neural network, inputting historical noise spectra, historical meteorological parameters, and terrain feature maps into the convolutional neural network, and outputting a sound field inversion weight matrix; When constructing a sound source propagation attenuation physical model, a sound source propagation attenuation physical model, a terrain influence mapping physical model, and a meteorological dynamic correction physical model, the sound field inversion weight matrix is ​​used to adjust model parameters.

5. The method according to claim 1, wherein The dynamic sound power level of the noise source is calculated using the following formula: in, is the first i The reference sound power level corresponding to each thrust level; It is the real-time thrust level of the engine; For the engine i thrust levels; is the speed correction term; is the aircraft's real-time airspeed; is the reference airspeed; is the dynamic sound power level.

6. The method according to claim 1, characterized in that Calculate the geometric attenuation based on the sound propagation distance, including: If the aircraft altitude is greater than the preset altitude, the spherical wave attenuation calculation formula is: If the aircraft altitude is lower than the preset altitude, the cylindrical wave attenuation calculation formula is: in, is the geometric attenuation; and They represent different propagation distances respectively.

7. The method according to claim 1, characterized in that The lateral diffraction correction factor is calculated using the following formula: in, is the lateral diffraction correction factor; β is the aircraft roll angle; is the diffraction edge length; is the angle of incidence.

8. The method according to claim 1, characterized in that Calculate terrain attenuation, including: The sound path difference is calculated using the following formula: in, is the sound path difference; is the horizontal distance from the sound source to the obstacle; is the horizontal distance from the obstacle to the receiving point; is the vertical height from the obstacle vertex to the propagation path; The barrier attenuation is calculated according to the following formula: in, is the wavelength of the sound wave; is the barrier attenuation; The reflection attenuation is calculated according to the following formula: in, is the ground reflection coefficient; is the straight-line distance from the sound source to the receiving point; is the reflection attenuation; The sum of barrier attenuation and reflection attenuation is taken as terrain attenuation.

9. The method according to claim 1, characterized in that Calculate atmospheric attenuation, including: The atmospheric absorption coefficient is calculated using the line-by-line integration model: in, is the atmospheric absorption coefficient; is the absorption coefficient of the gas component; is the density correction function; The atmospheric attenuation is calculated using the following formula: in, is the atmospheric attenuation; is the straight-line distance from the sound source to the receiving point.

10. An airport aircraft noise calculation and prediction device, characterized in that: include: A main controller, and a memory connected to the main controller; The memory stores program instructions; The main controller is used to execute program instructions stored in the memory and perform the method according to any one of claims 1 to 9.

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