Intelligent power amplifier management method based on vehicle-mounted sound system and related device

Abstract

The invention discloses an intelligent power amplifier management method based on a vehicle-mounted sound system and a related device, and the method comprises the steps: firstly, obtaining vehicle window opening and closing state data, a current vehicle speed and an external wind speed; then, in-vehicle airflow velocity distribution data, turbulence intensity and in-vehicle airflow mode combination are calculated; then, simulating a sound propagation path by using an acoustic simulation model to obtain sound intensity distribution data of each area in the vehicle; if the sound intensity distribution data of each area in the vehicle indicates that the volume difference between different areas in the vehicle is greater than a preset volume difference threshold value, generating a first optimization parameter set of the sound amplification system based on a target noise source position positioned through a sound source positioning technology and eddy current noise data; and finally, extracting a volume gain value and an equalizer optimization parameter from the first optimization parameter set, and determining an output parameter of the sound system. Therefore, the problem of regional audition experience difference caused by airflow interference is solved, and all users in the vehicle can obtain better audition experience.

Description

Technical Field

[0001] This application relates to the field of electronic information technology, and in particular to a smart amplifier management method and related device based on vehicle audio systems. Background Technology

[0002] With the continuous improvement of automotive intelligence, in-vehicle audio systems, as a core component for enhancing the driving experience, are increasingly in need of dynamically adapting to changes in the in-vehicle and external environments.

[0003] Currently, most car audio system management solutions are based on preset volume and sound effects. However, during vehicle operation, changes in the opening and closing of windows have a significant impact on sound propagation. Existing management solutions cannot identify the influence of air disturbances on the direction and intensity of sound propagation. As a result, when the windows are open, the sound is relatively clear in some areas of the car, while the sound in other areas is significantly masked by wind noise, leading to a poor listening experience for the user.

[0004] Therefore, how to ensure that all users in the car can have a good listening experience with the windows open has become a problem that needs to be solved. Summary of the Invention

[0005] To address the aforementioned issues, this application provides a smart amplifier management method and related device based on in-vehicle audio, enabling all users in the vehicle to enjoy a better listening experience even with the windows open.

[0006] The embodiments of this application disclose the following technical solutions: In a first aspect, embodiments of this application provide a smart amplifier management method based on in-vehicle audio systems, the method comprising: Acquire data on the opening and closing status of vehicle windows, current vehicle speed, and external wind speed; Based on the window opening / closing status data, the current vehicle speed, and the external wind speed, calculate the airflow velocity distribution data, turbulence intensity, and airflow pattern combination inside the vehicle. Based on the in-vehicle airflow velocity distribution data, the turbulence intensity, and the combination of in-vehicle airflow patterns, an acoustic simulation model is used to simulate the sound propagation path and obtain the sound intensity distribution data of each area inside the vehicle. If the sound intensity distribution data of each area inside the vehicle indicates that the volume difference between different areas inside the vehicle is greater than a preset volume difference threshold, then based on the location of the target noise source located by the sound source localization technology and the eddy noise data, wind noise spectrum data, background noise level and audio frequency overlap area range are generated. Based on the wind noise spectrum data, the background noise level, and the audio frequency overlap region, a first set of optimized parameters for the power amplifier system is generated; the first set of optimized parameters includes the low-frequency gain parameters and high-frequency gain parameters of the power amplifier system. The volume gain value and equalizer optimization parameters are extracted from the first set of optimization parameters to determine the output parameters of the audio system; the output parameters include the output power of each channel of each speaker in the audio system.

[0007] Optionally, the step of generating wind noise spectrum data, background noise level, and audio frequency overlap region range based on the target noise source location located by sound source localization technology and eddy noise data includes: Based on the location of the target noise source located by sound source localization technology and the corresponding eddy noise data, the wind noise source and wind noise spectrum are obtained. If the wind noise source is a single source, the wind noise frequency range is obtained by performing spectral analysis on the wind noise spectrum; if the wind noise source is multiple sources, independent component analysis is used to separate the different wind noise sources in the wind noise spectrum, and spectral analysis is performed on the wind noise spectra of different wind noise sources to obtain the wind noise frequency range. Based on the aforementioned wind noise frequency range, a psychoacoustic model is used to analyze subjective perception characteristics and obtain the tone masking intensity. Based on the wind noise frequency range and the tone masking intensity, wind noise spectrum data is generated; Background noise in the wind noise spectrum data is separated by adaptive filtering technology, and the background noise level is calculated. Based on the wind noise spectrum data and the background noise level, the frequency overlap region range is extracted using spectral analysis technology.

[0008] Optionally, the step of calculating the in-vehicle airflow velocity distribution data, turbulence intensity, and in-vehicle airflow pattern combination based on the window opening / closing status data, the current vehicle speed, and the external wind speed includes: Based on the window opening and closing status data, the drag coefficient and air intake volume are calculated using a fluid dynamics model. If the car window is open, the airflow velocity distribution data and turbulence intensity inside the car are calculated using the finite element analysis method based on the current vehicle speed and the external wind speed. Based on the in-vehicle airflow velocity distribution data and the turbulence intensity, the K-means clustering algorithm is used to determine the in-vehicle airflow pattern combination; the in-vehicle airflow pattern combination includes at least one of the lateral airflow pattern, longitudinal airflow pattern and spiral airflow pattern, as well as the percentage of each airflow pattern in the in-vehicle airflow pattern combination.

[0009] Optionally, the step of using an acoustic simulation model to simulate the sound propagation path based on the in-vehicle airflow velocity distribution data, the turbulence intensity, and the combination of in-vehicle airflow patterns to obtain sound intensity distribution data for various areas inside the vehicle includes: Based on the in-vehicle airflow velocity distribution data, the turbulence intensity, and the combination of in-vehicle airflow patterns, an acoustic simulation model is used to simulate the sound propagation path, and the sound propagation characteristics are obtained based on the sound attenuation effect and refraction effect. Based on the sound propagation characteristics and the collected in-vehicle echo data, the echo pattern distribution is extracted through time-domain analysis. Based on the sound propagation characteristics and the echo pattern distribution, the sound intensity distribution data of each area inside the vehicle is calculated using a spectrum analysis algorithm.

[0010] Optionally, after extracting the volume gain value and equalizer optimization parameters from the first set of optimization parameters to determine the output parameters of the audio system, the method further includes: Based on the preset sound quality analysis standards, the real-time collected listening feedback data is analyzed to obtain the timbre distortion and volume spatial distribution. Based on the timbre distortion, the volume spatial distribution, and the pre-configured target sound effect parameters, a first deviation value between the actual volume and the target volume and a second deviation value between the actual timbre equalization and the target timbre equalization are calculated; the target sound effect parameters include the target volume and the target timbre equalization. If the first deviation value is greater than a preset first threshold, or the second deviation value is greater than a preset second threshold, then a second optimization parameter is generated based on the first deviation value and / or the second deviation value. The output parameters of the audio system are updated based on the second optimized parameters.

[0011] Optionally, the step of analyzing real-time collected listening feedback data according to preset sound quality analysis standards to obtain timbre distortion and volume spatial distribution includes: Real-time acquisition of listening feedback data; The listening feedback data is analyzed in the frequency domain using the Fast Fourier Transform algorithm to extract a frequency domain feature set; the frequency domain feature set includes the volume and sound field distribution characteristics of each frequency band. Based on the frequency domain feature set and the preset sound quality analysis standard, the timbre distortion value is determined; The sound field distribution of the frequency domain feature set is analyzed, and the balance between the left and right channels and the volume ratio of different regions are calculated to obtain the volume spatial distribution.

[0012] Optionally, generating the second optimization parameter based on the first deviation value and / or the second deviation value includes: Based on the first deviation value and / or the second deviation value, the parameter adjustment amount of the audio system is calculated using a linear regression algorithm; Based on the parameter adjustment amount, a second optimized parameter is generated.

[0013] Optionally, after generating the second optimized parameters based on the parameter adjustment amount and the dynamic airflow coupling parameters, the method further includes: The target gain value is calculated based on the dynamic airflow coupling parameters and the power amplifier output gain formula. Update the power amplifier output parameters based on the target gain value.

[0014] Secondly, embodiments of this application provide an intelligent amplifier management device based on a car audio system, the device comprising: The acquisition module is used to acquire data on the opening and closing status of vehicle windows, current vehicle speed, and external wind speed. The calculation module is used to calculate the airflow velocity distribution data, turbulence intensity, and airflow pattern combination inside the vehicle based on the window opening / closing status data, the current vehicle speed, and the external wind speed. The simulation module is used to simulate the sound propagation path using an acoustic simulation model based on the in-vehicle airflow velocity distribution data, the turbulence intensity, and the combination of in-vehicle airflow patterns, to obtain sound intensity distribution data for each area inside the vehicle. The generation module is used to generate wind noise spectrum data, background noise level and audio frequency overlap area range based on the target noise source location located by sound source localization technology and eddy noise data if the sound intensity distribution data of each area in the vehicle indicates that the volume difference between different areas in the vehicle is greater than a preset volume difference threshold. The optimization module is used to generate a first set of optimized parameters for the power amplifier system based on the wind noise spectrum data, the background noise level, and the range of overlapping audio frequencies; the first set of optimized parameters includes the low-frequency gain parameters and high-frequency gain parameters of the power amplifier system. The determination module is used to extract volume gain values ​​and equalizer optimization parameters from the first set of optimization parameters to determine the output parameters of the audio system; the output parameters include the output power of each channel of each speaker in the audio system.

[0015] Thirdly, embodiments of this application provide an intelligent amplifier management device based on in-vehicle audio, the device comprising: a memory and a processor; The memory is used to store program code and transmit the program code to the processor; The processor is configured to execute, according to the program code, the steps of the intelligent amplifier management method based on vehicle audio according to any embodiment of the first aspect.

[0016] Compared with the prior art, this application has the following beneficial effects: On the one hand, by acquiring the opening and closing status of the car windows, the current vehicle speed, and the external wind speed, the system can calculate the airflow velocity distribution, turbulence intensity, and airflow pattern combinations inside the vehicle in real time. This allows for accurate identification of changes in the airflow characteristics inside the vehicle caused by changes in window status and vehicle speed fluctuations, providing a precise environmental parameter basis for subsequent sound propagation analysis and solving the problem that existing systems cannot respond to dynamic changes in the internal and external environment in real time. On the other hand, by using an acoustic simulation model based on airflow characteristics to simulate the sound propagation path, the system can effectively obtain the sound intensity distribution in different areas inside the vehicle, thereby accurately identifying the volume differences between different areas and providing a basis for targeted optimization. This avoids the neglect of actual sound field differences by traditional preset strategies. Furthermore, By locating the target noise source using sound source localization technology and combining it with eddy current noise data to generate wind noise spectrum, background noise level, and frequency overlap areas, the influence range and characteristics of wind noise can be accurately pinpointed. Then, by adjusting the low-frequency and high-frequency gain parameters in the first set of optimization parameters, the output power of each channel of the audio system in different areas can be determined, effectively reducing the obscuring of effective audio by wind noise, improving sound clarity, and specifically balancing the volume and sound effects in different areas such as the front and rear seats. This solves the problem of regional differences in listening experience caused by airflow interference and wind noise, significantly improving the stability and consistency of the overall audio effect in the vehicle, so that all users in the vehicle can have a good listening experience with the windows open. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating an intelligent amplifier management method based on in-vehicle audio systems, provided as an embodiment of this application; Figure 2 A flowchart illustrating another intelligent amplifier management method based on in-vehicle audio provided in an embodiment of this application; Figure 3 A schematic diagram of an intelligent power amplifier management device based on a car audio system, provided for an embodiment of this application; Figure 4 This is a structural diagram of an intelligent power amplifier management device based on a car audio system, provided as an embodiment of this application. Detailed Implementation

[0019] The intelligent amplifier management method and related device based on car audio provided in this application can be used in the field of electronic information. The above is only an example and does not limit the application field of the intelligent amplifier management method and related device based on car audio provided in this application.

[0020] The terms "first," "second," "third," and "fourth," etc., used in this application specification, claims, and drawings are used to distinguish different objects, not to limit a specific order.

[0021] In the embodiments of this application, the terms "as an example" or "for example" are used to indicate that they are examples, illustrations, or explanations. Any embodiment or design that is described as "as an example" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Specifically, the use of terms such as "as an example" or "for example" is intended to present the relevant concepts in a specific manner.

[0022] The terminology used in the implementation section of this application is for the purpose of explaining specific embodiments of this application only, and is not intended to limit this application.

[0023] In-vehicle audio management systems play a crucial role in modern automobiles. With the increasing intelligence of vehicles, audio systems need to dynamically adapt to changes in the internal and external environment, especially during vehicle operation, where changes in the opening and closing of windows significantly impact sound propagation. This dynamic environment places higher technical demands on the adaptive control of audio systems, making it a research hotspot in the field of intelligent vehicles.

[0024] Existing in-vehicle audio system solutions rely heavily on preset volume and sound effect adjustment strategies when dealing with changes in the opening and closing of car windows. They lack real-time monitoring and accurate response to airflow inside the vehicle and external wind noise interference. This results in the audio system being unable to effectively identify changes in the sound propagation path when the windows are open or closed, and it is also difficult to make targeted adjustments based on the masking effect of wind noise in different tonal ranges, thus affecting the passenger's listening experience.

[0025] The disturbance of airflow can alter the direction and intensity of sound waves propagation within a vehicle cabin, and this effect is particularly complex when different window opening configurations are used. For example, when only the front windows are open, airflow may be concentrated primarily at the front of the cabin, causing rear passengers to hear music significantly masked by wind noise, while front passengers can hear relatively clearly. Furthermore, the masking effect of wind noise varies significantly across different tonal ranges; low-frequency sounds may be attenuated by wind noise, while high-frequency sounds may be distorted due to airflow disturbances. Accurately capturing the interference of airflow on sound propagation in the complex and ever-changing in-car environment and optimizing sound effects in real time, monitoring the airflow interference caused by changes in window opening and closing status, and precisely adjusting volume and sound parameters to ensure a stable and consistent listening experience for both front and rear passengers across different tonal ranges has become a core technical challenge.

[0026] In view of this, embodiments of this application provide a smart amplifier management method based on in-vehicle audio. This method first acquires data on the opening and closing status of vehicle windows, current vehicle speed, and external wind speed. Then, based on the window opening and closing status data, current vehicle speed, and external wind speed, it calculates the airflow velocity distribution data, turbulence intensity, and airflow pattern combination within the vehicle. Next, based on the airflow velocity distribution data, turbulence intensity, and airflow pattern combination, it uses an acoustic simulation model to simulate the sound propagation path, obtaining sound intensity distribution data for each area within the vehicle. If the sound intensity distribution data for each area within the vehicle indicates the volume difference between different areas within the vehicle... If the difference exceeds a preset volume difference threshold, wind noise spectrum data, background noise level, and audio frequency overlap range are generated based on the target noise source location located by sound source localization technology and eddy current noise data. Then, based on the wind noise spectrum data, background noise level, and audio frequency overlap range, a first optimized parameter set for the power amplifier system is generated. The first optimized parameter set includes the low-frequency gain parameters and high-frequency gain parameters of the power amplifier system. Finally, the volume gain value and equalizer optimization parameters are extracted from the first optimized parameter set to determine the output parameters of the audio system. The output settings include the output power of each channel of each speaker in the audio system.

[0027] Therefore, on the one hand, by acquiring the opening and closing status of the car windows, the current vehicle speed, and the external wind speed, the airflow velocity distribution, turbulence intensity, and airflow pattern combination inside the vehicle can be calculated in real time. This allows for accurate identification of changes in the airflow characteristics inside the vehicle caused by changes in window status and vehicle speed fluctuations, providing a precise environmental parameter basis for subsequent sound propagation analysis and solving the problem that existing systems cannot respond to dynamic changes in the internal and external environment in real time. On the other hand, by using an acoustic simulation model based on airflow characteristics to simulate the sound propagation path, the sound intensity distribution in different areas inside the vehicle can be effectively obtained, thereby accurately identifying the volume differences between different areas and providing a basis for targeted optimization, avoiding the neglect of actual sound field differences by traditional preset strategies. Furthermore, By using sound source localization technology to locate the target noise source and combining it with eddy current noise data to generate wind noise spectrum, background noise level, and frequency overlap area, the influence range and characteristics of wind noise can be accurately locked. Then, by adjusting the low-frequency and high-frequency gain parameters in the first optimization parameter set, the output power of each channel of the audio system in different areas can be determined, effectively reducing the obscuring of effective audio by wind noise, improving sound clarity, and specifically balancing the volume and sound effects of different areas such as the front and rear seats. This solves the problem of regional listening experience differences caused by airflow interference and wind noise, significantly improving the stability and consistency of the overall audio effect in the car, so that all users in the car can have a good listening experience when the windows are open.

[0028] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0029] See Figure 1 The figure is a flowchart of a smart amplifier management method based on a car audio system provided in an embodiment of this application. The method includes: S101: Obtain data on the opening and closing status of vehicle windows, current vehicle speed, and external wind speed.

[0030] As an example, sensor data can be used to acquire window opening and closing status data, which includes, but is not limited to, window opening angles and sunroof opening status. For example, window opening and closing status data includes: the opening angles of the four doors are: 30° to the left front, 20° to the right front, 15° to the left rear rear, and 10° to the right rear rear; the sunroof is open to 50%.

[0031] The current vehicle speed can be obtained through the vehicle speed sensor, for example, the current vehicle speed is 80km / h.

[0032] External wind speed can be obtained through a meteorological API. For example, the external wind speed is 5 m / s, and the direction of the external wind is opposite to the vehicle speed.

[0033] S102: Based on the window opening / closing status data, current vehicle speed, and external wind speed, calculate the airflow velocity distribution data, turbulence intensity, and airflow pattern combination inside the vehicle.

[0034] For example, the drag coefficient and air intake can be calculated first based on the open / closed state data of the car windows using a fluid dynamics model; if the car windows are open, the airflow velocity distribution data and turbulence intensity inside the car can be calculated using the finite element analysis method based on the current vehicle speed and the external wind speed; finally, based on the airflow velocity distribution data and turbulence intensity inside the car, the K-means clustering algorithm can be used to determine the combination of airflow patterns inside the car.

[0035] As an example, the drag coefficient Cd can be obtained through CFD (Computational Fluid Dynamics) simulation using a k-ε turbulence model. Here, k represents turbulent kinetic energy, and ε represents turbulent dissipation rate. Specifically, the opening and closing status data of the car windows can be input into the k-ε turbulence model to obtain the drag coefficient Cd. For example, Cd = 0.35 when the front left window is at 30°, Cd = 0.33 when the front right window is at 20°, Cd = 0.31 when the rear left window is at 15°, Cd = 0.30 when the rear right window is at 10°, and Cd increases by 0.05 to 0.35 when the sunroof is 50% open.

[0036] Air intake volume can be calculated using the law of conservation of mass. Assuming a window area of ​​0.5 m², the air intake volume Q = vehicle speed × opening area × cos(opening angle). For the front left window, Q = 80 × 0.5 × cos30° = 34.6 m³ / h. Similarly, for the front right window, Q = 23.5 m³ / h, for the rear left window, Q = 12.3 m³ / h, for the rear right window, Q = 8.7 m³ / h, and for the sunroof, Q = 15 m³ / h.

[0037] The airflow velocity distribution can be simulated using LES (Large Eddy Simulation). For example, when the vehicle speed is 80 km / h and the external wind speed is 5 m / s, the relative speed is 85 m / s, and the peak airflow velocity at the vehicle window is 90 m / s. Furthermore, the turbulence intensity I = 0.1 can be obtained through the k-ε turbulence model.

[0038] In some embodiments, based on the vehicle's geometric model, CFD software (such as ANSYS Fluent) can be used to simulate the airflow inside the vehicle to obtain the airflow velocity of each of the multiple regions, which are then combined into an airflow velocity distribution. The multiple regions include at least the front row area where the driver is located and the rear row area behind the driver.

[0039] The in-vehicle airflow pattern combination includes at least one of the following: lateral airflow pattern, longitudinal airflow pattern, and spiral airflow pattern, as well as the percentage of each airflow pattern in the in-vehicle airflow pattern combination. The in-vehicle airflow pattern combination can be obtained by analyzing the data collected by the sensor array using the K-means clustering algorithm. For example, if the K-means clustering algorithm shows that the lateral airflow runs from the front left window to the rear right window at a speed of 2 m / s, the longitudinal airflow is along the central axis of the vehicle at 1.5 m / s, and the spiral airflow forms at the sunroof with an angular velocity of 0.5 rad / s, then the in-vehicle airflow pattern combination is 60% lateral airflow, 30% longitudinal airflow, and 10% spiral airflow.

[0040] Optionally, if the windows are closed, the vehicle's sealing performance and interior pressure can be calculated using a vehicle cabin sealing performance test model to obtain data on airflow velocity distribution and turbulence intensity. Specifically, with the windows closed, the vehicle's sealing performance can be simulated using a pressure decay test; the interior pressure can be measured using a pressure sensor.

[0041] S103: Based on the in-vehicle airflow velocity distribution data, turbulence intensity, and in-vehicle airflow pattern combination, the acoustic simulation model is used to simulate the sound propagation path and obtain the sound intensity distribution data of each area inside the vehicle.

[0042] For example, the sound propagation path can be simulated first using an acoustic simulation model based on the in-vehicle airflow velocity distribution data, turbulence intensity, and in-vehicle airflow pattern combination. Based on the sound attenuation and refraction effects, the sound propagation characteristics can be obtained. Then, based on the sound propagation characteristics and the collected in-vehicle echo data, the echo pattern distribution can be extracted through time-domain analysis. Finally, based on the sound propagation characteristics and echo pattern distribution, the sound intensity distribution data of each area in the vehicle can be calculated using a spectrum analysis algorithm.

[0043] Specifically, based on the in-vehicle airflow velocity distribution data, turbulence intensity, and in-vehicle airflow pattern combination, an acoustic simulation model can be used, combined with speaker position parameters and amplifier output power, to simulate the sound propagation path, consider the attenuation effect of turbulence and boundary layer on high-frequency sound, and calculate the sound attenuation degree in each region; and consider the sound refraction effect to calculate the deflection angle of sound waves in the turbulent region, and obtain the sound refraction angle in each region, such as the refraction angle of the front row region being 2° and the refraction angle of the rear row region being 1.5°; thus, the sound propagation characteristics considering the sound attenuation effect and the refraction effect can be obtained.

[0044] Then, based on the sound propagation characteristics, in-vehicle echo data was collected, and time-domain analysis was used to extract the echo time series to obtain the echo pattern distribution. Finally, based on the sound propagation characteristics and echo pattern distribution, a spectrum analysis algorithm was applied to calculate the sound intensity distribution data of each area inside the vehicle.

[0045] In some implementations, if the noise level in the sound intensity distribution data exceeds a preset noise threshold, a wavelet transform filtering algorithm can be used to reduce the noise in the sound intensity distribution data. Based on the denoised sound intensity distribution, the refraction effect and boundary layer interference are analyzed. The acoustic boundary element method is applied to calculate the degree of interference of the boundary layer on sound propagation, obtaining boundary interference correction data. Then, based on the boundary interference correction data, a genetic algorithm is used to adjust the power output and optimize the sound intensity distribution.

[0046] S104: If the sound intensity distribution data of each area inside the vehicle indicates that the volume difference between different areas inside the vehicle is greater than the preset volume difference threshold, then based on the location of the target noise source located by the sound source localization technology and the eddy noise data, wind noise spectrum data, background noise level and audio frequency overlap area range are generated.

[0047] As an example, based on the sound intensity distribution data of each area inside the vehicle, the frequency characteristics of the volume in each area can be extracted using the Fast Fourier Transform algorithm to determine the volume differences between different areas inside the vehicle.

[0048] For example, four microphones can be placed inside the vehicle, located near the headrests of the front seats and above the rear seats, with a spacing of 0.3 meters between adjacent microphones. The sampling frequency is set to 48kHz. An in-vehicle microphone array consisting of these four microphones collects sound signals to capture the full frequency range of the in-vehicle sound field. The sound signals recorded by the microphone array can reflect the differences in sound intensity between the driver's area, passenger area, and rear seat area. The collected raw signals include ambient noise, in-vehicle echo data, and speaker output. A sound intensity distribution map generated based on the raw signals and sound intensity distribution data can visually display the sound pressure level distribution in different areas of the vehicle; for example, the sound pressure level in the driver's area is approximately 82dB, while in the rear seats it is approximately 76dB.

[0049] In one possible implementation, when dividing the volume of the front and rear passenger areas from the sound intensity distribution map, a spatial grid partitioning method can be used to divide the vehicle into two main areas: the front and rear. The average sound pressure level (SPL) of each area is then calculated separately. The front passenger area, being closer to the speakers, typically has a higher volume (e.g., 82 dB), while the rear passenger area, affected by distance attenuation, has a lower volume (e.g., 76 dB). The regional volume data can be extracted through time-series analysis combined with spatial coordinates to generate a regionalized SPL dataset.

[0050] When using the Fast Fourier Transform (FFT) algorithm to extract frequency features, the acquired time-domain signal can be converted into the frequency domain to analyze the dominant frequency components. Frequency features reflect the echo patterns inside the vehicle; for example, due to the larger rear space and longer sound wave reflection paths, low-frequency echoes are more pronounced in the rear area.

[0051] If the sound intensity distribution data of different areas inside the vehicle indicates that the volume difference between different areas inside the vehicle is greater than the preset volume difference threshold, then specifically: first, based on the location of the target noise source located by sound source localization technology and the corresponding eddy noise data, the wind noise source and wind noise spectrum can be obtained.

[0052] For example, sound source localization technology can be used to analyze the field distribution of multi-channel sound pressure signals collected by a microphone array to calculate the sound source location coordinates, locate the target noise source, and determine the source of wind noise. Then, based on the location of the target noise source, the corresponding eddy noise data is extracted, and the spectral characteristics of the eddy noise data are analyzed using fast Fourier transform to obtain the wind noise spectrum.

[0053] For example, using sound source localization technology, an array of microphones can be used to collect ambient noise. This array can consist of eight microphones arranged in a circle with a radius of 0.5 meters and a sampling frequency of 44.1 kHz. The Time Difference of Arrival (TDOA) algorithm is used to calculate the time difference between the signals received by each microphone. Combined with the wave velocity of 343 m / s, the spatial coordinates of the main noise source are located, thus determining the source of wind noise. For instance, if the location result is the noise source coordinates (2, 1, 0.5), it indicates that the noise source is located on the left front of the vehicle. By combining eddy current noise data, the pressure fluctuations caused by airflow are analyzed. CFD simulation software is used to simulate the airflow over the vehicle surface, and the signals collected by the microphones are processed using Fast Fourier Transform (FFT) to obtain the wind noise spectrum.

[0054] If the wind noise source is a single source, the wind noise frequency range is obtained by performing spectral analysis on the wind noise spectrum; if the wind noise source is multiple sources, independent component analysis is used to separate the different wind noise sources in the wind noise spectrum, and spectral analysis is performed on the wind noise spectra of different sources to obtain the wind noise frequency range.

[0055] Then, based on the wind noise frequency range, a psychoacoustic model is used to analyze subjective perception characteristics and obtain the tone masking intensity. Subsequently, based on the wind noise frequency range and tone masking intensity, wind noise spectrum data is generated. Next, adaptive filtering technology is used to separate background noise from the wind noise spectrum data, and the background noise level is calculated. If the background noise level is lower than the wind noise peak value, it indicates that wind noise is the dominant noise. Finally, based on the wind noise spectrum data and the background noise level, spectral analysis technology is used to extract the frequency overlap region.

[0056] For example, a spectral overlap detection algorithm can be used to compare the power spectral density (PSD) of wind noise and background noise based on wind noise spectral data and background noise levels, calculate the overlapping region and the range of the intersection of power spectral densities within it, and obtain the frequency overlap region range. Combined with tone masking intensity, a Bark-scale psychoacoustic model is used to calculate the masking threshold of wind noise, which can filter out background noise below the masking threshold, ensuring that wind noise signals dominate within the overlapping region.

[0057] S105: Based on wind noise spectrum data, background noise level, and the range of overlapping audio frequencies, generate the first set of optimized parameters for the power amplifier system.

[0058] The first set of optimized parameters includes the low-frequency gain parameters and high-frequency gain parameters of the power amplifier system.

[0059] Specifically, the gain parameter for the low-frequency band (20-400Hz) can be generated using a linear interpolation algorithm and set according to the PSD value of the lower limit of the audio frequency overlap area at 400Hz, for example, set to +3dB to enhance low-frequency clarity; the gain parameter for the high-frequency band (900-2000Hz) is set based on the PSD value of the upper limit of the audio frequency overlap area at 900Hz, for example, set to -2dB to suppress high-frequency interference.

[0060] The first set of optimized parameters may include, but is not limited to, the low-frequency and high-frequency gain parameters of the power amplifier system. For example, it may include a low-frequency gain of 3dB, a high-frequency gain of -2dB, and equalizer bandwidth parameters (Q value of 1.5). This first set of optimized parameters is applied in real-time by a digital signal processor (DSP), using a finite impulse response (FIR) filter with an order of 128 to ensure a smooth frequency response. The entire process is automated by the signal processing module, forming a closed-loop logic from spectrum analysis to parameter adjustment for precise and efficient audio optimization.

[0061] S106: Extract the volume gain value and equalizer optimization parameters from the first set of optimized parameters to determine the output parameters of the audio system.

[0062] The output parameters include the output power of each channel of each speaker in the audio system.

[0063] For example, a JSON parser can be used to parse the first optimized parameter set file in JSON format, extracting volume gain values ​​(e.g., main channel gain 5.2dB, surround channel gain 3.8dB) and equalizer optimization parameters (e.g., 2.5dB boost at 80Hz, 1.2dB attenuation at 1kHz midrange, and 3.0dB boost at 10kHz high frequency), allowing the first optimized parameters to be accurately mapped to system memory. Then, considering the location of each speaker in the audio system, the output power of each channel of each speaker in the audio system can be calculated.

[0064] Therefore, in this embodiment, on the one hand, by acquiring the window opening / closing status, current vehicle speed, and external wind speed, the airflow velocity distribution, turbulence intensity, and airflow pattern combination inside the vehicle can be calculated in real time. This accurately identifies changes in the airflow characteristics inside the vehicle caused by changes in window status, vehicle speed fluctuations, etc., providing a precise environmental parameter basis for subsequent sound propagation analysis and solving the problem that existing systems cannot respond to dynamic changes in the environment inside and outside the vehicle in real time. On the other hand, by using an acoustic simulation model to simulate the sound propagation path based on airflow characteristics, the sound intensity distribution in various areas inside the vehicle can be effectively obtained, thereby accurately identifying the volume differences between different areas and providing a basis for targeted optimization. This avoids the neglect of actual sound field differences by traditional preset strategies. On the other hand, by using sound source localization technology to locate the target noise source and combining it with eddy current noise data to generate wind noise spectrum, background noise level and frequency overlap area, the influence range and characteristics of wind noise can be accurately locked. Then, by adjusting the low-frequency and high-frequency gain parameters in the first optimization parameter set, the output power of each channel of the audio system in different areas can be determined, effectively reducing the obscuring of effective audio by wind noise, improving sound clarity, and specifically balancing the volume and sound effects of different areas such as the front and rear seats. This solves the problem of regional listening experience differences caused by airflow interference and wind noise, significantly improving the stability and coherence of the overall audio effect in the car, so that all users in the car can have a better listening experience when the windows are open.

[0065] In some embodiments, after determining the output parameters of the audio system, the output parameters of the audio system can be further optimized. Specifically, see [link to relevant documentation]. Figure 2 The figure is a flowchart of another intelligent amplifier management method based on in-vehicle audio provided in an embodiment of this application. The method includes: S201: Based on the preset sound quality analysis standards, analyze the real-time collected listening feedback data to obtain the timbre distortion and volume spatial distribution.

[0066] For example, listening feedback data can be collected in real time using an in-vehicle microphone array; then, the listening feedback data is analyzed in the frequency domain using a fast Fourier transform algorithm to extract a frequency domain feature set; the frequency domain feature set includes the volume and sound field distribution characteristics of each frequency band; then, based on the frequency domain feature set and a preset sound quality analysis standard, the timbre distortion value is determined; finally, the sound field distribution of the frequency domain feature set is analyzed to calculate the balance of the left and right channels and the volume ratio of different regions, thus obtaining the volume spatial distribution.

[0067] For example, the timbre distortion index can be analyzed by extracting the total harmonic distortion (THD) from the frequency domain feature set. Then, based on the timbre distortion index and the preset sound quality analysis standard, the difference between the actual spectrum and the standard spectrum can be compared to determine the timbre distortion value.

[0068] Optionally, if the timbre distortion value is greater than a preset distortion threshold, a sound field distribution analysis can be performed on the frequency domain feature set to calculate the balance of the left and right channels and the volume ratio of different regions, thereby obtaining the volume spatial distribution.

[0069] Taking a left-right channel difference of <3dB as a passing standard, if the left channel is 71.2dB and the right channel is 70.8dB, then the left-right channel difference is 0.4dB, and the left-right channel balance is passing. Taking a division into front and rear rows with a target volume ratio of 1:0.85 as an example, if the actual measured volume of the front row is 72.5dB and the volume of the rear row is 60.3dB, then the volume ratio of the front and rear rows is 1:0.83, which deviates from the target value by 2%.

[0070] S202: Based on timbre distortion, volume spatial distribution, and pre-configured target sound effect parameters, calculate the first deviation value between the actual volume and the target volume, and the second deviation value between the actual timbre equalization and the target timbre equalization.

[0071] As an example, when the timbre distortion exceeds a preset distortion threshold, dynamic airflow coupling parameters can be acquired through in-vehicle sensors to obtain an airflow parameter set including the rate of change of airflow velocity and the amplitude of pressure fluctuations. Then, a linear regression algorithm is used to perform correlation analysis on the airflow parameter set and the spatial distribution difference values. Based on pre-configured target sound effect parameters, a first deviation value between the actual volume and the target volume, and a second deviation value between the actual timbre equalization and the target timbre equalization are calculated. The target sound effect parameters include the target volume and the target timbre equalization.

[0072] For example, dynamic airflow coupling parameters can be acquired through additional sensors. The rate of change of airflow velocity can be calculated using the differential method, and the pressure fluctuation amplitude can be measured by a pressure sensor, assuming a fluctuation range of ±50Pa. The target sound effect parameters are set as a target volume of 75dB and a target timbre equalization of <5dB deviation across all frequency bands. The actual volume is 72.5dB, resulting in a volume deviation of 2.5dB. The timbre equalization analysis shows a deviation of 4.8dB in the 2kHz frequency band, which meets the standard. The deviation value is calculated using the root mean square error (RMSE), yielding the first deviation value RMSE1 for the multi-frequency volume and the second deviation value RMSE2 for the multi-frequency timbre.

[0073] S203: If the first deviation value is greater than the preset first threshold, or the second deviation value is greater than the preset second threshold, then a second optimization parameter is generated based on the first deviation value and / or the second deviation value.

[0074] Specifically, the parameter adjustment amount of the audio system can be calculated first based on the first deviation value and / or the second deviation value using a linear regression algorithm; then, based on the parameter adjustment amount, the second optimized parameters can be generated.

[0075] For example, the audio system receives an input volume of 80 dB, with a timbre deviation of 0.15 and a preset deviation threshold of 0.1. The target volume is 85 dB, the actual volume is 80 dB, the volume deviation (first deviation) is 5 dB, exceeding the volume deviation threshold of 2 dB; the timbre deviation (second deviation) is 0.15, exceeding the timbre deviation threshold of 0.1. Based on this, according to the magnitude and direction of the deviation, a proportional-integral-derivative (PID) control algorithm is used to adjust the parameters. If the calculated volume adjustment is 3 dB and the timbre adjustment is 0.09, then the second optimization parameters can be generated: increase the volume gain by 3 dB to 83 dB, decrease the low-frequency gain by 0.05, and increase the mid-frequency gain by 0.04 to optimize the timbre.

[0076] Optionally, the target gain value can be calculated based on dynamic airflow coupling parameters and the amplifier output gain formula. For example, the amplifier output gain formula is: Target gain value = Base gain × (1 - Airflow drag coefficient). Taking a base gain of 10 watts and an airflow drag coefficient of 0.02 as an example, the target gain value is 9.8 watts. Then, the amplifier output parameters are updated based on the target gain value to provide users with a better listening experience.

[0077] S204: Update the output parameters of the audio system based on the second optimized parameters.

[0078] For example, based on the second optimization parameter, the output volume of the audio system can be updated to 83 dB, the low-frequency gain can be reduced by 0.05, the mid-frequency gain can be increased by 0.04, and the timbre deviation can be reduced to 0.06.

[0079] Optionally, real-time feedback data can be extracted from the actual output volume and actual output sound effects, and compared with a preset deviation threshold to determine whether the output meets the target requirements, so as to dynamically adjust the output parameters of the audio system in real time and provide users with a better listening experience.

[0080] See Figure 3 The figure is a schematic diagram of an intelligent amplifier management device based on a car audio system provided in an embodiment of this application. The device includes: The acquisition module 301 is used to acquire data on the opening and closing status of the vehicle windows, the current vehicle speed, and the external wind speed. The calculation module 302 is used to calculate the airflow velocity distribution data, turbulence intensity, and airflow pattern combination inside the vehicle based on the window opening and closing status data, the current vehicle speed, and the external wind speed. Simulation module 303 is used to simulate the sound propagation path using an acoustic simulation model based on in-vehicle airflow velocity distribution data, turbulence intensity, and in-vehicle airflow pattern combination, to obtain sound intensity distribution data for various areas inside the vehicle. The generation module 304 is used to generate wind noise spectrum data, background noise level and audio frequency overlap area range based on the target noise source location located by sound source localization technology and eddy noise data if the sound intensity distribution data of each area in the vehicle indicates that the volume difference between different areas in the vehicle is greater than a preset volume difference threshold. Optimization module 305 is used to generate a first set of optimized parameters for the power amplifier system based on wind noise spectrum data, background noise level, and the range of overlapping audio frequencies; the first set of optimized parameters includes the low-frequency gain parameters and high-frequency gain parameters of the power amplifier system. The determination module 306 is used to extract the volume gain value and equalizer optimization parameters from the first set of optimization parameters to determine the output parameters of the audio system; the output parameters include the output power of each channel of each speaker in the audio system.

[0081] Therefore, in this embodiment, on the one hand, by acquiring the window opening / closing status, current vehicle speed, and external wind speed, the airflow velocity distribution, turbulence intensity, and airflow pattern combination inside the vehicle can be calculated in real time. This accurately identifies changes in the airflow characteristics inside the vehicle caused by changes in window status, vehicle speed fluctuations, etc., providing a precise environmental parameter basis for subsequent sound propagation analysis and solving the problem that existing systems cannot respond to dynamic changes in the environment inside and outside the vehicle in real time. On the other hand, by using an acoustic simulation model to simulate the sound propagation path based on airflow characteristics, the sound intensity distribution in various areas inside the vehicle can be effectively obtained, thereby accurately identifying the volume differences between different areas and providing a basis for targeted optimization. This avoids the neglect of actual sound field differences by traditional preset strategies. On the other hand, by using sound source localization technology to locate the target noise source and combining it with eddy current noise data to generate wind noise spectrum, background noise level and frequency overlap area, the influence range and characteristics of wind noise can be accurately locked. Then, by adjusting the low-frequency and high-frequency gain parameters in the first optimization parameter set, the output power of each channel of the audio system in different areas can be determined, effectively reducing the obscuring of effective audio by wind noise, improving sound clarity, and specifically balancing the volume and sound effects of different areas such as the front and rear seats. This solves the problem of regional listening experience differences caused by airflow interference and wind noise, significantly improving the stability and coherence of the overall audio effect in the car, so that all users in the car can have a better listening experience when the windows are open.

[0082] Optionally, the calculation module 302 is specifically used for: calculating the drag coefficient and air intake volume based on the window opening / closing status data using a fluid dynamics model; if the window is open, calculating the airflow velocity distribution data and turbulence intensity inside the vehicle using a finite element analysis method based on the current vehicle speed and external wind speed; determining the airflow pattern combination inside the vehicle using a K-means clustering algorithm based on the airflow velocity distribution data and turbulence intensity inside the vehicle; the airflow pattern combination inside the vehicle includes at least one of a lateral airflow pattern, a longitudinal airflow pattern, and a spiral airflow pattern, as well as the percentage of each airflow pattern in the airflow pattern combination inside the vehicle.

[0083] Optionally, the simulation module 303 is specifically used for: simulating the sound propagation path using an acoustic simulation model based on in-vehicle airflow velocity distribution data, turbulence intensity, and in-vehicle airflow pattern combinations; obtaining sound propagation characteristics based on sound attenuation and refraction effects; extracting echo pattern distribution through time-domain analysis based on the sound propagation characteristics and the collected in-vehicle echo data; and calculating the sound intensity distribution data of each area inside the vehicle using a spectrum analysis algorithm based on the sound propagation characteristics and echo pattern distribution.

[0084] Optionally, the generation module 304 is specifically used for: obtaining wind noise source and wind noise spectrum based on the location of the target noise source located by sound source localization technology and the corresponding eddy noise data; if the wind noise source is a single source, obtaining the wind noise frequency range by performing spectral analysis on the wind noise spectrum; if the wind noise source is multiple sources, separating different wind noise sources in the wind noise spectrum using independent component analysis, and performing spectral analysis on the wind noise spectra of different wind noise sources to obtain the wind noise frequency range; based on the wind noise frequency range, analyzing subjective perception characteristics using a psychoacoustic model to obtain the tone masking intensity; generating wind noise spectrum data based on the wind noise frequency range and tone masking intensity; separating background noise in the wind noise spectrum data using adaptive filtering technology and calculating the background noise level; and extracting the frequency overlap region range using spectral analysis technology based on the wind noise spectrum data and the background noise level.

[0085] Optionally, another intelligent amplifier management device based on a car audio system provided in this application embodiment further includes: an analysis module, a deviation calculation module, a parameter generation module, and an update module; the analysis module is used to analyze real-time collected listening feedback data according to preset sound quality analysis standards to obtain timbre distortion and volume spatial distribution; the deviation calculation module is used to calculate a first deviation value between the actual volume and the target volume and a second deviation value between the actual timbre equalization and the target timbre equalization based on the timbre distortion, volume spatial distribution, and pre-configured target sound effect parameters; the target sound effect parameters include target volume and target timbre equalization; the parameter generation module is used to generate a second optimization parameter based on the first deviation value and / or the second deviation value if the first deviation value is greater than a preset first threshold or the second deviation value is greater than a preset second threshold; the update module is used to update the output parameters of the audio system based on the second optimization parameters.

[0086] Optionally, the analysis module is specifically used for: real-time acquisition of listening feedback data; frequency domain analysis of the listening feedback data using a fast Fourier transform algorithm to extract a frequency domain feature set; the frequency domain feature set includes volume and sound field distribution characteristics of each frequency band; determining the timbre distortion value based on the frequency domain feature set and preset sound quality analysis standards; and performing sound field distribution analysis on the frequency domain feature set to calculate the left and right channel balance and the volume ratio of different regions, thereby obtaining the volume spatial distribution.

[0087] Optionally, the parameter generation module is specifically used to: calculate the parameter adjustment amount of the audio system using a linear regression algorithm based on the first deviation value and / or the second deviation value; and generate a second optimized parameter based on the parameter adjustment amount.

[0088] Optionally, another intelligent power amplifier management device based on car audio provided in this application embodiment further includes: a gain update module, used to calculate a target gain value based on dynamic airflow coupling parameters and power amplifier output gain formula; and update the power amplifier output parameters based on the target gain value.

[0089] See Figure 4 The figure is a structural diagram of an intelligent power amplifier management device based on a car audio system provided in an embodiment of this application. The device includes a memory 401 and a processor 402.

[0090] Memory 401: Used to store program code and transfer program code to the processor.

[0091] Processor 402: Used to execute the steps of the above-described intelligent amplifier management method based on car audio according to the instructions in the program code.

[0092] In addition, this application also provides a computer-readable storage medium storing computer instructions, which, when executed on a smart amplifier management device based on a car audio system, cause the smart amplifier management device based on a car audio system to perform the steps of the aforementioned smart amplifier management method based on a car audio system.

[0093] It should be noted that the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for the device and storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method embodiments. The device and storage medium embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components indicated as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment solution according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0094] The above description is merely one specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for intelligent amplifier management based on in-vehicle audio systems, characterized in that, The method includes: Acquire data on the opening and closing status of vehicle windows, current vehicle speed, and external wind speed; Based on the window opening / closing status data, the current vehicle speed, and the external wind speed, calculate the airflow velocity distribution data, turbulence intensity, and airflow pattern combination inside the vehicle. Based on the in-vehicle airflow velocity distribution data, the turbulence intensity, and the combination of in-vehicle airflow patterns, an acoustic simulation model is used to simulate the sound propagation path and obtain the sound intensity distribution data of each area inside the vehicle. If the sound intensity distribution data of each area inside the vehicle indicates that the volume difference between different areas inside the vehicle is greater than a preset volume difference threshold, then based on the location of the target noise source located by the sound source localization technology and the eddy noise data, wind noise spectrum data, background noise level and audio frequency overlap area range are generated. Based on the wind noise spectrum data, the background noise level, and the audio frequency overlap region, a first set of optimized parameters for the power amplifier system is generated; the first set of optimized parameters includes the low-frequency gain parameters and high-frequency gain parameters of the power amplifier system. The volume gain value and equalizer optimization parameters are extracted from the first set of optimization parameters to determine the output parameters of the audio system; the output parameters include the output power of each channel of each speaker in the audio system.

2. The method according to claim 1, characterized in that, The generation of wind noise spectrum data, background noise level, and audio frequency overlap region range based on the target noise source location located by sound source localization technology and eddy noise data includes: Based on the location of the target noise source located by sound source localization technology and the corresponding eddy noise data, the wind noise source and wind noise spectrum are obtained. If the wind noise source is a single source, the wind noise frequency range is obtained by performing spectral analysis on the wind noise spectrum; if the wind noise source is multiple sources, independent component analysis is used to separate the different wind noise sources in the wind noise spectrum, and spectral analysis is performed on the wind noise spectra of different wind noise sources to obtain the wind noise frequency range. Based on the aforementioned wind noise frequency range, a psychoacoustic model is used to analyze subjective perception characteristics and obtain the tone masking intensity. Based on the wind noise frequency range and the tone masking intensity, wind noise spectrum data is generated; Background noise in the wind noise spectrum data is separated by adaptive filtering technology, and the background noise level is calculated. Based on the wind noise spectrum data and the background noise level, the frequency overlap region range is extracted using spectral analysis technology.

3. The method according to claim 1, characterized in that, The calculation of in-vehicle airflow velocity distribution data, turbulence intensity, and in-vehicle airflow pattern combinations based on the window opening / closing status data, the current vehicle speed, and the external wind speed includes: Based on the window opening and closing status data, the drag coefficient and air intake volume are calculated using a fluid dynamics model. If the car window is open, the airflow velocity distribution data and turbulence intensity inside the car are calculated using the finite element analysis method based on the current vehicle speed and the external wind speed. Based on the in-vehicle airflow velocity distribution data and the turbulence intensity, the K-means clustering algorithm is used to determine the in-vehicle airflow pattern combination; the in-vehicle airflow pattern combination includes at least one of the lateral airflow pattern, longitudinal airflow pattern and spiral airflow pattern, as well as the percentage of each airflow pattern in the in-vehicle airflow pattern combination.

4. The method according to claim 1, characterized in that, Based on the in-vehicle airflow velocity distribution data, the turbulence intensity, and the combination of in-vehicle airflow patterns, an acoustic simulation model is used to simulate the sound propagation path to obtain sound intensity distribution data for various areas inside the vehicle, including: Based on the in-vehicle airflow velocity distribution data, the turbulence intensity, and the combination of in-vehicle airflow patterns, an acoustic simulation model is used to simulate the sound propagation path, and the sound propagation characteristics are obtained based on the sound attenuation effect and refraction effect. Based on the sound propagation characteristics and the collected in-vehicle echo data, the echo pattern distribution is extracted through time-domain analysis. Based on the sound propagation characteristics and the echo pattern distribution, the sound intensity distribution data of each area inside the vehicle is calculated using a spectrum analysis algorithm.

5. The method according to claim 1, characterized in that, After extracting the volume gain value and equalizer optimization parameters from the first set of optimized parameters to determine the output parameters of the audio system, the method further includes: Based on the preset sound quality analysis standards, the real-time collected listening feedback data is analyzed to obtain the timbre distortion and volume spatial distribution. Based on the timbre distortion, the volume spatial distribution, and the pre-configured target sound effect parameters, a first deviation value between the actual volume and the target volume and a second deviation value between the actual timbre equalization and the target timbre equalization are calculated; the target sound effect parameters include the target volume and the target timbre equalization. If the first deviation value is greater than a preset first threshold, or the second deviation value is greater than a preset second threshold, then a second optimization parameter is generated based on the first deviation value and / or the second deviation value. The output parameters of the audio system are updated based on the second optimized parameters.

6. The method according to claim 5, characterized in that, The process of analyzing real-time collected listening feedback data according to preset sound quality analysis standards to obtain timbre distortion and volume spatial distribution includes: Real-time acquisition of listening feedback data; The listening feedback data is analyzed in the frequency domain using the Fast Fourier Transform algorithm to extract a frequency domain feature set; the frequency domain feature set includes the volume and sound field distribution characteristics of each frequency band. Based on the frequency domain feature set and the preset sound quality analysis standard, the timbre distortion value is determined; The sound field distribution of the frequency domain feature set is analyzed, and the balance between the left and right channels and the volume ratio of different regions are calculated to obtain the volume spatial distribution.

7. The method according to claim 5, characterized in that, The step of generating the second optimization parameter based on the first deviation value and / or the second deviation value includes: Based on the first deviation value and / or the second deviation value, the parameter adjustment amount of the audio system is calculated using a linear regression algorithm; Based on the parameter adjustment amount, a second optimized parameter is generated.

8. The method according to claim 7, characterized in that, After generating the second optimized parameters based on the parameter adjustment amount and the dynamic airflow coupling parameters, the method further includes: The target gain value is calculated based on the dynamic airflow coupling parameters and the power amplifier output gain formula. Update the power amplifier output parameters based on the target gain value.

9. A smart amplifier management device based on vehicle audio, characterized in that, The device includes: The acquisition module is used to acquire data on the opening and closing status of vehicle windows, current vehicle speed, and external wind speed. The calculation module is used to calculate the airflow velocity distribution data, turbulence intensity, and airflow pattern combination inside the vehicle based on the window opening / closing status data, the current vehicle speed, and the external wind speed. The simulation module is used to simulate the sound propagation path using an acoustic simulation model based on the in-vehicle airflow velocity distribution data, the turbulence intensity, and the combination of in-vehicle airflow patterns, to obtain sound intensity distribution data for each area inside the vehicle. The generation module is used to generate wind noise spectrum data, background noise level and audio frequency overlap area range based on the target noise source location located by sound source localization technology and eddy noise data if the sound intensity distribution data of each area in the vehicle indicates that the volume difference between different areas in the vehicle is greater than a preset volume difference threshold. The optimization module is used to generate a first set of optimized parameters for the power amplifier system based on the wind noise spectrum data, the background noise level, and the range of overlapping audio frequencies; the first set of optimized parameters includes the low-frequency gain parameters and high-frequency gain parameters of the power amplifier system. The determination module is used to extract volume gain values ​​and equalizer optimization parameters from the first set of optimization parameters to determine the output parameters of the audio system; the output parameters include the output power of each channel of each speaker in the audio system.

10. A smart amplifier management device based on vehicle audio systems, characterized in that, The device includes: a memory and a processor; The memory is used to store program code and transmit the program code to the processor; The processor is configured to execute the steps of the intelligent amplifier management method based on vehicle audio according to any one of claims 1-8, based on the program code.

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