In-vehicle active noise reduction method based on coupling of sound field base and engine frequency

Abstract

The invention relates to an in-vehicle active noise reduction method based on coupling of a sound field base and engine frequency, and belongs to the technical field of noise control. The method comprises the following steps: acquiring working condition data, an in-vehicle noise signal and vehicle body vibration data, and integrating the data into a unified data frame; performing modal decomposition on an in-vehicle sound field based on a unified data frame, extracting modal features, constructing a dynamically updated sound field modal model, analyzing a response relationship between each order frequency of an engine and a sound field modal, and performing fitting to generate a coupling coefficient matrix; calculating an offset sound wave parameter according to a matrix weight ratio, synthesizing a global comprehensive offset signal, and combining with a real-time noise signal for dynamic correction; driving partition loudspeakers in the vehicle to output offset sound waves, and adjusting the output intensity of the loudspeakers to optimize energy consumption. Accurate coupling of the engine frequency and the in-vehicle sound field mode is achieved, the problem that noise reduction lags behind under the complex working condition in the prior art is solved, and noise reduction uniformity and working condition adaptability are improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of noise control, and particularly relates to an in-vehicle active noise reduction method based on coupling of sound field base and engine frequency. BACKGROUND

[0002] In the field of automotive engineering, in-vehicle noise control is one of the core technical directions for improving driving comfort. Engine noise, as the main low-frequency noise source in the vehicle, has its fundamental, multiple and harmonic frequency components dynamically changing with the engine operating state, which easily causes auditory fatigue and discomfort of the driver and passengers. Therefore, efficient suppression of engine noise has become a research focus in the industry. Currently, active noise reduction technology has been widely used in in-vehicle noise control. The core principle of this technology is to emit sound waves with opposite phase to the noise through a loudspeaker to achieve noise cancellation. Existing technologies are mostly based on fixed-position transfer function models to construct sound field control logic or use single-order frequency compensation to achieve basic noise reduction functions. However, the existing technologies have significant limitations. On the one hand, the correlation analysis of engine frequency and in-vehicle sound field is not deep enough, and single-order frequency tracking or simple linear compensation is mostly used, which makes it difficult to accurately match the dynamic coupling relationship between engine multi-order frequency (fundamental, multiple and harmonic) and in-vehicle sound field modes, resulting in a decrease in noise reduction accuracy under complex working conditions (such as engine load changes and speed fluctuations). On the other hand, the sound field modeling method is fixed, and it mostly relies on pre-set transfer function parameters, which cannot dynamically update the sound field model according to changes in vehicle body vibration and engine operating conditions (such as coolant temperature fluctuations), making it difficult to adapt to real-time changes in the in-vehicle acoustic environment. At the same time, existing technologies lack balance in working condition adaptation and energy consumption optimization. Most schemes do not adjust noise reduction parameters based on real-time engine operating conditions (such as load and temperature), and do not optimize loudspeaker output intensity based on sound field mode distribution when performing zoned noise reduction, which easily causes energy waste or insufficient noise reduction in local areas. SUMMARY

[0003] To solve the above problems in the prior art, the present application provides an in-vehicle active noise reduction method based on coupling of sound field base and engine frequency, The object of the present application can be achieved by the following technical solutions: S1: obtaining working condition data, noise signals of in-vehicle noise frequency bands and vehicle body vibration data; integrating the working condition data, noise signals and vibration data into a unified data frame; S2: based on the unified data frame, performing modal decomposition on the in-vehicle sound field to extract the order, amplitude and phase characteristics of each mode; constructing a dynamically updated sound field modal model and real-time correcting the sound field modal model parameters according to the vehicle body vibration state feedback by the vibration sensor; analyzing the response relationship between engine frequencies of different orders and sound field modes, fitting and generating a coupling coefficient matrix, and quantifying the contribution weight of different order noises to each sound field mode in the matrix elements; S3: Calculate the cancellation acoustic wave parameters corresponding to each sound field mode based on the weight ratio of the elements in the coupling coefficient matrix; synthesize the cancellation acoustic waves of each single mode into a comprehensive cancellation signal for the entire sound field; dynamically correct the cancellation signal based on the noise signal collected in real time. S4: Drives the in-vehicle zone speaker output to cancel sound waves; based on engine load and coolant temperature changes, it pre-corrects the weights of the corresponding order noise in the coupling coefficient matrix, pre-loads noise reduction parameters that match the current operating conditions, and optimizes energy consumption by dynamically adjusting the speaker output intensity in each area.

[0004] Specifically, the process of acquiring operating condition data is as follows: real-time speed fluctuation signals are acquired through the signal interface, and the distribution of the fundamental frequency, multiple frequency, and harmonic frequency is analyzed; fuel injection control signals and intake pressure signals are acquired simultaneously and converted to obtain the current load state; the real-time temperature of the coolant is acquired through the temperature sensing element; various signals are filtered to remove signals with instantaneous fluctuations and abnormalities, and the data is organized into continuous state data in chronological order.

[0005] Specifically, the noise signal in the in-vehicle noise frequency band is used to characterize the in-vehicle sound field state. The specific process is as follows: sound pickup elements are arranged at different seat positions in the vehicle to collect the original sound signal containing engine noise; the original signal is filtered to remove frequency band signals unrelated to engine noise and retain characteristic sound signals related to engine order frequencies; the sound signal in the time domain is converted into a frequency domain signal, and the amplitude and phase information of each frequency component are extracted to obtain a signal set reflecting the sound field characteristics.

[0006] Specifically, the process of integrating the data into a unified data frame is as follows: taking the change node of the engine speed signal as a reference, a unified time stamp is added to the operating condition data, noise signal and vibration data; different information sets are divided according to data type, and the time delay between different acquisition elements is eliminated through a synchronization algorithm; the processed data are integrated in chronological order to form a structured data sequence containing time stamps and corresponding data content.

[0007] As a preferred technical solution of the present invention, the specific process of S2 is as follows: Based on the unified data frame generated in S1, the frequency domain features of the in-vehicle noise signal are extracted, an analysis matrix is ​​constructed using orthogonal basis functions that match the in-vehicle spatial structure, the noise signal is projected onto the matrix, the order information of each sound field mode is separated by feature extraction, and the amplitude of each mode is calculated by combining the signal amplitude and the phase of each mode is determined by phase comparison; vehicle vibration data is extracted from the unified data frame, the intrinsic correlation between the vibration amplitude, frequency and direction and each sound field mode is analyzed, a correlation model is constructed, the vibration data is converted into the basis for modal parameter correction, and parameters such as order weight and phase compensation coefficient in the sound field modal model are adjusted in real time; continuous engine frequency data and corresponding sound field modal response data are extracted from the unified data frame, the engine frequency is used as input and the sound field modal response is used as output, the correlation coefficient between the two is calculated by regression analysis, the correlation coefficients of the same frequency to different modes are grouped together, and the correlation groups of different frequencies are arranged in order to form a coupling coefficient matrix.

[0008] Specifically, the modal decomposition is used to analyze the compositional features of the in-vehicle sound field. The specific process is as follows: extract the frequency domain features of the noise signal from the unified data frame, select orthogonal basis functions to construct the analysis matrix, and select the basis functions based on the structural features of the in-vehicle space; project the noise signal onto the basis function matrix, and separate the order information of each sound field mode through feature extraction methods; determine the amplitude of each mode by combining the amplitude change of the signal, and determine the phase features of each mode through phase comparison analysis.

[0009] Specifically, the real-time correction of the sound field modal model parameters is used to ensure that the model is consistent with the actual sound field. The specific process is as follows: extract vehicle vibration data from the unified data frame, analyze the influence of vibration amplitude, frequency and direction on each sound field mode; construct the correlation between vibration and modal parameters according to the influence law, and convert the vibration data into the correction basis for modal parameters; adjust the relevant parameters in the sound field modal model based on the correction basis to keep the simulated sound field output by the model consistent with the actual collected sound field characteristics.

[0010] Specifically, the coupling coefficient matrix fitting is used to quantify the correlation between engine frequency and acoustic field mode. The specific process is as follows: extract continuous engine frequency data of each order and corresponding acoustic field mode response data from the unified data frame; take the engine order frequency as input and the acoustic field mode response as output, and calculate the correlation coefficient between the order frequency and the acoustic field mode response through regression analysis; group the correlation coefficients of the same order frequency to different modes into one group, and arrange the correlation groups of different orders frequencies in order to form a coupling coefficient matrix.

[0011] Specifically, the calculation of the cancellation acoustic wave parameters is used to determine the key characteristics of the cancellation signal. The specific process is as follows: analyze the proportion of each element in the coupling coefficient matrix to determine the influence weight of different order noises on each sound field mode; the cancellation acoustic wave frequency corresponding to each sound field mode is consistent with the engine order frequency that excites the sound field mode; the amplitude is calculated based on the amplitude of the sound field mode and the corresponding influence weight; the phase is set to be opposite to the phase of the sound field mode to form the cancellation acoustic wave parameters of each mode.

[0012] Specifically, the synthesis process of the integrated cancellation signal is as follows: based on the cancellation acoustic wave parameters of each mode, a single-frequency signal corresponding to each mode is generated; based on the distribution intensity of each sound field mode in different areas of the vehicle, spatial weights are assigned to each single-frequency signal; the weighted single-frequency signals are superimposed and merged into a comprehensive signal containing all mode cancellation information, and converted into an output electrical signal form.

[0013] Specifically, the dynamic correction of the cancellation signal to improve the accuracy of cancellation is carried out as follows: the noise signal acquired in real time is used as a reference standard, and the feedback signal after the comprehensive cancellation signal is output is used as a comparison object; the difference between the noise signal and the feedback signal is compared by a filtering algorithm, and the amplitude and phase correction amounts that need to be adjusted are calculated; the comprehensive cancellation signal is adjusted in real time according to the correction amount, and the adjusted cancellation signal is matched with the noise signal in reverse.

[0014] Specifically, the in-vehicle zoned loudspeakers are used to output directional canceling sound waves. The specific process is as follows: the in-vehicle seats are divided into different acoustic zones, and each zone is equipped with a corresponding loudspeaker group; the comprehensive canceling signal is decomposed into multiple sub-signals according to the sound field modal characteristics of each zone, and each sub-signal corresponds to a zone; the output intensity of each sub-signal is adjusted, and the loudspeaker group forms an enhanced canceling sound wave in the target zone, while naturally attenuating in other zones.

[0015] Specifically, the preloaded noise reduction parameters matching the current operating condition are used to quickly match changes in engine status. The specific process is as follows: establish a database corresponding to engine operating conditions and noise reduction parameters, and store matching parameters under different loads and coolant temperatures; monitor changes in engine load and coolant temperature in real time, and retrieve parameters close to the current operating condition as a basis; and adjust the basic parameters based on the real-time analysis results of the current coupling coefficient matrix.

[0016] The beneficial effects of this invention are as follows: (1) By setting up a sound field modal decomposition and engine multi-order frequency-sound field modal coupling coefficient matrix fitting mechanism based on unified data frame, it can fully integrate engine operating data, in-vehicle noise signal and vehicle vibration data. First, it performs refined modal analysis on the in-vehicle sound field, accurately extracts the order, amplitude and phase characteristics of each sound field mode, and then quantifies the contribution weight of different order engine noise to each sound field mode through the coupling coefficient matrix. This breaks the limitation of existing technology that only relies on single-order frequency compensation or fixed transfer function modeling. It can effectively avoid the noise reduction accuracy attenuation problem caused by engine speed fluctuation and load change. It can not only accurately match the dynamic correlation between engine noise and in-vehicle sound field under complex working conditions, but also ensure that the generated comprehensive cancellation signal covers the entire sound field, reduce the situation of insufficient noise reduction in local areas, and significantly improve the uniformity and accuracy of in-vehicle noise cancellation. (2) By setting up a coupling coefficient matrix pre-correction mechanism linked to engine operating conditions and a dynamic adjustment mechanism for the output intensity of the partitioned speakers, the weight of the corresponding order noise in the coupling coefficient matrix can be corrected in advance based on the real-time load of the engine and the change of coolant temperature. The noise reduction parameters adapted to the current operating conditions are preloaded to avoid the noise reduction response lag when the operating conditions change suddenly. At the same time, the output intensity of the partitioned speakers is dynamically adjusted in combination with the sound field modal distribution characteristics of each area in the vehicle. This ensures the effective coverage of the target area canceled sound waves and reduces the energy waste of speakers in unnecessary areas. It solves the problems of poor operating condition adaptability and difficulty in balancing energy consumption and noise reduction effect in the existing technology. While improving long-term driving comfort, it reduces the overall energy consumption of the active noise cancellation system and extends the range adaptability of the vehicle power supply system. Attached Figure Description

[0017] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0018] Fig. 1 This is an architectural diagram of an in-vehicle active noise reduction method based on the coupling of sound field basis and engine frequency according to the present invention. Fig. 2 This is a data flow diagram of an in-vehicle active noise reduction method based on the coupling of sound field basis and engine frequency according to the present invention. Detailed Implementation

[0019] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0020] Please see Figs. 1-2 An in-vehicle active noise reduction method based on the coupling of sound field basis and engine frequency. S1: Acquire operating condition data, noise signals in the in-vehicle noise frequency band, and vehicle body vibration data; integrate the operating condition data, noise signals, and vibration data into a unified data frame; S2: Based on the unified data frame, perform modal decomposition on the in-vehicle sound field, extract the order, amplitude, and phase characteristics of each mode; construct a dynamically updated sound field modal model, and correct the parameters of the sound field modal model in real time according to the vehicle vibration state fed back by the vibration sensor; analyze the response relationship between the engine's various orders of frequencies and the sound field modes, fit and generate a coupling coefficient matrix, and quantify the contribution weight of different orders of noise to each sound field mode in the matrix; S3: Calculate the cancellation acoustic wave parameters corresponding to each sound field mode based on the weight ratio of the elements in the coupling coefficient matrix; synthesize the cancellation acoustic waves of each single mode into a comprehensive cancellation signal for the entire sound field; dynamically correct the cancellation signal based on the noise signal collected in real time. S4: Drives the in-vehicle zone speaker output to cancel sound waves; based on engine load and coolant temperature changes, it pre-corrects the weights of the corresponding order noise in the coupling coefficient matrix, pre-loads noise reduction parameters that match the current operating conditions, and optimizes energy consumption by dynamically adjusting the speaker output intensity in each area.

[0021] Specifically, the process of acquiring operating condition data is as follows: real-time speed fluctuation signals are acquired through the signal interface, and the distribution of the fundamental frequency, multiple frequency, and harmonic frequency is analyzed; fuel injection control signals and intake pressure signals are acquired simultaneously and converted to obtain the current load state; the real-time temperature of the coolant is acquired through the temperature sensing element; various signals are filtered to remove signals with instantaneous fluctuations and abnormalities, and the data is organized into continuous state data in chronological order.

[0022] In this embodiment, when acquiring operating condition data, the engine speed fluctuation signal is read through the conventional signal interface of the engine control unit (such as the vehicle CAN bus interface), and the load status is calculated by combining the feedback signal of the fuel injection control model and the intake pressure sensor. Temperature data is collected using the engine's built-in coolant temperature sensor, and then the vehicle processor performs basic filtering on these signals to remove obvious interference signals (such as abnormal values ​​caused by instantaneous circuit fluctuations). Finally, the data is organized in chronological order into state data that can be directly called.

[0023] Specifically, the noise signal in the in-vehicle noise frequency band is used to characterize the in-vehicle sound field state. The specific process is as follows: sound pickup elements are arranged at different seat positions in the vehicle to collect the original sound signal containing engine noise; the original signal is filtered to remove frequency band signals unrelated to engine noise and retain characteristic sound signals related to engine order frequencies; the sound signal in the time domain is converted into a frequency domain signal, and the amplitude and phase information of each frequency component are extracted to obtain a signal set reflecting the sound field characteristics.

[0024] Specifically, the process of integrating the data into a unified data frame is as follows: taking the change node of the engine speed signal as a reference, a unified time stamp is added to the operating condition data, noise signal and vibration data; different information sets are divided according to data type, and the time delay between different acquisition elements is eliminated through a synchronization algorithm; the processed data are integrated in chronological order to form a structured data sequence containing time stamps and corresponding data content.

[0025] In this embodiment, when integrating into a unified data frame, the natural change nodes of the engine speed signal (such as the inflection point of speed increase or decrease) are used as the synchronization reference. The operating condition data, noise signal and vibration data are marked with a unified time mark. The time synchronization algorithm commonly used in vehicle systems (such as the alignment algorithm based on hardware clock) is used to eliminate the acquisition delay of different sensors. Then, the data is packaged according to a fixed structure to form a structured data sequence that is easy for subsequent modules to call.

[0026] As a preferred technical solution of the present invention, the specific process of S2 is as follows: Based on the unified data frame generated in S1, the frequency domain features of the in-vehicle noise signal are extracted, an analysis matrix is ​​constructed using orthogonal basis functions that match the in-vehicle spatial structure, the noise signal is projected onto the matrix, the order information of each sound field mode is separated by feature extraction, and the amplitude of each mode is calculated by combining the signal amplitude and the phase of each mode is determined by phase comparison; vehicle vibration data is extracted from the unified data frame, the intrinsic correlation between the vibration amplitude, frequency and direction and each sound field mode is analyzed, a correlation model is constructed, the vibration data is converted into the basis for modal parameter correction, and parameters such as order weight and phase compensation coefficient in the sound field modal model are adjusted in real time; continuous engine frequency data and corresponding sound field modal response data are extracted from the unified data frame, the engine frequency is used as input and the sound field modal response is used as output, the correlation coefficient between the two is calculated by regression analysis, the correlation coefficients of the same frequency to different modes are grouped together, and the correlation groups of different frequencies are arranged in order to form a coupling coefficient matrix.

[0027] Specifically, the modal decomposition is used to analyze the compositional features of the in-vehicle sound field. The specific process is as follows: extract the frequency domain features of the noise signal from the unified data frame, select orthogonal basis functions to construct the analysis matrix, and select the basis functions based on the structural features of the in-vehicle space; project the noise signal onto the basis function matrix, and separate the order information of each sound field mode through feature extraction methods; determine the amplitude of each mode by combining the amplitude change of the signal, and determine the phase features of each mode through phase comparison analysis.

[0028] In this embodiment, when performing modal decomposition on the in-vehicle sound field, the selection of orthogonal basis functions is determined in conjunction with the specific vehicle's interior space structure (such as the compact cabin of a sedan or the spacious cabin of an SUV). For example, cylindrical harmonic basis functions are preferred for narrow cabins, while spherical harmonic basis functions are selected for approximately symmetrical cabins. By projecting the noise signal onto the selected basis function matrix, the modal orders that play a major role in the current sound field are separated, and the amplitude and phase characteristics of each mode are clarified.

[0029] Specifically, the real-time correction of the sound field modal model parameters is used to ensure that the model is consistent with the actual sound field. The specific process is as follows: extract vehicle vibration data from the unified data frame, analyze the influence of vibration amplitude, frequency and direction on each sound field mode; construct the correlation between vibration and modal parameters according to the influence law, and convert the vibration data into the correction basis for modal parameters; adjust the relevant parameters in the sound field modal model based on the correction basis to keep the simulated sound field output by the model consistent with the actual collected sound field characteristics.

[0030] Specifically, the coupling coefficient matrix fitting is used to quantify the correlation between engine frequency and acoustic field mode. The specific process is as follows: extract continuous engine frequency data of each order and corresponding acoustic field mode response data from the unified data frame; take the engine order frequency as input and the acoustic field mode response as output, and calculate the correlation coefficient between the order frequency and the acoustic field mode response through regression analysis; group the correlation coefficients of the same order frequency to different modes into one group, and arrange the correlation groups of different orders frequencies in order to form a coupling coefficient matrix.

[0031] In this embodiment, when fitting the coupling coefficient matrix, the order frequency data (such as the order frequencies during idling, acceleration, and deceleration) of the engine during stable operation and dynamic changes are continuously extracted. At the same time, the sound field modal response at the corresponding moment is recorded. The correlation coefficient is calculated using a regression analysis algorithm (such as stepwise regression) that can be supported by the on-board processor. Then, by comparing the actual vehicle noise measurement results, data groups with large deviations are eliminated to ensure that the matrix can truly reflect the correlation between engine frequency and sound field mode.

[0032] Specifically, the calculation of the cancellation acoustic wave parameters is used to determine the key characteristics of the cancellation signal. The specific process is as follows: analyze the proportion of each element in the coupling coefficient matrix to determine the influence weight of different order noises on each sound field mode; the cancellation acoustic wave frequency corresponding to each sound field mode is consistent with the engine order frequency that excites the sound field mode; the amplitude is calculated based on the amplitude of the sound field mode and the corresponding influence weight; the phase is set to be opposite to the phase of the sound field mode to form the cancellation acoustic wave parameters of each mode.

[0033] Specifically, the synthesis process of the integrated cancellation signal is as follows: based on the cancellation acoustic wave parameters of each mode, a single-frequency signal corresponding to each mode is generated; based on the distribution intensity of each sound field mode in different areas of the vehicle, spatial weights are assigned to each single-frequency signal; the weighted single-frequency signals are superimposed and merged into a comprehensive signal containing all mode cancellation information, and converted into an output electrical signal form.

[0034] Specifically, the dynamic correction of the cancellation signal to improve the accuracy of cancellation is carried out as follows: the noise signal acquired in real time is used as a reference standard, and the feedback signal after the comprehensive cancellation signal is output is used as a comparison object; the difference between the noise signal and the feedback signal is compared by a filtering algorithm, and the amplitude and phase correction amounts that need to be adjusted are calculated; the comprehensive cancellation signal is adjusted in real time according to the correction amount, and the adjusted cancellation signal is matched with the noise signal in reverse.

[0035] Specifically, the in-vehicle zoned loudspeakers are used to output directional canceling sound waves. The specific process is as follows: the in-vehicle seats are divided into different acoustic zones, and each zone is equipped with a corresponding loudspeaker group; the comprehensive canceling signal is decomposed into multiple sub-signals according to the sound field modal characteristics of each zone, and each sub-signal corresponds to a zone; the output intensity of each sub-signal is adjusted, and the loudspeaker group forms an enhanced canceling sound wave in the target zone, while naturally attenuating in other zones.

[0036] In this embodiment, when preloading noise reduction parameters that match the current operating conditions, the database is established based on multiple sets of real vehicle test data, covering the optimal noise reduction parameters under different engine loads (such as no load, half load, and full load) and coolant temperatures (such as initial cold start, normal operating temperature, and high temperature). When retrieving parameters, the parameter group that is closest to the characteristics of the current operating conditions is selected first, and then fine-tuned by combining the calculation results of the real-time coupling coefficient matrix to ensure that the parameters can quickly adapt to the real-time state of the engine.

[0037] Specifically, the preloaded noise reduction parameters matching the current operating condition are used to quickly match changes in engine status. The specific process is as follows: establish a database corresponding to engine operating conditions and noise reduction parameters, and store matching parameters under different loads and coolant temperatures; monitor changes in engine load and coolant temperature in real time, and retrieve parameters close to the current operating condition as a basis; and adjust the basic parameters based on the real-time analysis results of the current coupling coefficient matrix.

[0038] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An in-vehicle active noise reduction method based on the coupling of sound field basis and engine frequency, characterized in that, include: S1: Acquire operating condition data, noise signals in the in-vehicle noise frequency band, and vehicle body vibration data; The operating condition data, noise signals, and vibration data are integrated into a unified data frame; S2: Based on the unified data frame, perform modal decomposition on the in-vehicle sound field and extract the order, amplitude and phase features of each mode; A dynamically updated acoustic field modal model is constructed, and the parameters of the acoustic field modal model are corrected in real time based on the vehicle vibration state fed back by the vibration sensor. The response relationship between the engine's various frequencies and the acoustic field modes is analyzed, and a coupling coefficient matrix is ​​generated by fitting. The elements in the matrix quantify the contribution weight of different order noises to each acoustic field mode. S3: Calculate the cancellation acoustic wave parameters corresponding to each sound field mode based on the weight ratio of the elements in the coupling coefficient matrix; synthesize the cancellation acoustic waves of each single mode into a comprehensive cancellation signal for the entire sound field; dynamically correct the cancellation signal based on the noise signal collected in real time. S4: Drives the in-vehicle zone speaker output to cancel sound waves; based on engine load and coolant temperature changes, it pre-corrects the weights of the corresponding order noise in the coupling coefficient matrix, pre-loads noise reduction parameters that match the current operating conditions, and optimizes energy consumption by dynamically adjusting the speaker output intensity in each area.

2. The method according to claim 1, characterized in that, The specific process for acquiring operating condition data is as follows: real-time speed fluctuation signals are acquired through the signal interface, and the distribution of the fundamental frequency, multiple frequency, and harmonic frequency is analyzed; fuel injection control signals and intake pressure signals are acquired simultaneously and converted to obtain the current load state; the real-time temperature of the coolant is acquired through the temperature sensing element; various signals are filtered to remove signals with instantaneous fluctuations and abnormalities, and the data is organized into continuous state data in chronological order.

3. The method according to claim 1, characterized in that, The noise signal in the in-vehicle noise frequency band is used to characterize the in-vehicle sound field state. The specific process is as follows: sound pickup elements are arranged at different seat positions in the vehicle to collect the original sound signal containing engine noise; the original signal is filtered to remove frequency band signals that are not related to engine noise and retain the characteristic sound signal related to the engine order frequency; the sound signal in the time domain is converted into a frequency domain signal, and the amplitude and phase information of each frequency component are extracted to obtain a set of signals reflecting the sound field characteristics.

4. The method according to claim 1, characterized in that, The specific process of integrating the data into a unified data frame is as follows: taking the change node of the engine speed signal as a reference, a unified time stamp is added to the operating condition data, noise signal and vibration data; different information sets are divided according to data type, and the time delay between different acquisition elements is eliminated through a synchronization algorithm; the processed data are integrated in chronological order to form a structured data sequence containing time stamps and corresponding data content.

5. The method according to claim 1, characterized in that, The modal decomposition is used to analyze the compositional features of the in-vehicle sound field. The specific process is as follows: extract the frequency domain features of the noise signal from the unified data frame, select orthogonal basis functions to construct the analysis matrix, and select the basis functions based on the spatial structure features of the in-vehicle area. The noise signal is projected onto the basis function matrix, and the order information of each sound field mode is separated by feature extraction method. The amplitude of each mode is determined by combining the amplitude change of the signal, and the phase characteristics of each mode are determined by phase comparison analysis.

6. The method according to claim 1, characterized in that, The real-time correction of the sound field modal model parameters is used to ensure that the model is consistent with the actual sound field. The specific process is as follows: extract vehicle vibration data from the unified data frame, analyze the influence of vibration amplitude, frequency and direction on each sound field mode; construct the correlation between vibration and modal parameters according to the influence law, and convert the vibration data into the correction basis for modal parameters; adjust the relevant parameters in the sound field modal model based on the correction basis to keep the simulated sound field output by the model consistent with the actual collected sound field characteristics.

7. The method according to claim 1, characterized in that, The coupling coefficient matrix fitting is used to quantify the correlation between engine frequency and acoustic field mode. The specific process is as follows: extract continuous engine frequency data of each order and corresponding acoustic field mode response data from the unified data frame; take the engine order frequency as the input and the acoustic field mode response as the output, and calculate the correlation coefficient between the order frequency and the acoustic field mode response through regression analysis; group the correlation coefficients of the same order frequency to different modes into one group, and arrange the correlation groups of different orders frequencies in order to form a coupling coefficient matrix.

8. The method according to claim 1, characterized in that, The calculation of the cancellation acoustic wave parameters is used to determine the key characteristics of the cancellation signal. The specific process is as follows: analyze the proportion of each element in the coupling coefficient matrix to determine the influence weight of different order noises on each sound field mode; the cancellation acoustic wave frequency corresponding to each sound field mode is consistent with the engine order frequency that excites the sound field mode; the amplitude is calculated based on the amplitude of the sound field mode and the corresponding influence weight; the phase is set to be opposite to the phase of the sound field mode to form the cancellation acoustic wave parameters of each mode.

9. The method according to claim 1, characterized in that, The specific process of synthesizing the integrated cancellation signal is as follows: based on the cancellation acoustic wave parameters of each mode, a single-frequency signal corresponding to each mode is generated; based on the distribution intensity of each sound field mode in different areas inside the vehicle, spatial weights are assigned to each single-frequency signal. The weighted single-frequency signals are superimposed and merged into a comprehensive signal containing all mode cancellation information, which is then converted into an output electrical signal.

10. The method according to claim 1, characterized in that, The dynamic correction of the cancellation signal is used to improve the accuracy of cancellation. The specific process is as follows: the noise signal collected in real time is used as a reference standard, and the feedback signal after the comprehensive cancellation signal is output is used as a comparison object; the difference between the noise signal and the feedback signal is compared by a filtering algorithm, and the amplitude and phase correction amounts that need to be adjusted are calculated; the comprehensive cancellation signal is adjusted in real time according to the correction amount, and the adjusted cancellation signal is matched with the noise signal in reverse.

11. The method according to claim 1, characterized in that, The in-vehicle zoned loudspeakers are used to output directional canceling sound waves. The specific process is as follows: the in-vehicle seats are divided into different acoustic zones, and each zone is equipped with a corresponding loudspeaker group; the comprehensive canceling signal is decomposed into multiple sub-signals according to the sound field modal characteristics of each zone, and each sub-signal corresponds to a zone; the output intensity of each sub-signal is adjusted, and the loudspeaker group forms an enhanced canceling sound wave in the target zone, while naturally attenuating in other zones.

12. The method according to claim 1, characterized in that, The preloaded noise reduction parameters matching the current operating condition are used to quickly match changes in engine status. The specific process is as follows: establish a database corresponding to engine operating conditions and noise reduction parameters, and store matching parameters under different loads and coolant temperatures; monitor changes in engine load and coolant temperature in real time, and retrieve parameters close to the current operating condition as a basis. Based on the real-time analysis results of the current coupling coefficient matrix, the basic parameters are adjusted.

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