Noise recognition method and device of vehicle, vehicle and storage medium

Abstract

The application relates to the technical field of vehicles, in particular to a noise identification method and device of a vehicle, a vehicle and a storage medium, wherein the method comprises the following steps: acquiring a vibration acceleration signal and a vehicle interior noise signal when the vehicle drives on a preset impact road and performing time domain preprocessing; constructing a cross spectrum matrix based on the vibration acceleration signal after the time domain preprocessing and performing target principal component analysis to obtain a principal component signal of the cross spectrum matrix; then obtaining a transfer function matrix from each wheel center excitation point to an interior response point by using a hammering method test; calculating and identifying road impact loads at each wheel center of the vehicle based on the principal component signal and the transfer function matrix by using an inverse matrix method; and calculating contribution amount quantization results of each noise transmission path to the vehicle interior noise signal according to the road impact loads and the transfer function matrix. Therefore, the problems of noise identification result distortion and instability caused by the ill-conditioned transfer function matrix and the coupling of multiple reference point signals in the related art are solved.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a method, apparatus, vehicle, and storage medium for vehicle noise identification. Background Technology

[0002] As users' demands for vehicle ride comfort continue to increase, road noise caused by road surface unevenness has become a key evaluation indicator for the NVH (Noise, Vibration, and Harshness) performance of passenger vehicles. Road noise is mainly divided into two categories: airborne noise and structurally transmitted noise. Among them, structurally transmitted noise caused by road surface excitation has become an increasingly important focus for modern automobiles.

[0003] In related technologies, methods such as the inverse matrix method and the dynamic stiffness method are mainly used for identifying road surface excitation.

[0004] However, the relevant technologies face the following two major challenges in practical applications: (1) Due to the existence of multiple coupled transmission paths in the vehicle system, the transfer function matrix is ​​often ill-conditioned. Small measurement errors during the inversion process will be amplified, resulting in distorted or even unstable load identification results. (2) Road excitation acts on four wheels at the same time, and there is correlation between each excitation source. This multi-reference coupling problem further increases the complexity of load identification, especially when analyzing impact noise caused by discrete impacts (such as speed bumps and potholes), which urgently needs to be solved. Summary of the Invention

[0005] This application provides a method, apparatus, vehicle, and storage medium for vehicle noise identification, in order to solve the problems of distortion and instability in noise identification results caused by ill-conditioned transfer function matrix and multi-reference point signal coupling in related technologies.

[0006] The first aspect of this application provides a vehicle noise identification method, comprising the following steps: Acquire vibration acceleration signals and in-vehicle noise signals when the vehicle is traveling on a preset impact surface; The vibration acceleration signal and the vehicle interior noise signal are preprocessed in the time domain, and a cross-spectral matrix is ​​constructed based on the preprocessed vibration acceleration signal. Target principal component analysis is performed on the cross-spectrum matrix to obtain the principal component signals of the cross-spectrum matrix, and the transfer function matrix from each wheel center excitation point to the vehicle response point is obtained by hammer impact test. Based on the principal component signal and the transfer function matrix, the road impact load at each wheel center of the vehicle is calculated and identified using the inverse matrix method, and the contribution of each noise transmission path to the noise signal inside the vehicle is quantified according to the road impact load and the transfer function matrix.

[0007] Furthermore, in some embodiments, performing target principal component analysis on the cross-spectrum matrix to obtain the principal component signals of the cross-spectrum matrix includes: Singular value decomposition is performed on the cross-spectrum matrix to obtain the singular value sequence of the cross-spectrum matrix and the singular vectors corresponding to the singular value sequence; The contribution rate of the principal components at each order and the cumulative contribution rate of all orders are calculated based on the singular value sequence. Obtain the principal contribution order corresponding to the first time the cumulative contribution rate reaches a preset threshold, select the singular values ​​and singular vectors corresponding to the principal contribution order, and reconstruct the singular values ​​and singular vectors to obtain the principal component signals of the cross-spectrum matrix.

[0008] Furthermore, in some embodiments, the main contribution order is the first four principal components in the principal component signal, and each principal component corresponds to a global vibration mode of the vehicle suspension-body system.

[0009] Furthermore, in some embodiments, obtaining the transfer function matrix from each wheel center excitation point to the in-vehicle response point using the hammer impact test includes: A preset force sensing device is used to sequentially apply transient excitation at each wheel center of the vehicle in a preset direction; Under each excitation, the force signal from the preset force sensing device and the sound pressure signal and / or vibration acceleration signal at each response point inside the vehicle are collected; Based on the force signal, the sound pressure signal, and / or the vibration acceleration signal, calculate the frequency response function of each excitation point-direction combination to each response point, and arrange all frequency response functions according to the excitation point and the response point to form the transfer function matrix from each wheel center excitation point to the vehicle interior response point.

[0010] Furthermore, in some embodiments, the quantification result of calculating the contribution of each noise transmission path to the in-vehicle noise signal based on the road impact load and the transfer function matrix includes: Using the transfer function in the transfer function matrix corresponding to the road impact load at each wheel center and the noise signal inside the vehicle, the partial sound pressure contribution generated at the target point by each noise transmission path is calculated respectively. Based on the partial sound pressure contribution and the in-vehicle noise signal obtained under the same operating conditions, the percentage contribution of each noise transmission path is calculated to determine the quantification result of the contribution of the in-vehicle noise signal.

[0011] The vehicle noise identification method according to embodiments of this application acquires the vibration acceleration signal and in-vehicle noise signal of the vehicle when driving on a preset impact road surface, and performs time-domain preprocessing. Based on the time-domain preprocessed vibration acceleration signal, a cross-spectral matrix is ​​constructed and target principal component analysis is performed to obtain the principal component signals of the cross-spectral matrix. Then, the transfer function matrix from each wheel center excitation point to the in-vehicle response point is obtained using the hammer impact test. Based on the principal component signals and the transfer function matrix, the road impact load at each wheel center of the vehicle is calculated and identified using the inverse matrix method. The contribution of each noise transmission path to the in-vehicle noise signal is quantified based on the road impact load and the transfer function matrix. This solves the problems of distortion and instability in noise identification results caused by ill-conditioned transfer function matrices and multi-reference point signal coupling in related technologies.

[0012] A second aspect of this application provides a vehicle noise identification device, comprising: The first acquisition module is used to acquire the vibration acceleration signal and the noise signal inside the vehicle when the vehicle is driving on a preset impact road surface. The data processing module is used to perform time-domain preprocessing on the vibration acceleration signal and the vehicle interior noise signal, and to construct a cross-spectral matrix based on the time-domain preprocessed vibration acceleration signal. The second acquisition module is used to perform target principal component analysis on the cross-spectrum matrix to obtain the principal component signals of the cross-spectrum matrix, and to obtain the transfer function matrix from each wheel center excitation point to the vehicle response point using the hammer impact method. The calculation module is used to calculate and identify the road impact load at each wheel center of the vehicle based on the principal component signal and the transfer function matrix using the inverse matrix method, and to calculate the quantification result of the contribution of each noise transmission path to the noise signal inside the vehicle according to the road impact load and the transfer function matrix.

[0013] Furthermore, in some embodiments, the second acquisition module includes: The decomposition unit is used to perform singular value decomposition on the cross-spectrum matrix to obtain the singular value sequence of the cross-spectrum matrix and the singular vectors corresponding to the singular value sequence. The first calculation unit is used to calculate the contribution rate of the principal components of each order and the cumulative contribution rate of all orders based on the singular value sequence. The acquisition unit is used to acquire the main contribution order corresponding to the first time the cumulative contribution rate reaches a preset threshold, select the singular values ​​and singular vectors corresponding to the main contribution order, and reconstruct the singular values ​​and singular vectors to obtain the principal component signals of the cross-spectrum matrix.

[0014] Furthermore, in some embodiments, the main contribution order is the first four principal components in the principal component signal, and each principal component corresponds to a global vibration mode of the vehicle suspension-body system.

[0015] Furthermore, in some embodiments, the second acquisition module includes: An application unit is used to sequentially apply transient excitation in a preset direction at each wheel center of the vehicle using a preset force sensing device. The acquisition unit is used to acquire the force signal of the preset force sensing device and the sound pressure signal and / or vibration acceleration signal of each response point in the vehicle under each excitation. The second calculation unit is used to calculate the frequency response function of each excitation point-direction combination to each response point based on the force signal, the sound pressure signal and / or the vibration acceleration signal, and to arrange all frequency response functions according to the excitation point and the response point to form the transfer function matrix from each wheel center excitation point to the vehicle interior response point.

[0016] Furthermore, in some embodiments, the computing module includes: The third calculation unit is used to calculate the partial sound pressure contribution generated at the target point by each noise transmission path by using the transfer function corresponding to the road impact load at each wheel center and the noise signal inside the vehicle in the transfer function matrix. The determining unit is used to calculate the percentage contribution of each noise transmission path based on the partial sound pressure contribution and the in-vehicle noise signal obtained under the same operating conditions, so as to determine the quantification result of the contribution of the in-vehicle noise signal.

[0017] The vehicle noise recognition device according to an embodiment of this application acquires the vibration acceleration signal and the vehicle interior noise signal when the vehicle is traveling on a preset impact road surface and performs time-domain preprocessing. Based on the time-domain preprocessed vibration acceleration signal, a cross-spectral matrix is ​​constructed and target principal component analysis is performed to obtain the principal component signals of the cross-spectral matrix. Then, the transfer function matrix from each wheel center excitation point to the vehicle interior response point is obtained using the hammer impact test. Based on the principal component signals and the transfer function matrix, the road impact load at each wheel center of the vehicle is calculated and identified using the inverse matrix method. The contribution of each noise transmission path to the vehicle interior noise signal is quantified based on the road impact load and the transfer function matrix. This solves the problems of distortion and instability in noise recognition results caused by ill-conditioned transfer function matrices and multi-reference point signal coupling in related technologies.

[0018] A third aspect of this application provides a vehicle, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle noise identification method as described in the above embodiments.

[0019] A fourth aspect of this application provides a computer-readable storage medium storing computer instructions for causing the computer to perform the vehicle noise identification method as described in the above embodiments.

[0020] A fifth aspect of this application provides a computer program product, including a computer program that is executed to implement the vehicle noise identification method described in the above embodiments.

[0021] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0022] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a vehicle noise identification method according to an embodiment of this application; Figure 2 This is a flowchart illustrating the principal component identification of road impact noise load in a passenger vehicle according to an embodiment of this application. Figure 3 This is an example diagram of a vehicle noise recognition device according to an embodiment of this application; Figure 4 This is a structural schematic diagram of a vehicle according to an embodiment of this application. Detailed Implementation

[0023] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0024] The following description, with reference to the accompanying drawings, describes a vehicle noise identification method, apparatus, vehicle, and storage medium according to embodiments of this application. Addressing the problems of distortion and instability in noise identification results caused by ill-conditioned transfer function matrices and multi-reference point signal coupling in related technologies mentioned in the background, this application provides a vehicle noise identification method. In this method, vibration acceleration signals and in-vehicle noise signals are acquired when the vehicle is traveling on a preset impact road surface and preprocessed in the time domain. A cross-spectral matrix is ​​constructed based on the preprocessed vibration acceleration signals, and principal component analysis is performed to obtain the principal component signals of the cross-spectral matrix. Then, the transfer function matrix from each wheel center excitation point to the in-vehicle response point is obtained using a hammer impact test. Based on the principal component signals and the transfer function matrix, the road impact load at each wheel center of the vehicle is calculated using the inverse matrix method. The contribution of each noise transmission path to the in-vehicle noise signal is quantified based on the road impact load and the transfer function matrix. This solves the problems of distortion and instability in noise identification results caused by ill-conditioned transfer function matrices and multi-reference point signal coupling in related technologies.

[0025] Specifically, Figure 1 This is a flowchart illustrating a vehicle noise identification method provided in an embodiment of this application.

[0026] like Figure 1 As shown, the noise identification method for this vehicle includes the following steps: In step S101, the vibration acceleration signal and the noise signal inside the vehicle are acquired when the vehicle is traveling on a preset impact road surface.

[0027] The preset impact road surface can be any condition in the field of NVH testing where a vehicle drives on a road surface with discrete, limited amplitude but high rate of change (excitation frequency) local bumps or depressions, such as speed bumps, small potholes on paved roads, or standard NVH test bump road surfaces, etc., without being specifically limited here.

[0028] Specifically, due to the excitation force of the road surface, the vehicle body structure vibrates through different transmission paths, thereby radiating a large amount of noise into the vehicle. In order to effectively control and analyze road noise, transmission path analysis is usually required. For example, transmission path test analysis or CAE (Computer-Aided Engineering) simulation analysis can be used to determine the contribution of each transmission path to the target point (i.e., in-vehicle noise), thereby providing a strong guidance scheme for the design and control of low-noise vehicle products.

[0029] Furthermore, since vehicle road noise is generated by the interaction and joint action of the four wheels as excitation sources, and the excitation sources are partially correlated, the degree of correlation depends on the road surface characteristics. This multi-reference coupling problem increases the complexity of load identification, especially the load identification of in-vehicle impact noise caused by road impact load.

[0030] Therefore, to solve the multi-reference coupling problem, principal component analysis (PCA) has been introduced into load identification. PCA mainly decouples the coupled signals of multiple reference points into mutually orthogonal independent components through singular value decomposition, thereby transforming the complex multi-reference problem into a simple single-reference problem. At the same time, combined with the inverse matrix method, the road noise impact load excitation force at the wheel center can be effectively identified, thus solving the ill-conditioned problem of matrix inversion in traditional methods, significantly improving the accuracy and efficiency of load identification, and providing reliable data support and theoretical basis for vehicle road noise optimization.

[0031] Specifically, during the test, the vehicle was driven at a preset speed (e.g., 40–80 km / h) over a real small-impact road surface to induce structural vibrations and in-vehicle impact noise in the 50Hz to 300Hz frequency range. This simulates typical small-impact scenarios encountered by users in daily driving, such as speed bumps, road seams, or damaged asphalt surfaces. High-sensitivity triaxial piezoelectric accelerometers were installed at the steering knuckles of all four wheels, forming a 12-channel vibration measurement system to measure the vehicle's vibration acceleration in three orthogonal directions. Simultaneously, microphones were placed at the driver's and passenger's outer ears to collect in-vehicle noise signals. For example, free-field microphones were standardly placed at the driver's right ear and the front passenger's left ear to synchronously record the actual sound pressure level of structurally propagated noise at the perceived location of the ear. All sensor signals were synchronously acquired through a high-precision data acquisition system (sampling frequency ≥ 2048Hz, anti-aliasing filtering enabled) to ensure strict alignment of vibration and noise signals on the time axis, providing a reliable data foundation for subsequent frequency domain analysis.

[0032] In step S102, the vibration acceleration signal and the vehicle interior noise signal are preprocessed in the time domain, and a cross-spectral matrix is ​​constructed based on the preprocessed vibration acceleration signal.

[0033] Specifically, such as Figure 2As shown, after completing the real-vehicle road test, the synchronously acquired vibration acceleration signal and vehicle interior noise signal are first subjected to time-domain preprocessing to eliminate non-target interference and improve the signal-to-noise ratio. This can include mean-reduction processing, anti-aliasing low-pass filtering, amplitude calibration and unit unification, effective data segment truncation, and time alignment correction for the vibration acceleration signal and vehicle interior noise signal. Among these, mean-reduction processing is mainly used to subtract the arithmetic mean from the time-domain data of each signal channel to eliminate the DC component in the signal, ensuring that subsequent spectrum analysis is only performed on the AC dynamic component; anti-aliasing low-pass filtering is mainly used to apply anti-aliasing filtering with a cutoff frequency of half the sampling rate (e.g., 1024Hz when the sampling rate is 2048Hz) to the signal according to the sampling theorem to prevent spectrum aliasing; amplitude calibration and unit unification are mainly used to convert the original voltage signal into standard physical units based on the sensor sensitivity; effective data segment truncation is mainly used to retain only the stable driving segment when the vehicle is completely within the preset impact road surface area (usually through GPS (Global Positioning System)). System (Global Positioning System) or wheel speed signal trigger), eliminating starting, braking or transition segments to ensure consistency of excitation conditions; time alignment correction is mainly used to perform sub-sampling level alignment using cross-correlation method if there is a micro-delay in multi-channel acquisition, to ensure accurate phase relationship of four-wheel vibration signals.

[0034] Secondly, after performing time-domain preprocessing on the vibration acceleration signal and the vehicle interior noise signal, a frequency-domain data structure for principal component analysis is constructed based solely on the 12-channel vibration acceleration signal. This structure is the cross-spectral matrix, which is the core input for principal component analysis. It mainly includes: calculating the cross-power spectral density. For a given frequency, the cross-power spectral density between each pair of vibration acceleration signal channels is calculated. Then, all channel pairs are arranged according to their positions to form an N×N complex matrix, which is the cross-spectral matrix. Finally, within the frequency range of interest (e.g., 50Hz to 300Hz), the above steps are repeated for each discrete frequency point to obtain a three-dimensional cross-spectral matrix data volume, which serves as the input for subsequent principal component analysis.

[0035] Thus, through the standardized preprocessing procedure described above, the original time-domain impulse signal is transformed into a frequency-domain cross-spectral matrix suitable for analyzing its internal correlation structure.

[0036] In step S103, target principal component analysis is performed on the cross-spectrum matrix to obtain the principal component signals of the cross-spectrum matrix, and the transfer function matrix from each wheel center excitation point to the vehicle response point is obtained by hammer impact test.

[0037] Further, in some embodiments, target principal component analysis is performed on the cross-spectrum matrix to obtain the principal component signals of the cross-spectrum matrix, including: performing singular value decomposition on the cross-spectrum matrix to obtain the singular value sequence of the cross-spectrum matrix and the singular vectors corresponding to the singular value sequence; calculating the contribution rate of the principal components of each order and the cumulative contribution rate of all orders based on the singular value sequence; obtaining the main contribution order corresponding to the first time the cumulative contribution rate reaches a preset threshold, selecting the singular values ​​and singular vectors corresponding to the main contribution order, and reconstructing the singular values ​​and singular vectors to obtain the principal component signals of the cross-spectrum matrix.

[0038] The preset threshold can be set by those skilled in the art according to the testing requirements, or it can be obtained through a limited number of computer simulations, and no specific limitation is made here.

[0039] Specifically, after completing the construction of the cross-spectral matrix, in order to effectively decouple the strong correlation between the multi-channel vibration responses caused by four-wheel excitation and extract the dynamic features that play a dominant role in road impact noise, this application performs target principal component analysis on the cross-spectral matrix.

[0040] Specifically, such as Figure 2 As shown, firstly, singular value decomposition is performed on the cross-spectral matrix at each target frequency point (typically covering 50–300 Hz). Then, the contribution rate of the i-th principal component at frequency f is calculated based on the singular value sequence, and the cumulative contribution rate of the first n principal components is further calculated. This cumulative contribution rate characterizes the proportion of total energy contained in the first n principal components, reflecting their information coverage of the original multi-channel vibration signal. Secondly, a preset cumulative contribution rate threshold is set (e.g., 98%, 98.5%, or 99%, preferably 98.6%), aiming to retain most of the effective dynamic information while eliminating redundant components introduced by measurement noise or weak coupling paths. The process iterates through orders n=1, 2, …, 12, obtaining the highest cumulative contribution rate when the preset threshold is first reached. The order n0 is defined as the main contributing order, which is the first four principal components in the principal component signal. Each principal component corresponds to a global vibration mode of the vehicle suspension-body system. Then, the first n0 largest singular values ​​and their corresponding singular vectors corresponding to the first n0 principal components are selected to form a dimension-reduced orthogonal basis set. The original frequency domain response is reconstructed using this basis set to obtain the principal component signal matrix, which is the high-energy, low-noise, and approximately decoupled equivalent vibration response signal extracted by principal component analysis. Although its dimension is still 12 channels, its intrinsic degree of freedom has been reduced to n0 (usually n0≤4), and each principal component is independent and orthogonal to each other, which significantly weakens the cross-coupling effect between the original four-wheel excitation.

[0041] It should be noted that this principal component signal will be used as the input response data for subsequent load inversion calculation. Since it is dominated by singular values, the condition number of the corresponding transfer function matrix is ​​significantly improved, thereby effectively alleviating the load identification distortion problem caused by ill-conditionedness in the high-frequency band of the traditional inverse matrix method, and laying a mathematical foundation for high-precision wheel center dynamic load identification.

[0042] Furthermore, in some embodiments, the transfer function matrix from each wheel center excitation point to the vehicle interior response point is obtained using the hammer impact test, including: applying transient excitation sequentially along a preset direction at each wheel center of the vehicle using a preset force sensing device; collecting the force signal from the preset force sensing device and the sound pressure signal and / or vibration acceleration signal at each response point inside the vehicle under each excitation; calculating the frequency response function from each excitation point-direction combination to each response point based on the force signal, sound pressure signal, and / or vibration acceleration signal, and arranging all frequency response functions according to the excitation point and the response point to form the transfer function matrix from each wheel center excitation point to the vehicle interior response point.

[0043] The preset force sensing device can be any force sensor selected by those skilled in the art according to the testing requirements, and no specific limitation is made here.

[0044] Specifically, in order to establish the physical mapping relationship between road excitation at the wheel center and in-vehicle acoustic vibration response, this application uses the hammer impact method to test and obtain the transfer function matrix from each wheel center excitation point to the key response point in the vehicle under controlled laboratory conditions.

[0045] Specifically, such as Figure 2 As shown, firstly, the vehicle is placed in a semi-anechoic chamber or NVH rotating test bench, and the active end connectors of the four wheel suspension systems (such as control arm ball joints, connecting rod bushings, etc.) are removed, so that the vehicle body is in a free-free boundary state. The test is carried out using the single-point excitation and multi-point response method of the force hammer. This operation aims to isolate the constraint influence of the suspension system on the vehicle body mode, and ensure that the measured transfer function only reflects the dynamic characteristics of the vehicle body structure itself, avoiding measurement distortion caused by nonlinear contact under the whole vehicle state.

[0046] Secondly, an excitation point is set at the wheel center position corresponding to each wheel. Using a high-precision impact hammer, a short-time broadband transient excitation (typical pulse width ≤2ms, spectrum coverage 10–500Hz) is applied to each wheel center in sequence along three translational directions (X: longitudinal, Y: lateral, Z: vertical) and three rotational directions (Rx, Ry, Rz, which are achieved by applying an eccentric torque). Each excitation ensures that the force signal has a good signal-to-noise ratio and frequency flatness, and the force pulse energy is sufficient to excite the structural response within the target frequency band (50–300Hz).

[0047] During each hammer impact, two types of signals are simultaneously acquired: force signals and response signals. The force signals are recorded in real time by the built-in sensor of the hammer and used as an input reference. The response signals include in-vehicle acoustic response and structural vibration response. The in-vehicle acoustic response is the sound pressure signal measured by microphones located at the driver's right ear and the left ear of the front passenger. The structural vibration response is the acceleration signal acquired from key points of the vehicle body (such as the A-pillar, floor, and seat rails). All signals are recorded synchronously through a multi-channel data acquisition system with a sampling frequency of not less than 2048Hz and anti-aliasing filtering is applied.

[0048] Next, a fast Fourier transform is performed on each excitation-response signal pair to obtain the frequency domain force signal and frequency domain response signal. Based on the definition of the frequency response function, the single-input single-output is calculated.

[0049] Finally, all the calculated frequency response functions are systematically arranged according to the excitation point-degree of freedom and response point to form a complete transfer function matrix.

[0050] It should be noted that although the hammer impact test is conducted in a free-body state, in engineering practice it is generally believed that in the mid-to-high frequency range of 50–300Hz, the local stiffness of the vehicle body dominates the response characteristics, and the influence of boundary condition changes caused by suspension connection is relatively small. Therefore, this transfer function matrix can be reasonably approximated for load inversion under the road conditions of the whole vehicle, and its effectiveness has been verified in a large number of NVH engineering cases.

[0051] In step S104, based on the principal component signal and the transfer function matrix, the road impact load at each wheel center of the vehicle is calculated and identified using the inverse matrix method, and the contribution of each noise transmission path to the noise signal inside the vehicle is quantified according to the road impact load and the transfer function matrix.

[0052] Furthermore, in some embodiments, the contribution of each noise transmission path to the in-vehicle noise signal is quantified based on the road impact load and the transfer function matrix. This includes: using the transfer function corresponding to the road impact load and the in-vehicle noise signal at each wheel center in the transfer function matrix, calculating the partial sound pressure contribution generated by each noise transmission path at the target point; and calculating the percentage contribution of each noise transmission path based on the partial sound pressure contribution and the in-vehicle noise signal obtained under the same working conditions to determine the quantification result of the contribution of the in-vehicle noise signal.

[0053] Specifically, after obtaining the high-energy decoupled vibration response signal (i.e., principal component signal) extracted by principal component analysis and the transfer function matrix from the wheel center to the response point inside the vehicle measured by the hammer impact method, this application further realizes high-precision identification of the road impact load at the wheel center based on the inverse matrix method, and on this basis completes the quantification of the contribution of each noise transmission path to the acoustic response inside the vehicle. This process specifically includes two stages: identification of the road impact load at the wheel center and quantification of the contribution of the noise transmission path.

[0054] Specifically, such as Figure 2 As shown, in the process of identifying the road impact load at the wheel center, firstly, the principal component signal obtained by the above reconstruction is used as the system output, and the load inversion model is jointly constructed with the transfer function matrix measured by the hammer impact method. Since the load inversion model has ill-conditioned properties in the high-frequency band, direct inversion will lead to unstable results. Therefore, this application uses the inverse matrix method to solve it. The final output wheel center dynamic load vector is the real road impact load at each wheel center when the vehicle is driving on the preset impact road surface.

[0055] Furthermore, in the process of determining the contribution of noise transmission paths, after obtaining the wheel center load, the acoustic transfer function matrix is ​​used to analyze the transmission paths, merging the contributions of the six degrees of freedom of the same wheel to obtain the total sound pressure contribution of a single wheel. Then, the suspension paths are physically categorized to form several sets of noise transmission paths. The synthesized sound pressure of all paths is then superimposed to obtain the reconstructed total sound pressure, which is compared with the measured in-vehicle noise signal under the same operating conditions. If the amplitude error between the two is less than 3dB in the 50–300Hz frequency band, the load identification result is verified as valid. Finally, the percentage contribution of each noise transmission path is calculated; this percentage is the quantified result of the contribution of each noise transmission path to the in-vehicle noise signal.

[0056] The transfer path analysis model includes a global transfer function and a local transfer function, which were obtained through hammer impact testing.

[0057] Therefore, based on the above analysis, those skilled in the art can clearly identify the dominant noise path (such as "the left front suspension contributes 42% of the vertical force"), and thus optimize the bushing stiffness, add vibration damping pads, or adjust the subframe mounting points in a targeted manner to achieve precise control of road noise.

[0058] In summary, based on the above analysis, this application can achieve the following beneficial effects: (1) By decoupling the multi-reference point coupled signals through principal component analysis, the ill-conditioned problem of matrix inversion in traditional methods is effectively solved, and the accuracy of load identification is significantly improved; (2) The main contribution order is selected for analysis, while the secondary contribution order is ignored, which simplifies the calculation process and improves the analysis efficiency; (3) By combining the transmission path analysis model, the contribution of each path to the in-vehicle noise can be quantified, providing a clear direction for optimizing the in-vehicle noise caused by road impact.

[0059] The vehicle noise identification method according to embodiments of this application acquires the vibration acceleration signal and in-vehicle noise signal of the vehicle when driving on a preset impact road surface, and performs time-domain preprocessing. Based on the time-domain preprocessed vibration acceleration signal, a cross-spectral matrix is ​​constructed and target principal component analysis is performed to obtain the principal component signals of the cross-spectral matrix. Then, the transfer function matrix from each wheel center excitation point to the in-vehicle response point is obtained using the hammer impact test. Based on the principal component signals and the transfer function matrix, the road impact load at each wheel center of the vehicle is calculated and identified using the inverse matrix method. The contribution of each noise transmission path to the in-vehicle noise signal is quantified based on the road impact load and the transfer function matrix. This solves the problems of distortion and instability in noise identification results caused by ill-conditioned transfer function matrices and multi-reference point signal coupling in related technologies.

[0060] Next, referring to the accompanying drawings, a vehicle noise recognition device according to an embodiment of this application is described.

[0061] Figure 3 This is a block diagram of a vehicle noise recognition device according to an embodiment of this application.

[0062] like Figure 3 As shown, the noise recognition device 10 for the vehicle includes: a first acquisition module 100, a data processing module 200, a second acquisition module 300, and a calculation module 400.

[0063] The first acquisition module 100 is used to acquire the vibration acceleration signal and the noise signal inside the vehicle when the vehicle is driving on a preset impact road surface. The data processing module 200 is used to perform time-domain preprocessing on the vibration acceleration signal and the vehicle interior noise signal, and to construct a cross-spectral matrix based on the time-domain preprocessed vibration acceleration signal. The second acquisition module 300 is used to perform target principal component analysis on the cross-spectrum matrix to obtain the principal component signals of the cross-spectrum matrix, and to obtain the transfer function matrix from each wheel center excitation point to the vehicle response point using the hammer impact method. The calculation module 400 is used to calculate and identify the road impact load at each wheel center of the vehicle based on the principal component signal and the transfer function matrix using the inverse matrix method, and to quantify the contribution of each noise transmission path to the noise signal inside the vehicle based on the road impact load and the transfer function matrix.

[0064] Furthermore, in some embodiments, the second acquisition module 300 includes: The decomposition unit is used to perform singular value decomposition on the cross-spectrum matrix to obtain the singular value sequence of the cross-spectrum matrix and the singular vectors corresponding to the singular value sequence. The first calculation unit is used to calculate the contribution rate of the principal components of each order and the cumulative contribution rate of all orders based on the singular value sequence. The acquisition unit is used to acquire the main contribution order corresponding to the first time the cumulative contribution rate reaches a preset threshold, select the singular values ​​and singular vectors corresponding to the main contribution order, and reconstruct the singular values ​​and singular vectors to obtain the principal component signals of the cross-spectrum matrix.

[0065] Furthermore, in some embodiments, the main contributing order is the first four principal components in the principal component signal, and each principal component corresponds to a global vibration mode of the vehicle suspension-body system.

[0066] Furthermore, in some embodiments, the second acquisition module 300 includes: An application unit is used to apply transient excitation sequentially along a preset direction at each wheel center of the vehicle using a preset force sensing device. The acquisition unit is used to acquire the force signal of the preset force sensing device and the sound pressure signal and / or vibration acceleration signal of each response point in the vehicle under each excitation. The second calculation unit is used to calculate the frequency response function of each excitation point-direction combination to each response point based on force signal, sound pressure signal and / or vibration acceleration signal, and arrange all frequency response functions according to excitation point and response point to form a transfer function matrix from each wheel center excitation point to the vehicle interior response point.

[0067] Furthermore, in some embodiments, the computing module 400 includes: The third calculation unit is used to calculate the partial sound pressure contribution generated at the target point by each noise transmission path by using the transfer function corresponding to the road impact load and the noise signal inside the vehicle at each wheel center in the transfer function matrix. The determination unit is used to calculate the percentage contribution of each noise transmission path based on the partial sound pressure contribution and the in-vehicle noise signal obtained under the same operating conditions, so as to determine the quantification result of the contribution of the in-vehicle noise signal.

[0068] The vehicle noise recognition device according to an embodiment of this application acquires the vibration acceleration signal and the vehicle interior noise signal when the vehicle is traveling on a preset impact road surface and performs time-domain preprocessing. Based on the time-domain preprocessed vibration acceleration signal, a cross-spectral matrix is ​​constructed and target principal component analysis is performed to obtain the principal component signals of the cross-spectral matrix. Then, the transfer function matrix from each wheel center excitation point to the vehicle interior response point is obtained using the hammer impact test. Based on the principal component signals and the transfer function matrix, the road impact load at each wheel center of the vehicle is calculated and identified using the inverse matrix method. The contribution of each noise transmission path to the vehicle interior noise signal is quantified based on the road impact load and the transfer function matrix. This solves the problems of distortion and instability in noise recognition results caused by ill-conditioned transfer function matrices and multi-reference point signal coupling in related technologies.

[0069] Figure 4 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include: The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0070] When the processor 402 executes the program, it implements the vehicle noise recognition method provided in the above embodiments.

[0071] Furthermore, the vehicle also includes: Communication interface 403 is used for communication between memory 401 and processor 402.

[0072] The memory 401 is used to store computer programs that can run on the processor 402.

[0073] Memory 401 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0074] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized into address buses, data buses, control buses, etc. For ease of representation, Figure 4The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0075] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0076] Processor 402 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0077] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the vehicle noise recognition method described above.

[0078] This embodiment also provides a computer program product, including a computer program that is executed to implement the vehicle noise recognition method of the above embodiment.

[0079] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0080] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0081] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0082] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0083] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0084] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium. When executed, the program includes one or a combination of the steps of the method embodiments.

[0085] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0086] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for vehicle noise identification, characterized in that, Includes the following steps: Acquire vibration acceleration signals and in-vehicle noise signals when the vehicle is traveling on a preset impact surface; The vibration acceleration signal and the vehicle interior noise signal are preprocessed in the time domain, and a cross-spectral matrix is ​​constructed based on the preprocessed vibration acceleration signal. Target principal component analysis is performed on the cross-spectrum matrix to obtain the principal component signals of the cross-spectrum matrix, and the transfer function matrix from each wheel center excitation point to the vehicle response point is obtained by hammer impact test. Based on the principal component signal and the transfer function matrix, the road impact load at each wheel center of the vehicle is calculated and identified using the inverse matrix method, and the contribution of each noise transmission path to the noise signal inside the vehicle is quantified according to the road impact load and the transfer function matrix.

2. The method according to claim 1, characterized in that, The step of performing target principal component analysis on the cross-spectrum matrix to obtain the principal component signals of the cross-spectrum matrix includes: Singular value decomposition is performed on the cross-spectrum matrix to obtain the singular value sequence of the cross-spectrum matrix and the singular vectors corresponding to the singular value sequence; The contribution rate of the principal components of each order and the cumulative contribution rate of all orders are calculated based on the singular value sequence. Obtain the principal contribution order corresponding to the first time the cumulative contribution rate reaches a preset threshold, select the singular values ​​and singular vectors corresponding to the principal contribution order, and reconstruct the singular values ​​and singular vectors to obtain the principal component signals of the cross-spectrum matrix.

3. The method according to claim 2, characterized in that, The main contribution order is the first four principal components in the principal component signal, and each principal component corresponds to a global vibration mode of the vehicle suspension-body system.

4. The method according to claim 1, characterized in that, The method of obtaining the transfer function matrix from each wheel center excitation point to the in-vehicle response point using the hammer impact test includes: A preset force sensing device is used to sequentially apply transient excitation at each wheel center of the vehicle in a preset direction; Under each excitation, the force signal from the preset force sensing device and the sound pressure signal and / or vibration acceleration signal at each response point inside the vehicle are collected; Based on the force signal, the sound pressure signal, and / or the vibration acceleration signal, calculate the frequency response function of each excitation point-direction combination to each response point, and arrange all frequency response functions according to the excitation point and the response point to form the transfer function matrix from each wheel center excitation point to the vehicle interior response point.

5. The method according to claim 1, characterized in that, The quantification result of calculating the contribution of each noise transmission path to the noise signal inside the vehicle based on the road impact load and the transfer function matrix includes: Using the transfer function in the transfer function matrix corresponding to the road impact load at each wheel center and the noise signal inside the vehicle, the partial sound pressure contribution generated at the target point by each noise transmission path is calculated respectively. Based on the partial sound pressure contribution and the in-vehicle noise signal obtained under the same operating conditions, the percentage contribution of each noise transmission path is calculated to determine the quantification result of the contribution of the in-vehicle noise signal.

6. A vehicle noise identification device, characterized in that, include: The first acquisition module is used to acquire the vibration acceleration signal and the noise signal inside the vehicle when the vehicle is driving on a preset impact road surface. The data processing module is used to perform time-domain preprocessing on the vibration acceleration signal and the vehicle interior noise signal, and to construct a cross-spectral matrix based on the time-domain preprocessed vibration acceleration signal. The second acquisition module is used to perform target principal component analysis on the cross-spectrum matrix to obtain the principal component signals of the cross-spectrum matrix, and to obtain the transfer function matrix from each wheel center excitation point to the vehicle response point using the hammer impact method. The calculation module is used to calculate and identify the road impact load at each wheel center of the vehicle based on the principal component signal and the transfer function matrix using the inverse matrix method, and to calculate the quantification result of the contribution of each noise transmission path to the noise signal inside the vehicle according to the road impact load and the transfer function matrix.

7. The apparatus according to claim 6, characterized in that, The second acquisition module includes: The decomposition unit is used to perform singular value decomposition on the cross-spectrum matrix to obtain the singular value sequence of the cross-spectrum matrix and the singular vectors corresponding to the singular value sequence. The first calculation unit is used to calculate the contribution rate of the principal components of each order and the cumulative contribution rate of all orders based on the singular value sequence. The acquisition unit is used to acquire the main contribution order corresponding to the first time the cumulative contribution rate reaches a preset threshold, select the singular values ​​and singular vectors corresponding to the main contribution order, and reconstruct the singular values ​​and singular vectors to obtain the principal component signals of the cross-spectrum matrix.

8. The apparatus according to claim 7, characterized in that, The main contribution order is the first four principal components in the principal component signal, and each principal component corresponds to a global vibration mode of the vehicle suspension-body system.

9. A vehicle, characterized in that, include: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, the processor executing the program to implement the vehicle noise identification method as described in any one of claims 1-5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the vehicle noise identification method as described in any one of claims 1-5.

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