Virtual reference signal multi-working-condition transformation matrix synthesis method and device
Through the multi-case transformation matrix synthesis method of virtual reference signal, the problems of large amount of calculation and difficult real-time update in the active control of in-vehicle road noise are solved, and stable noise reduction control is realized under multiple operating conditions, which improves the applicability and reliability of the system.
Patent Information
- Application Number
- CN202411789205.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-06
- Publication Date
- 2025-05-02
AI Technical Summary
In the active control of in-vehicle road noise, there are problems in the existing technology that large amount of calculation, difficult real-time updates, and lack of research on the characteristic vector differences of vibration signal data under multiple operating conditions.
A method of synthesis of virtual reference signal multi-case transformation matrix is proposed. By collecting noise and vibration data under different road driving conditions, screening sensor distribution points using multiple coherence method, performing singular value decomposition, extracting feature vectors, and constructing a multi-case transformation matrix through Pearson correlation analysis.
The reference signal quality is optimized, the number of signals is reduced, the problems of waste of computing resources and slowing down the system convergence speed are avoided, and the stable noise reduction control is achieved under multi-vehicle speed and multi-pavement conditions are improved, and the applicability and reliability of the noise reduction system are improved.
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Abstract
Description
Technical Field
[0001] The present application relates to the technical field of active noise reduction of road noise vehicles, and in particular to a method and device for synthesizing a virtual reference signal multi-operating condition transformation matrix. Background Art
[0002] Noise control inside the car has always been an area of great concern to automakers and consumers. Therefore, how to reduce the noise level inside the vehicle has become a key issue in NVH (noise, vibration and harshness) engineering development. Noise reduction methods can be divided into passive noise reduction and active noise reduction. Passive noise reduction methods mostly use acoustic packages to suppress mid- and high-frequency noise, but the suppression effect on low-frequency noise is usually average; active noise control technology (Active Noise Control, ANC) is significant in suppressing low-frequency noise.
[0003] Due to its time-varying random characteristics and broadband color, the imbalance between the system's computing power and noise reduction effect has not been effectively solved. Therefore, the application of active road noise control (RNC) in practical engineering is a key issue of concern in the industry. Since the sensors all use three-dimensional acceleration sensors, the selected reference signals are also correlated to a certain extent. Redundant reference signals may lead to problems such as slow convergence speed and tight computing resources. Therefore, many domestic and foreign scholars have proposed to optimize the selected reference signals by constructing virtual reference signals to address this problem. The purpose is to construct a small number of high-quality virtual reference signals based on the original reference signals. Related studies point out that the main features corresponding to the larger singular values can be taken as the transformation matrix for signal reconstruction after extracting the signal features and performing singular value truncation. Since the features are independent of each other and the condition number is reduced after truncation, it can be used as an effective method. Existing studies have verified that whether the transformation matrix is extracted in the time domain or the frequency domain, the constructed virtual reference signal can reproduce most of the information of the original signal.
[0004] However, although the method of updating the transformation matrix in the time domain solves the causal constraint problem, it still has the problem of large amount of calculation in practical application. In addition, existing studies have verified a single working condition in a simulation environment, lacking verification in a real vehicle environment, and lacking research on the differences in the eigenvectors of vibration signal data under multiple working conditions. Therefore, the method of generating the transformation matrix in the frequency domain has the problem of causal relationship failure, and the method of generating the time domain transformation matrix in real time also has the problem of too high amount of calculation. Both methods are not suitable for practical engineering. Summary of the invention
[0005] The present application aims to solve one of the technical problems in the related art at least to some extent.
[0006] To this end, the first objective of the present application is to propose a method for synthesizing a virtual reference signal multi-condition transformation matrix.
[0007] The second objective of the present application is to provide a virtual reference signal multi-operating condition transformation matrix synthesis device.
[0008] The third objective of the present application is to provide an electronic device.
[0009] A fourth objective of the present application is to provide a computer-readable storage medium.
[0010] A fifth object of the present application is to provide a computer program product.
[0011] To achieve the above-mentioned purpose, the first embodiment of the present application proposes a virtual reference signal multi-condition transformation matrix synthesis method, including:
[0012] Collect noise data at target positions and vibration acceleration data at chassis of the test vehicle under different road driving conditions;
[0013] According to the collected noise data and vibration acceleration data, the correlation between the candidate sensor locations and the expected noise signal is analyzed by using the multi-coherence method to screen out the location of the original reference signal.
[0014] Perform singular value decomposition on the original reference signals at the selected points to extract the characteristic vectors and singular values under each road driving condition;
[0015] After sorting the extracted eigenvectors according to the corresponding singular value sizes, the eigenvectors are recombined at the same positions to form a new eigenvector group;
[0016] Calculate the Pearson correlation coefficient between all eigenvectors in each eigenvector group, and calculate the average correlation coefficient of a single eigenvector in its group;
[0017] The eigenvector with the highest average correlation coefficient is selected from each eigenvector group to finally form a new virtual reference signal multi-operating condition transformation matrix.
[0018] Optionally, the collecting of noise data at a target position and vibration acceleration data at a chassis of the test vehicle under different road driving conditions includes:
[0019] Arrange multiple vibration acceleration sensors on the chassis of the test vehicle as candidate sensor points to collect vibration acceleration data at various locations of the chassis as original reference signals;
[0020] A noise error sensor is arranged at the headrest human ear position to collect noise data at the target position;
[0021] The vibration acceleration data collected by the vibration acceleration sensor and the noise data collected by the noise error sensor are recorded when the test vehicle is traveling at a constant speed at different vehicle speeds and different road conditions.
[0022] Optionally, the calculation formula of the multi-coherence method is:
[0023]
[0024] in, is the multi-coherence between the reference signal and the noise signal; S xd is the cross power spectral density of the reference signal and the noise expected signal; S dd is the auto-power spectral density of the noise expected signal; For S xd The transposed conjugate of S xx is the autopower spectral density of the reference signal.
[0025] Optionally, performing singular value decomposition on the original reference signals at the screened distribution points to extract characteristic vectors and singular values under various road driving conditions includes:
[0026] Construct a covariance matrix of the original reference signal, the calculation formula of the covariance matrix is:
[0027]
[0028] Where X(n) is the reference signal matrix, representing the time series of I reference signals, and n is the length of the time series;
[0029] Perform singular value decomposition on the constructed covariance matrix, the expression is:
[0030] A=U*∑*V T
[0031] Where A = Cov(X(n)) is the covariance matrix, U is the left singular matrix, which represents the eigenvectors of multiple original reference signals in the signal source dimension, ∑ is a diagonal matrix whose diagonal elements are singular values, and V T is a right singular matrix;
[0032] According to the singular value decomposition results, the covariance matrix is expressed as:
[0033]
[0034] Among them, the left singular matrix U is the eigenvector under each road driving condition, and the diagonal elements of the singular value matrix ∑ are the singular values corresponding to each eigenvector.
[0035] Optionally, the step of calculating the Pearson correlation coefficients between all feature vectors in each feature vector group and calculating the average correlation coefficient of a single feature vector in the group includes:
[0036] The Pearson correlation coefficient between all eigenvectors in each eigenvector group is calculated using the following formula:
[0037]
[0038] Where r is the Pearson correlation coefficient; is the mean of the reference signal samples; is the mean of the noise signal samples; n is the number of sample elements;
[0039] The average correlation coefficient of a single eigenvector in its group is calculated using the following formula:
[0040]
[0041] In the formula, score i,j,k is the Pearson correlation coefficient between the kth feature of the i-th working condition and the j-th working condition; N is the number of working conditions.
[0042] To achieve the above-mentioned purpose, the second embodiment of the present application proposes a virtual reference signal multi-condition transformation matrix synthesis device, including:
[0043] An acquisition module is used to collect noise data at a target position and vibration acceleration data at a chassis of the test vehicle under different road driving conditions;
[0044] A screening module is used to analyze the correlation between the candidate sensor locations and the noise expected signal using a multi-coherence method based on the collected noise data and vibration acceleration data, and screen out the location of the original reference signal;
[0045] A singular value decomposition module is used to perform singular value decomposition on the original reference signal at the selected points to extract the characteristic vectors and singular values under various road driving conditions;
[0046] A sorting and combining module is used to sort the extracted feature vectors according to the corresponding singular value sizes, and then recombine the feature vectors at the same position to form a new feature vector group;
[0047] A calculation module, used for calculating the Pearson correlation coefficient between all feature vectors of each feature vector group, and calculating the average correlation coefficient of a single feature vector in its group;
[0048] The output module is used to select the eigenvector with the highest average correlation coefficient from each eigenvector group, and finally form a new virtual reference signal multi-operating condition transformation matrix.
[0049] To achieve the above-mentioned purpose, the third aspect of the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;
[0050] The memory stores computer-executable instructions;
[0051] The processor executes the computer-executable instructions stored in the memory to implement the method as described in any one of the first aspects.
[0052] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present application proposes a computer-readable storage medium, in which computer-readable storage medium is stored computer execution instructions, and when the computer execution instructions are executed by a processor, they are used to implement the method as described in any one of the first aspects.
[0053] To achieve the above-mentioned purpose, the fifth aspect of the present application proposes a computer program product, which implements any method in the first aspect when executed by a processor.
[0054] The technical solution provided by the embodiments of the present application brings at least the following beneficial effects:
[0055] (1) By constructing a multi-condition transformation matrix of a virtual reference signal, the characteristics of noise and vibration data under multiple conditions are comprehensively analyzed, the main characteristic signals are extracted and redundant signals are removed, thus achieving the goal of optimizing the quality of the reference signal and reducing the number of signals, avoiding the waste of computing resources and slowing down the system convergence caused by the redundancy of the original signal.
[0056] (12) By extracting the characteristic vectors of signals under different working conditions and recombining them, a unified transformation matrix compatible with multiple working conditions is constructed, which enables the active noise reduction system to achieve stable noise reduction control under multiple vehicle speeds and multiple road conditions, avoids the problem of limited noise reduction effect under a single working condition, and improves the applicability and reliability of the noise reduction system.
[0057] (3) By adopting the singular value decomposition and correlation analysis method to construct the transformation matrix, it is possible to significantly reduce the computational complexity while maintaining the main characteristic signals, avoid the problem of excessive computational complexity in the real-time update of the time domain transformation matrix method, and improve the operating efficiency in practical engineering applications.
[0058] (4) By screening the multi-condition correlation of the feature vector and optimizing the construction process of the virtual reference signal, the high-quality construction of the virtual reference signal is achieved, the problem of causal failure is avoided, and the noise reduction effect of low-frequency noise and the system convergence performance are improved.
[0059] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS
[0060] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:
[0061] Figure 1 A schematic diagram of a flow chart of a virtual reference signal multi-operating condition transformation matrix synthesis method provided in an embodiment of the present application;
[0062] Figure 2 A schematic diagram of the synthesis principle of the virtual reference signal provided in the embodiment of the present application;
[0063] Figure 3 A schematic diagram of Pearson correlation analysis provided by another embodiment of the present application;
[0064] Figure 4 A structural schematic diagram of a virtual reference signal multi-operating state transformation matrix synthesis device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0065] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.
[0066] In view of the problems existing in the prior art, the embodiment of the present application provides a virtual reference signal multi-condition transformation matrix synthesis method. Figure 1 A flowchart of a method for synthesizing a multi-condition transformation matrix of a virtual reference signal provided in an embodiment of the present application. Figure 2 A schematic diagram of the synthesis principle of a virtual reference signal provided in an embodiment of the present application.
[0067] like Figure 1 As shown, the method comprises the following steps:
[0068] Step 101 : collecting noise data at a target position and vibration acceleration data at a chassis of a test vehicle under different road driving conditions.
[0069] In the embodiment of the present application, firstly, a plurality of vibration acceleration sensors are arranged on the chassis of the test vehicle as candidate sensor points for collecting vibration acceleration data at various locations of the chassis as original reference signals; and a noise error sensor is arranged at the human ear position of the headrest for collecting noise data at the target position (i.e., near the head of the occupant in the vehicle) as a noise error signal.
[0070] Then, when the test vehicle is traveling at a constant speed at different speeds on different road conditions (such as flat road, bumpy road, etc.), the vibration acceleration data collected by the vibration acceleration sensor and the noise data collected by the noise error sensor are recorded.
[0071] It can be understood that through the above arrangement and data collection steps, the vibration and noise signals of the test vehicle under various working conditions are fully recorded, providing a comprehensive data basis for the subsequent analysis of sensor layout correlation and the optimization of the construction of virtual reference signals.
[0072] Step 102, based on the collected noise data and vibration acceleration data, the correlation between the candidate sensor locations and the noise expected signal is analyzed using a multi-coherence method to screen out the location of the original reference signal.
[0073] In the embodiment of the present application, the noise data and vibration acceleration data collected in step 101 are used, the vibration acceleration sensor points are used as candidate reference signals, and the noise signal at the target position is used as the noise expected signal.
[0074] Then the correlation between the signal and the noise signal of the candidate sensor points is calculated by the multi-coherence method. The calculation formula is:
[0075]
[0076] in, is the multi-coherence between the reference signal and the noise signal; S xd is the cross power spectral density of the reference signal and the noise expected signal; S dd is the auto-power spectral density of the noise expected signal; For S xd The transposed conjugate of S xx is the autopower spectral density of the reference signal.
[0077] Finally, based on the calculation results, the correlation between each sensor point and the target noise signal is analyzed, and the points with higher multiple coherence are selected as the positions of the original reference signals.
[0078] It can be understood that by analyzing the multiple coherence method, the sensor locations that are highly correlated with the noise signal can be accurately screened out, which can ensure the effectiveness of the selected original reference signal for noise control and provide high-quality input signals for subsequent feature extraction and noise reduction optimization.
[0079] Step 103, performing singular value decomposition on the original reference signals at the selected points to extract the characteristic vectors and singular values under various road driving conditions;
[0080] In the embodiment of the present application, the covariance matrix of the original reference signal is first constructed, and the calculation formula of the covariance matrix is:
[0081]
[0082] Where X(n) is the reference signal matrix, representing the time series of I reference signals, n is the length of the time series, and the covariance matrix Cov(X(n)) represents the mutual relationship and its own characteristics of the reference signals.
[0083] Then the covariance A = Cov(X(n)) is subjected to singular value decomposition, and the expression is:
[0084] A=U*∑*V T
[0085] Among them, U is a left singular matrix, which represents the eigenvectors of multiple original reference signals in the signal source dimension, ∑ is a diagonal matrix, and the elements on its diagonal are singular values, V T is a right singular matrix.
[0086] Finally, according to the singular value decomposition results, the covariance matrix can be expressed as:
[0087]
[0088] Among them, the left singular matrix U is the eigenvector under each road driving condition, and the diagonal elements of the singular value matrix ∑ are the singular values corresponding to each eigenvector.
[0089] The eigenvectors and singular values extracted by singular value decomposition in the embodiment of the present application can effectively characterize the main features of the original reference signal under different road driving conditions, and provide high-quality basic data for the subsequent construction of optimized virtual reference signals. The independence of the eigenvectors and the importance ranking of the singular values ensure the accuracy of signal optimization and the reliability of the noise reduction effect.
[0090] Step 104 , after sorting the extracted feature vectors according to the corresponding singular value sizes, the feature vectors are recombined according to the same positions to form a new feature vector group.
[0091] In the embodiment of the present application, the eigenvectors extracted under different working conditions are sorted according to the sizes of their corresponding singular values in the singular value decomposition, and then the eigenvectors at the same position in different working conditions are combined together to form a new eigenvector group.
[0092] That is, firstly, the eigenvectors extracted under each road driving condition are sorted from large to small according to the size of their corresponding singular values, ensuring that the eigenvectors with larger singular values are arranged first, because they contribute the most to the information of the original signal.
[0093] Then, for the feature vector groups of different working conditions (such as working condition 1, working condition 2, ... working condition K), the sorted feature vectors at the same position are recombined. For example:
[0094] The first eigenvector of working condition 1, the first eigenvector of working condition 2, and the first eigenvector of working condition K constitute a first eigenvector group;
[0095] The second eigenvector of working condition 1, the second eigenvector of working condition 2, and the second eigenvector of working condition K constitute a second eigenvector group;
[0096] And so on, until all eigenvector positions are reassembled.
[0097] Finally, through the above reorganization process, multiple new feature vector groups are generated, each group containing feature vectors from different working conditions.
[0098] The embodiment of the present application forms a unified feature vector group by sorting the feature vectors under different working conditions by singular values and then recombining them, which helps to capture the commonalities and differences of features under multiple working conditions, and lays the foundation for subsequent calculation of correlation and screening of representative feature vectors. This method ensures that the organizational structure of the feature vector takes into account both globality and representativeness, thereby improving the accuracy of virtual reference signal optimization.
[0099] Step 105, calculating the Pearson correlation coefficients between all eigenvectors in each eigenvector group, and calculating the average correlation coefficient of a single eigenvector in the group;
[0100] In the embodiment of the present application, the Pearson correlation coefficient between all feature vectors of each feature vector group is calculated by the following formula, which is expressed as:
[0101]
[0102] Where r is the Pearson correlation coefficient; is the mean of the reference signal samples; is the mean of the noise signal samples; n is the number of sample elements.
[0103] Through this formula, the correlation between each pair of feature vectors in the feature vector group can be quantified.
[0104] Then, for each eigenvector, calculate the average correlation coefficient between it and other eigenvectors in the group. The formula is as follows:
[0105]
[0106] In the formula, score i,j,k is the Pearson correlation coefficient between the kth feature of the i-th working condition and the j-th working condition; N is the number of working conditions.
[0107] This formula calculates the average correlation of a single eigenvector with other eigenvectors within its group, reflecting its representativeness within the group.
[0108] It can be understood that by calculating the Pearson correlation coefficient within the eigenvector group, the independence and correlation between the eigenvectors in the group can be quantified. Further calculating the average correlation coefficient of a single eigenvector can screen out the most representative eigenvector in the group, providing a scientific basis for the transformation matrix of the final synthetic virtual reference signal. This method ensures the accuracy of feature selection and the optimization effect of the transformation matrix.
[0109] Step 106 , selecting the eigenvector with the highest average correlation coefficient from each eigenvector group, and finally forming a new virtual reference signal multi-operating condition transformation matrix.
[0110] It can be understood that, in each feature vector group, the calculated average correlation coefficient reflects the degree of correlation between the feature vector and other feature vectors in the group.
[0111] Therefore, the embodiment of the present application selects the feature vector with the highest average correlation coefficient in each feature vector group as the representative feature vector of the group, because it is the most representative in the group and contains the maximum degree of information within the group.
[0112] Then, the representative eigenvectors selected from each eigenvector group are arranged according to their positions in the group and combined to form the final virtual reference signal multi-operating condition transformation matrix.
[0113] It should be noted that by selecting the eigenvectors with the highest average correlation coefficient, the most representative information in each group of eigenvectors is extracted, eliminating the interference of redundant features on the transformation matrix. The multi-condition transformation matrix of the virtual reference signal finally generated can fully take into account the main features under multiple conditions, and significantly improve the independence and quality of the reference signal, providing an optimized input signal for subsequent active noise reduction control.
[0114] The present application is further described below in conjunction with the embodiments, but it should not be understood that the protection scope of the present application is limited to the following embodiments. Without departing from the technical concept of the present application, various substitutions and changes made according to the common technical knowledge and customary means of those skilled in the art should be included in the protection scope of the present application.
[0115] Example 1
[0116] This embodiment takes the driving conditions of a vehicle under different road conditions and vehicle speeds as the research object, and specifically includes the following steps:
[0117] Step 1: Deploy sensors and collect data
[0118] 48 three-dimensional acceleration sensors are arranged on the chassis of the test vehicle, and 2 microphones are arranged at the inner and outer ears of the driver respectively to collect acceleration and noise data. The proposed working conditions are 4 types of road surfaces, 2 types of vehicle speeds, a total of 8 steady-state driving conditions, the collection time is 10 seconds, and the sampling frequency is 48000Hz. The selected road surface types and driving speeds are shown in Table 1:
[0119] Table 1
[0120]
[0121] Step 2: Select the reference signal locations
[0122] The acceleration data under 8 working conditions were calculated using the multi-coherence method, and the correlation analysis was performed with the noise data collected by the two microphones. The average coherence coefficient was used as the point selection index, and the coherence coefficients of the two microphones and the vibration acceleration data were added and averaged, from which 4 distribution points were selected. The screening results are shown in Table 2:
[0123] Table 2
[0124] serial number Location Select reference signal direction 1 Right front shock absorber tower top X 2 Subframe right crossbar X, Y 3 Rear right shock absorber tower top X, Y, Z 4 Rear left shock absorber tower top X, Y
[0125] Step 3: Singular Value Decomposition
[0126] The acceleration data of the four selected distribution points are subjected to singular value decomposition for eight working conditions, and the eigenvectors and singular values under each working condition are extracted for subsequent analysis.
[0127] Step 4: Feature vector reorganization
[0128] Sort the eigenvectors extracted in step 3 by the size of the singular value from large to small, and then recombine the eigenvectors at the same position in different working conditions. For example, the eigenvectors corresponding to the first singular value in all working conditions form the first eigenvector group, the eigenvectors corresponding to the second singular value form the second eigenvector group, and so on.
[0129] Step 5: Correlation analysis and screening
[0130] The Pearson correlation coefficients between all eigenvectors within each eigenvector group were calculated, and the average correlation coefficient of a single eigenvector within the group was calculated. Figure 3 is the correlation analysis result of the first eigenvector group. After the other eigenvector groups are processed in the same way, the eigenvectors with the highest average correlation coefficients in the first 7 eigenvector groups are selected. The screening results are shown in Table 3:
[0131] Table 3
[0132] Feature Number Selected working conditions Average correlation coefficient 1 E 0.9809 2 E 0.9671 3 A 0.8642 4 F 0.8682 5 E 0.7766 6 F 0.8085 7 H 0.7618
[0133] Step 6: Generate multi-condition transformation matrix
[0134] The eigenvector with the highest average correlation coefficient is selected from each eigenvector group to finally form a new multi-operating condition transformation matrix.
[0135] The multi-operating condition transformation matrix of the virtual reference signal generated by the above method can not only take into account the main characteristics of the vibration signal under different operating conditions, but also significantly improve the efficiency and applicability of the noise reduction system.
[0136] In order to implement the above-mentioned embodiment, the present application also proposes a virtual reference signal multi-operating condition transformation matrix synthesis device. Figure 4 Schematic diagram of the structure of a virtual reference signal multi-mode transformation matrix synthesis device 10 provided in an embodiment of the present application. Figure 4 As shown, the device comprises:
[0137] The acquisition module 100 is used to collect noise data at a target position and vibration acceleration data at a chassis of the test vehicle under different road driving conditions;
[0138] The screening module 200 is used to analyze the correlation between the candidate sensor locations and the noise expected signal by using the multi-coherence method according to the collected noise data and vibration acceleration data, and screen out the location of the original reference signal;
[0139] A singular value decomposition module 300 is used to perform singular value decomposition on the original reference signal at the selected points, and extract the characteristic vector and singular value under each road driving condition;
[0140] The sorting and combining module 400 is used to sort the extracted feature vectors according to the corresponding singular value sizes, and then re-combine the feature vectors at the same position to form a new feature vector group;
[0141] A calculation module 500 is used to calculate the Pearson correlation coefficient between all feature vectors in each feature vector group, and calculate the average correlation coefficient of a single feature vector in its group;
[0142] The output module 600 is used to select the eigenvector with the highest average correlation coefficient from each eigenvector group, and finally form a new virtual reference signal multi-operating condition transformation matrix.
[0143] In order to implement the above embodiments, the present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.
[0144] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.
[0145] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.
[0146] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in this application are in compliance with relevant laws and regulations and do not violate public order and good morals.
[0147] It should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.
[0148] The present application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by limiting data collection and deleting the data. In addition, when applicable, such personal information is de-identified to protect the privacy of the user.
[0149] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.
[0150] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.
[0151] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.
[0152] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.
[0153] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.
[0154] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.
[0155] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.
[0156] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.
[0157] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution of this application can be achieved, and this document is not limited here.
[0158] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.
Claims
1. A virtual reference signal multi-operating condition transformation matrix synthesis method, characterized in that: The following steps are involved: Collect noise data at target positions and vibration acceleration data at chassis of the test vehicle under different road driving conditions; According to the collected noise data and vibration acceleration data, the correlation between the candidate sensor locations and the expected noise signal is analyzed by using the multi-coherence method to screen out the location of the original reference signal. Perform singular value decomposition on the original reference signals at the selected points to extract the characteristic vectors and singular values under each road driving condition; After sorting the extracted eigenvectors according to the corresponding singular value sizes, the eigenvectors are recombined at the same positions to form a new eigenvector group; Calculate the Pearson correlation coefficient between all eigenvectors in each eigenvector group, and calculate the average correlation coefficient of a single eigenvector in its group; The eigenvector with the highest average correlation coefficient is selected from each eigenvector group to finally form a new virtual reference signal multi-operating condition transformation matrix.
2. The method according to claim 1, characterized in that The collecting of noise data at a target position and vibration acceleration data at a chassis of the test vehicle under different road driving conditions includes: Arrange multiple vibration acceleration sensors on the chassis of the test vehicle as candidate sensor points to collect vibration acceleration data at various locations of the chassis as original reference signals; A noise error sensor is arranged at the headrest human ear position to collect noise data at the target position; The vibration acceleration data collected by the vibration acceleration sensor and the noise data collected by the noise error sensor are recorded when the test vehicle is traveling at a constant speed at different vehicle speeds and different road conditions.
3. The method according to claim 2, characterized in that The calculation formula of the multi-coherence method is: in, is the multi-coherence between the reference signal and the noise signal; S xd is the cross power spectral density of the reference signal and the noise expected signal; S dd is the auto-power spectral density of the noise expected signal; For S xd The transposed conjugate of S xx is the autopower spectral density of the reference signal.
4. The method according to claim 3, characterized in that The singular value decomposition is performed on the original reference signal at the selected points to extract the characteristic vectors and singular values under each road driving condition, including: Construct a covariance matrix of the original reference signal, the calculation formula of the covariance matrix is: Where X(n) is the reference signal matrix, representing the time series of I reference signals, and n is the length of the time series; Perform singular value decomposition on the constructed covariance matrix, the expression is: A=U*∑*V T Where A = Cov(X(n)) is the covariance matrix, U is the left singular matrix, which represents the eigenvectors of multiple original reference signals in the signal source dimension, ∑ is a diagonal matrix whose diagonal elements are singular values, and V T is a right singular matrix; According to the singular value decomposition results, the covariance matrix is expressed as: Among them, the left singular matrix U is the eigenvector under each road driving condition, and the diagonal elements of the singular value matrix ∑ are the singular values corresponding to each eigenvector.
5. The method according to claim 4, characterized in that The step of calculating the Pearson correlation coefficients between all feature vectors in each feature vector group and calculating the average correlation coefficient of a single feature vector in the group includes: The Pearson correlation coefficient between all eigenvectors in each eigenvector group is calculated using the following formula: Where r is the Pearson correlation coefficient; is the mean of the reference signal samples; is the mean of the noise signal samples; n is the number of sample elements; The average correlation coefficient of a single eigenvector in its group is calculated using the following formula: In the formula, score i,j,k is the Pearson correlation coefficient between the kth feature of the i-th working condition and the j-th working condition; N is the number of working conditions.
6. A virtual reference signal multi-operating condition transformation matrix synthesis device, characterized in that: include: An acquisition module is used to collect noise data at a target position and vibration acceleration data at a chassis of the test vehicle under different road driving conditions; A screening module is used to analyze the correlation between the candidate sensor locations and the noise expected signal using a multi-coherence method based on the collected noise data and vibration acceleration data, and screen out the location of the original reference signal; A singular value decomposition module is used to perform singular value decomposition on the original reference signal at the selected points to extract the characteristic vectors and singular values under various road driving conditions; A sorting and combining module is used to sort the extracted feature vectors according to the corresponding singular value sizes, and then recombine the feature vectors at the same position to form a new feature vector group; A calculation module, used for calculating the Pearson correlation coefficient between all feature vectors of each feature vector group, and calculating the average correlation coefficient of a single feature vector in its group; The output module is used to select the eigenvector with the highest average correlation coefficient from each eigenvector group, and finally form a new virtual reference signal multi-operating condition transformation matrix.
7. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 5.
8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 5 when executed by a processor.
9. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 5 when being executed by a processor.