Low-frequency noise data processing method and device, electronic equipment and vehicle

Abstract

The invention relates to a low-frequency noise data processing method and device, electronic equipment and a vehicle, and relates to the technical field of data processing, and the method comprises the steps: firstly obtaining a target frequency corresponding to a to-be-determined frequency spectrum component; determining a Nyquist frequency corresponding to the target frequency; according to the Nyquist frequency, obtaining the time domain sampling number R required by the target frequency; selecting a target sequence corresponding to the first R sampling points in the displacement sequence, and performing butterfly recursion calculation of fast Fourier transform on the target sequence to obtain a result used for determining sound pressure level information corresponding to the frequency spectrum component, the displacement sequence is obtained by performing butterfly odd-even displacement of fast Fourier transform according to a time domain sequence corresponding to the low-frequency noise data. According to the technical scheme, for analysis and calculation of low-frequency noise, it can be guaranteed that the precision error is within the acceptable range, meanwhile, calculation redundancy is reduced, and calculation power is saved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device, electronic equipment and vehicle for processing low-frequency noise data. Background Art

[0002] The noise data generated by the operation of equipment such as motors or engines in vehicles can be used to analyze whether the equipment has operating abnormalities.

[0003] At present, in the scenario of measuring the high-frequency noise data and low-frequency noise data of the device at the same time, the algorithm with the highest accuracy needs to be satisfied, that is, it needs to satisfy the Nyquist sampling theorem of the highest frequency among these frequencies. For example, the same sampling points (obtained according to the Nyquist sampling theorem of the highest frequency) are used for analysis and calculation of both high-frequency noise data and low-frequency noise data.

[0004] However, this method does not require so many sampling points for the analysis and calculation of low-frequency noise data, which will cause computational redundancy and waste computing power. Summary of the invention

[0005] In view of this, the present application provides a method, device, electronic device and vehicle for processing low-frequency noise data, the main purpose of which is to improve the technical problem of the current existing technology for analyzing and calculating low-frequency noise data, which will cause calculation redundancy and waste of computing power.

[0006] In a first aspect, the present application provides a method for processing low-frequency noise data, comprising:

[0007] Obtaining a target frequency corresponding to a spectral component to be determined;

[0008] Determine the Nyquist frequency corresponding to the target frequency;

[0009] According to the Nyquist frequency, the number of time domain samples R required for the target frequency is obtained;

[0010] The target sequence corresponding to the first R sampling points is selected in the displacement sequence, and the target sequence is subjected to butterfly recursive calculation of fast Fourier transform, and the result is used to determine the sound pressure level information corresponding to the spectral component, wherein the displacement sequence is obtained by performing butterfly parity displacement of fast Fourier transform on the time domain sequence corresponding to the low-frequency noise data.

[0011] In a second aspect, the present application provides a low-frequency noise data processing device, comprising:

[0012] An acquisition module, configured to acquire a target frequency corresponding to a spectral component to be determined;

[0013] A determination module configured to determine a Nyquist frequency corresponding to a target frequency;

[0014] The acquisition module is further configured to acquire the number of time domain samples R required for the target frequency according to the Nyquist frequency;

[0015] The calculation module is configured to select a target sequence corresponding to the first R sampling points in the displacement sequence, and perform butterfly recursive calculation of fast Fourier transform on the target sequence, and the result obtained is used to determine the sound pressure level information corresponding to the spectral component, wherein the displacement sequence is obtained by performing butterfly parity displacement of fast Fourier transform on the time domain sequence corresponding to the low-frequency noise data.

[0016] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for controlling the vehicle function described in the first aspect is implemented.

[0017] In a fourth aspect, the present application provides an electronic device comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor implements the vehicle function control method described in the first aspect when executing the computer program.

[0018] In a fifth aspect, the present application provides a vehicle, comprising the device as described in the second aspect, or the electronic device as described in the fourth aspect.

[0019] By means of the above technical scheme, the present application provides a method, device, electronic device and vehicle for processing low-frequency noise data. Compared with the current prior art, the present application can be used for the analysis and calculation of low-frequency noise, which can save computing power. Specifically, first obtain the target frequency corresponding to the spectral component to be determined; determine the Nyquist frequency corresponding to the target frequency; then obtain the number of time domain samples R required for the target frequency based on the Nyquist frequency; select the target sequence corresponding to the first R sampling points in the displacement sequence for butterfly recursive calculation of fast Fourier transform, and the result is used to determine the sound pressure level information corresponding to the spectral component, wherein the displacement sequence is obtained by performing butterfly parity displacement of fast Fourier transform on the time domain sequence corresponding to the low-frequency noise data. This scheme is equivalent to downsampling the frequency domain data according to the number of time domain samples R that meets the Nyquist frequency, and then obtaining the sound pressure level calculation result that can meet the calculation accuracy requirements through butterfly recursive calculation of fast Fourier transform. By applying the technical solution of the present application, for the analysis and calculation of low-frequency noise, it is possible to reduce calculation redundancy and save computing power while ensuring that the accuracy error is within an acceptable range.

[0020] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0022] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0023] Figure 1 A schematic flow chart of a method for processing low-frequency noise data provided in an embodiment of the present application is shown;

[0024] Figure 2 A schematic diagram showing an example provided by an embodiment of the present application;

[0025] Figure 3 A schematic diagram showing an example provided by an embodiment of the present application;

[0026] Figure 4 A schematic diagram showing a flow chart of another method for controlling a vehicle function provided in an embodiment of the present application;

[0027] Figure 5 A schematic diagram showing an example display effect provided by an embodiment of the present application is shown;

[0028] Figure 6 A schematic diagram showing an example display effect provided by an embodiment of the present application is shown;

[0029] Figure 7 A schematic diagram of the structure of a low-frequency noise data processing device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0030] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the scheme of the present application will be further described below. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0031] In order to improve the current existing technology for analyzing and calculating low-frequency noise data, which will cause calculation redundancy and waste of computing power. This embodiment provides a method for processing low-frequency noise data, such as Figure 1 As shown, the method includes:

[0032] Step 101: Obtain a target frequency corresponding to a spectral component to be determined.

[0033] The spectrum refers to the representation of a time domain signal in the frequency domain. It can be obtained by Fourier transforming the signal. The result will be two graphs with amplitude and phase as the vertical axis and frequency as the horizontal axis. However, sometimes the phase information is omitted, and only the corresponding amplitude data at different frequencies are available. Sometimes the "amplitude spectrum" is used to represent the change of amplitude with frequency, and the "phase spectrum" is used to represent the change of phase with frequency. In simple terms, the spectrum can indicate which frequencies of sine waves a signal is composed of, and can also show information such as the size and phase of the sine waves of each frequency.

[0034] Sound is a mechanical vibration that can pass through substances in various states of matter. These substances that can transmit sound are called media. Sound cannot propagate in a vacuum. The sound we hear is also a sound wave with a certain frequency. The frequency range of human hearing is about 20 to 20,000 Hz. Anything beyond this range is not perceptible to our ears. Below 20 Hz is infrasound, and above 20 kHz is ultrasound. The higher the frequency of sound, the higher the pitch of the sound, and the lower the frequency of sound, the lower the pitch of the sound.

[0035] In the embodiment of the present application, the target frequency refers to the frequency of the noise data corresponding to the spectrum component currently to be determined.

[0036] Step 102: Determine the Nyquist frequency corresponding to the target frequency.

[0037] Nyquist frequency is the minimum sampling frequency that needs to be defined to prevent signal aliasing. Aliasing refers to the phenomenon that when the discrete Fourier transform is used to sample the signal domain in the frequency domain, the number of sampling points is less than the length of the time domain sequence, causing the time domain period extension sequence to overlap with each other.

[0038] If the sampling frequency is too low to avoid overlap, the sampling result will be different from the original sample. If the spectrum of a sample is a band-limited spectrum, that is, a spectrum that is 0 outside a certain frequency, then the sampling frequency must be greater than twice the current frequency to avoid overlap and thus distortion. The sampling frequency must be greater than twice the current frequency, which is the Nyquist criterion.

[0039] Downsampling is the process of reducing the sampling rate of a particular signal, usually to reduce the data transmission rate or data size. The downsampling factor (usually represented by the symbol M) is generally an integer or rational number greater than 1. This factor expresses how many times the sampling period has become larger than the original, or equivalently, how much the sampling rate has become smaller than the original. Since downsampling reduces the sampling rate, it is necessary to ensure that the Nyquist sampling theorem still holds at the new lower sampling rate.

[0040] In the embodiment of the present application, the Nyquist frequency corresponding to the target frequency is determined, that is, the minimum sampling frequency needs to be defined to prevent signal aliasing at the target frequency.

[0041] By determining the minimum sampling frequency, the minimum number of samples in the sample set required for downsampling at the current target frequency can be determined.

[0042] Step 103: Obtain the number of time domain samples R required for the target frequency according to the Nyquist frequency.

[0043] In the embodiment of the present application, obtaining the number of time domain samples is to determine the number of time domain samples required to calculate the frequency corresponding to the current spectrum component according to the Nyquist frequency, wherein the number of time domain signals corresponds to the number of frequency domain signals.

[0044] The time domain is the real world and the only domain that actually exists. When evaluating the performance of a digital product, analysis is usually performed in the time domain because that is where the performance of the product is ultimately measured.

[0045] The frequency domain is especially used in radio frequency and communication systems, and is also encountered in high-speed digital applications. The most important property of the frequency domain is that it is not real, but a mathematical construct. The time domain is the only objectively existing domain, while the frequency domain is a mathematical category that follows specific rules. The frequency domain is also called the God's perspective by some scholars.

[0046] The time domain uses the time axis as the coordinate to represent the relationship of dynamic signals, and the frequency domain uses the frequency axis as the coordinate to represent the signal. Generally speaking, the time domain is more vivid and intuitive, while the frequency domain is more concise, and the analysis of problems is more profound and convenient. At present, the time domain and frequency domain of signal analysis are interrelated, indispensable and complementary.

[0047] Step 104: Select a target sequence corresponding to the first R sampling points in the displacement sequence, and perform butterfly recursive calculation of fast Fourier transform on the target sequence. The obtained result is used to determine the sound pressure level information corresponding to the spectral component.

[0048] The displacement sequence is obtained by performing butterfly odd-even displacement of fast Fourier transform on the time domain sequence corresponding to the low-frequency noise data.

[0049] Fast Fourier transform is a fast algorithm for discrete Fourier transform. It is obtained by improving the algorithm of discrete Fourier transform based on the odd, even, imaginary and real characteristics of discrete Fourier transform.

[0050] The butterfly parity shift of the fast Fourier transform refers to the use of point-valued polynomial representation and the use of the unit root property to perform recursive calculations on the parity decomposition of the polynomial to accelerate the algorithm. The premise of the recursion is to rearrange the original sequence according to the butterfly parity distribution.

[0051] In the embodiment of the present application, the selected bit shift sequence includes 16 data 0-15, the number of time domain samples R required to obtain the target frequency is 4, and the minimum number of samples in the sample set required for downsampling at the current target frequency is 8 as an example, Figure 2 As shown, an odd-even butterfly permutation is performed. The first K numbers after the permutation are the permutation sequence of the original N number sequence downsampled to a K number sequence. Specifically, if the permutation sequence [0 1 2 3 4 5 6 7 8 9 10 111213 14 15] is downsampled to 8, the new permutation sequence is [0 2 4 6 8 10 12 14], as shown in Figure 3 shown.

[0052] For example, in the permutation sequence [0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15], the target sequence corresponding to the first four sampling points is [0 4 8 12][2 6 10 14][1 5 9 13][3 7 11 15]. Other cases can be deduced by analogy and no specific examples are given here.

[0053] The new sequence is transposed, that is, after the original sequence is transposed, the first R numbers are taken for butterfly recursive calculation of fast Fourier transform to obtain a sequence that is the same as the minimum number of samples in the sample set, and the spectrum corresponding to the downsampled sequence can be obtained. The result is used to determine the sound pressure level information corresponding to the spectral component.

[0054] Compared with the current existing technology, this embodiment can be used for the analysis and calculation of low-frequency noise, which can save computing power. Specifically, first obtain the target frequency corresponding to the spectral component to be determined; determine the Nyquist frequency corresponding to the target frequency; then obtain the number of time domain samples R required for the target frequency according to the Nyquist frequency; select the target sequence corresponding to the first R sampling points in the displacement sequence for butterfly recursive calculation of fast Fourier transform, and the result obtained is used to determine the sound pressure level information corresponding to the spectral component, wherein the displacement sequence is obtained by performing butterfly parity displacement of fast Fourier transform on the time domain sequence corresponding to the noise data, and then this embodiment can accurately perform the analysis and calculation of low-frequency noise data through a small amount of sampling point data. This embodiment is equivalent to downsampling the frequency domain data according to the number of time domain samples R that meets the Nyquist frequency, and then obtaining the sound pressure level calculation result that can meet the calculation accuracy requirements through butterfly recursive calculation of fast Fourier transform. By applying the technical solution of this embodiment, for the analysis and calculation of low-frequency noise, while ensuring that the accuracy error is within an acceptable range, the calculation redundancy is reduced and computing power is saved.

[0055] Further, as a refinement and extension of the above embodiment, in order to fully illustrate the specific implementation process of the method of this embodiment, this embodiment provides the following Figure 4 The specific method shown includes:

[0056] Step 201: Obtain a target frequency corresponding to a spectral component to be determined.

[0057] Optionally, the target frequency corresponding to the spectral component to be determined may be any frequency, taking the target frequency of 16 Hz as an example.

[0058] Step 202: Determine the Nyquist frequency corresponding to the target frequency.

[0059] In the embodiment of the present application, the calculation formula for obtaining the Nyquist frequency can be specifically as shown in the following formula 1:

[0060] f NQST =2f s (Formula 1)

[0061] Exemplarily, based on step 201, when the target frequency obtained is 8 Hz, the target frequency is multiplied by 2, and the obtained Nyquist frequency is 16 Hz. If the target frequency obtained is 5 Hz, the obtained Nyquist frequency is 10 Hz. Other situations can be deduced by analogy, and no specific examples are given here.

[0062] Step 203: Obtain the number of time domain samples R required for the target frequency according to the Nyquist frequency.

[0063] Optionally, step 203 may specifically include: analyzing the number of time domain samples R required for the target frequency according to the Nyquist frequency and the butterfly odd-even change rule of the fast Fourier transform. The butterfly odd-even change rule of the fast Fourier transform may be as follows: Figure 2 and Figure 3 The displacement rules shown in the embodiment.

[0064] Exemplarily, step 203 specifically further includes: using a first formula to calculate the number of time domain samples R, the first formula is specifically shown in the following formula 2:

[0065]

[0066] Among them, f NQST represents the Nyquist frequency, Indicates rounding up.

[0067] The rounding function refers to the largest integer that does not exceed the real number x, which is called the integer part of x and is denoted as [x] or INT(x).

[0068] In the embodiments of the present application, Indicates rounding up, that is, the result is the smallest integer greater than the current real number. When the content in the brackets is 1.6, take the smallest integer greater than 1.6, which is 2. When the content in the brackets is 1.1, take the smallest integer greater than 1.1, which is 2. The same can be said for other cases, and no specific examples are given here.

[0069] In the embodiment of the present application, according to the butterfly parity change rule of the fast Fourier transform, it can be specifically determined that the number of time domain samples R must be a positive integer, and the corresponding Nyquist frequency must be an integer power of 2. If the Nyquist frequency is not an integer power of 2, it is necessary to take the logarithm of the Nyquist frequency and round it up.

[0070] For example, if the Nyquist frequency is 200 Hz, which is greater than 2 to the 7th power and less than 2 to the 8th power, it needs to be rounded up to get R = 8. It can also be understood that the Nyquist frequency needs to be rounded up to an integer power of 2. If the Nyquist frequency is 200 Hz, it needs to be rounded up to 256 Hz, and R = 8.

[0071] Exemplarily, based on step 202, when the obtained Nyquist frequency is 16 Hz, the obtained number of time domain samples R is 4. Similarly, when the obtained Nyquist frequency is 10 Hz, the obtained number of time domain samples R is 4. Other situations can be deduced by analogy and no specific examples are given here.

[0072] Step 204: Obtain the number of recursive layers corresponding to the number R of time domain samples.

[0073] In the embodiment of the present application, the logarithm with base 2 is taken for the number of time domain samples R to obtain the number of recursive layers, and the number of recursive layers is specifically log2(R).

[0074] Exemplarily, based on step 203, the number of time domain samples R obtained is 4, and the corresponding number of recursive layers is 2.

[0075] Step 205: Perform butterfly recursive calculation of fast Fourier transform on the target sequence corresponding to the first R sampling points according to the number of recursive layers, and the obtained result is used to determine the sound pressure level information corresponding to the spectral component.

[0076] Optionally, step 205 may specifically include: based on the polynomial of the layer where the target sequence is located, deriving and calculating the upper layer polynomials successively according to the number of recursive layers, and finally obtaining the polynomial as the butterfly recursive calculation result of the fast Fourier transform.

[0077] Optionally, step 205 specifically further includes: starting from the layer where the target sequence is located, according to the number of recursive layers, using the second formula to perform butterfly recursive calculation of the fast Fourier transform, and the second formula is specifically shown in the following formula 3:

[0078] Y k,m+1 i =Y k,m 2i-1 +W 2m k Y k,m 2i (Formula 3)

[0079] Wherein, k represents the spectral component, Y k,m+1 i represents the i-th polynomial of the m+1-th layer, Y k,m 2i-1 represents the 2i-1th polynomial of the mth level, represents the unit root extracted at the mth level in the recursive calculation, Y k,m 2i represents the 2i-th polynomial of the m-th layer, where m is in the range of [0, log2(R)-1].

[0080] Optionally, step 205 specifically further includes: processing the butterfly recursive calculation result through the time domain sampling number R to obtain sound pressure level information corresponding to the spectral component.

[0081] In some embodiments, step 205 specifically further includes: obtaining a calculation result obtained by butterfly recursive calculation of fast Fourier transform; calculating the calculation result by a third formula to obtain sound pressure level information corresponding to the spectral component, and the third formula is shown in the following formula 4:

[0082]

[0083] Among them, Y k Represents the sound pressure level information corresponding to the spectral component, Y k,log2(R) 1 Represents the calculation result obtained by butterfly recursive calculation of fast Fourier transform.

[0084] Optionally, according to Pasval's theorem, after a sequence of N samples is subjected to a fast Fourier butterfly parity transform, in order to satisfy the conservation of energy, the result needs to be divided by 2 / N. However, the fast Fourier butterfly parity transform is not performed uniformly, and different spectra use different sequence sampling numbers. After the spectrum is obtained, it is not possible to uniformly divide it by 2 / N to obtain the same energy amplitude. Therefore, when calculating each frequency component, it is divided separately by the sampled sequence length, that is, R.

[0085] Exemplarily, based on step 204, when the number of recursive layers is 2, the number of time domain samples R is 4, and the target sequence corresponding to the first 4 sampling points is subjected to butterfly recursive calculation of fast Fourier transform. Since the number of recursive layers is 2, it is necessary to perform two recursive calculations on the target sequence to obtain a polynomial of the target sequence two layers upwards, and then multiply the obtained polynomial by the number of recursive layers and divide it by the length of the sampling sequence. The result is used to determine the sound pressure level information corresponding to the spectral component.

[0086] The sound pressure level is defined as the common logarithm of the ratio of the sound pressure p to be measured to the reference sound pressure p (ref), multiplied by 20, that is: 20lg p / p (ref), and its unit is decibel.

[0087] In the embodiment of the present application, each spectral component has corresponding sound pressure level information, and the sound pressure level information of multiple spectral components can be combined to obtain spectral information corresponding to the sound pressure level information of noise data.

[0088] Furthermore, taking the engine noise within 10s, the second-order energy is extracted by using two fast Fourier transform butterfly recursive calculation methods as an example, as Figure 5 shown.

[0089] The total sound pressure level difference is within plus or minus 1dB, and the root mean square error is 0.116dB, which meets the accuracy requirements.

[0090] Computing power saving description: The amount of computation required for butterfly recursive calculation of fast Fourier transform of a conventional sequence of length N=2M is shown in the following formula 5:

[0091] P 0 =Nlog2(N)=NM (Formula 5)

[0092] Among them, M is the number of recursive layers and N is the sequence length.

[0093] Nlog2(N) times of multiplication, and the computing power P required by the butterfly recursion of the non-uniform fast Fourier transform is shown in the following formula 6:

[0094]

[0095] The amount of computation that can be saved is calculated according to the following formula 7:

[0096]

[0097] As the length of N increases from 2 13 To 2 20 Changes can save computing power such as Figure 6 shown.

[0098] By applying the solution of this embodiment, the calculation accuracy error is within 1dB, and the sequence length is within 2 13 To 2 20 Changes, saving 5%-7.5% of computing power.

[0099] Compared with the current existing technology, this embodiment can save computing power while ensuring that the accuracy error is within an acceptable range for the analysis and calculation of low-frequency noise.

[0100] Furthermore, in order to fully illustrate the specific implementation process of the method of this embodiment, this embodiment provides the following examples, but is not limited thereto.

[0101] First, the sequence is subjected to fast Fourier butterfly parity shifting. The Nyquist frequency that satisfies the sampling theorem is calculated based on the data to be extracted. The number of time domain samples required to calculate the corresponding frequency of the current spectrum component is determined based on the Nyquist frequency. In the shifted sequence, the first R sampling points are selected, and the selected sequence is subjected to butterfly recursive calculation. The number of recursive layers is log2(R). The polynomial at the root of the arrow in the previous layer is calculated using the two parity polynomials pointed to by the arrows in the next layer: the calculation result of the final k-th component is obtained. According to Pasval's theorem, after the fast Fourier butterfly parity transform of the sequence of N samples, in order to satisfy the energy conservation, the result needs to be divided by 2 / N. The number of sequence samples used in the non-uniform fast Fourier butterfly parity shift for different spectra is different, and the same energy amplitude cannot be obtained by uniformly dividing by 2 / N after the spectrum is obtained. Therefore, each frequency component is calculated separately by the length of the sampled sequence to obtain the final k-th component of the spectrum.

[0102] Compared with the current existing technology, this embodiment is equivalent to downsampling the frequency domain data according to the number of time domain samples R that meets the Nyquist frequency, and then obtaining a sound pressure level calculation result that can meet the calculation accuracy requirements through butterfly recursive calculation of fast Fourier transform. For the analysis and calculation of low-frequency noise, this embodiment can save computing power while ensuring that the accuracy error is within an acceptable range.

[0103] Further, as Figures 1 to 4 The specific implementation of the method shown in the embodiment provides a low-frequency noise data processing device, such as Figure 7 As shown, the device includes: an acquisition module 31, a determination module 32, and a calculation module 33.

[0104] An acquisition module 31 is configured to acquire a target frequency corresponding to a spectral component to be determined;

[0105] A determination module 32, configured to determine a Nyquist frequency corresponding to a target frequency;

[0106] The acquisition module 31 is further configured to acquire the number of time domain samples R required for the target frequency according to the Nyquist frequency;

[0107] The calculation module 33 is configured to select a target sequence corresponding to the first R sampling points in the displacement sequence, and perform butterfly recursive calculation of fast Fourier transform on the target sequence, and the result obtained is used to determine the sound pressure level information corresponding to the spectral component, wherein the displacement sequence is obtained by performing butterfly parity displacement of fast Fourier transform on the time domain sequence corresponding to the noise data.

[0108] In a specific application scenario, the acquisition module 31 is specifically configured to analyze the number of time domain samples R required for the target frequency according to the Nyquist frequency and the butterfly odd-even change rule of the fast Fourier transform.

[0109] In a specific application scenario, the acquisition module 31 is further configured to calculate the number R of time domain samples using a first formula, where the first formula includes: Among them, f NQST represents the Nyquist frequency, Indicates rounding up.

[0110] In a specific application scenario, the calculation module 33 is specifically configured to obtain the number of recursive layers corresponding to the number R of time domain samples; and perform butterfly recursive calculation of fast Fourier transform on the target sequence according to the number of recursive layers.

[0111] In a specific application scenario, the calculation module 33 is further configured to derive and calculate the upper-layer polynomials based on the polynomial of the layer where the target sequence is located according to the number of recursive layers, and the final polynomial is used as the butterfly recursive calculation result of the fast Fourier transform.

[0112] In a specific application scenario, the calculation module 33 is further configured to start from the layer where the target sequence is located, and perform butterfly recursive calculation of the fast Fourier transform using the second formula according to the number of recursive layers, wherein the second formula includes: Wherein, k represents the spectral component, Y k,m+1 i represents the i-th polynomial of the m+1-th layer, Y k,m 2i-1 represents the 2i-1th polynomial of the mth level, represents the unit root extracted at the mth level in the recursive calculation, Y k ,m 2i represents the 2i-th polynomial of the m-th layer, where m is in the range of [0, log2(R)-1].

[0113] In a specific application scenario, the calculation module 33 is further configured to process the butterfly recursive calculation result through the time domain sampling number R to obtain the sound pressure level information corresponding to the spectral component.

[0114] In a specific application scenario, the calculation module 33 is further configured to obtain a calculation result obtained by butterfly recursive calculation of fast Fourier transform; calculate the calculation result by a third formula to obtain sound pressure level information corresponding to the spectral component, and the third formula includes: Among them, Y k represents the sound pressure level information corresponding to the spectral component, Y k,log2(R) 1 Represents the calculation result obtained by butterfly recursive calculation of fast Fourier transform.

[0115] It should be noted that for other corresponding descriptions of the functional units involved in the vehicle function control device provided in this embodiment, reference can be made to Figures 1 to 4 The corresponding description in will not be repeated here.

[0116] Based on the above Figures 1 to 4 The method shown in the embodiment also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned Figures 1 to 4 The method shown.

[0117] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of the present application.

[0118] Based on the above Figures 1 to 4 The method shown, and Figure 7 In order to achieve the above-mentioned purpose, the embodiment of the present application further provides an electronic device, which can be configured on a vehicle (such as a new energy vehicle) or a server, etc. The device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to achieve the above-mentioned Figures 1 to 4 The method shown.

[0119] Optionally, the above-mentioned physical device may also include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), etc.

[0120] Those skilled in the art will appreciate that the above-mentioned physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or a combination of certain components, or different arrangements of components.

[0121] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the above-mentioned physical device, and supports the operation of the information processing program and other software and / or programs. The network communication module is used to realize the communication between the components inside the storage medium, and the communication with other hardware and software in the information processing physical device.

[0122] Furthermore, based on the above Figures 1 to 4 The method shown, and Figure 7 The virtual device embodiment shown in the figure, as well as the electronic device embodiment described above, this embodiment also provides a vehicle, which may include the electronic device described above, and may further perform the following Figures 1 to 4 The method shown.

[0123] Through the description of the above implementation methods, the technicians in this field can clearly understand that the present application can be implemented by means of software plus the necessary general hardware platform, or by hardware. By applying the scheme of this embodiment, compared with the current prior art, this embodiment can be used for the analysis and calculation of low-frequency noise, which can save computing power. Specifically, first obtain the target frequency corresponding to the spectral component to be determined; determine the Nyquist frequency corresponding to the target frequency; then obtain the number of time domain samples R required for the target frequency according to the Nyquist frequency; select the target sequence corresponding to the first R sampling points in the displacement sequence for butterfly recursive calculation of fast Fourier transform, and the result obtained is used to determine the sound pressure level information corresponding to the spectral component, wherein the displacement sequence is obtained by performing butterfly parity displacement of fast Fourier transform according to the time domain sequence corresponding to the noise data, and then this embodiment can accurately perform the analysis and calculation of low-frequency noise data through a small amount of sampling point data. This embodiment is equivalent to downsampling the frequency domain data according to the number of time domain samples R that meets the Nyquist frequency, and then obtaining the sound pressure level calculation result that can meet the precise calculation requirements through butterfly recursive calculation of fast Fourier transform. By applying the technical solution of this embodiment, for the analysis and calculation of low-frequency noise, it is possible to reduce calculation redundancy and save computing power while ensuring that the accuracy error is within an acceptable range.

[0124] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0125] The above is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features applied for herein.

Claims

1. A method for processing low-frequency noise data, characterized in that: include: Obtaining a target frequency corresponding to a spectral component to be determined; determining a Nyquist frequency corresponding to the target frequency; According to the Nyquist frequency, obtaining the number R of time domain samples required for the target frequency; A target sequence corresponding to the first R sampling points is selected in the displacement sequence, and a butterfly recursive calculation of a fast Fourier transform is performed on the target sequence, and the result obtained is used to determine the sound pressure level information corresponding to the spectral component, wherein the displacement sequence is obtained by performing a butterfly parity displacement of a fast Fourier transform on a time domain sequence corresponding to the low-frequency noise data.

2. The method according to claim 1, characterized in that The step of obtaining the number of time domain samples R required for the target frequency according to the Nyquist frequency includes: The number of time domain samples R required for the target frequency is analyzed according to the Nyquist frequency and the butterfly odd-even change rule of the fast Fourier transform.

3. The method according to claim 2, characterized in that The step of analyzing the number of time domain samples R required for the target frequency according to the Nyquist frequency and the butterfly odd-even change rule of the fast Fourier transform includes: The time domain sampling number R is calculated using a first formula, wherein the first formula includes: Among them, f NQST represents the Nyquist frequency, Indicates rounding up.

4. The method according to claim 1, characterized in that: The step of selecting the target sequence corresponding to the first R sampling points in the permutation sequence to perform butterfly recursive calculation of fast Fourier transform includes: Obtaining the number of recursive layers corresponding to the number R of time domain samples; According to the number of recursive layers, a butterfly recursive calculation of fast Fourier transform is performed on the target sequence.

5. The method according to claim 4, characterized in that The butterfly recursive calculation of fast Fourier transform of the target sequence according to the number of recursive layers includes: Based on the polynomial of the layer where the target sequence is located, the upper layer polynomial is derived and calculated successively according to the number of recursive layers, and the polynomial finally obtained is used as the butterfly recursive calculation result of the fast Fourier transform.

6. The method according to claim 5, characterized in that The polynomial based on the layer where the target sequence is located, deriving and calculating the upper layer polynomial one by one according to the number of recursive layers, includes: Starting from the layer where the target sequence is located, according to the number of recursive layers, a butterfly recursive calculation of fast Fourier transform is performed using a second formula, wherein the second formula includes: AND k,m+1 i =And k,m 2i-1 +W 2m k AND k,m 2i Wherein, k represents the spectral component, Y k,m+1 i represents the i-th polynomial of the m+1-th layer, Y k,m 2i-1 represents the 2i-1th polynomial of the mth level, represents the unit root extracted at the mth level in the recursive calculation, Y k,m 2i represents the 2i-th polynomial of the m-th layer, where m is in the range of [0, log2(R)-1].

7. The method according to claim 5, characterized in that The method further comprises: The butterfly recursive calculation result is processed by the time domain sampling number R to obtain the sound pressure level information corresponding to the spectrum component.

8. The method according to claim 7, characterized in that The step of processing the butterfly recursive calculation result by the time domain sampling number R to obtain the sound pressure level information corresponding to the spectral component includes: The butterfly recursive calculation result is calculated by a third formula to obtain the sound pressure level information corresponding to the spectral component, and the third formula includes: Among them, Y k represents the sound pressure level information corresponding to the spectral component, Y k,log2(R) 1 represents the butterfly recursive calculation result.

9. A low-frequency noise data processing device, characterized in that: include: An acquisition module, configured to acquire a target frequency corresponding to a spectral component to be determined; A determination module, configured to determine a Nyquist frequency corresponding to the target frequency; The acquisition module is further configured to acquire the number R of time domain samples required for the target frequency according to the Nyquist frequency; The calculation module is configured to select a target sequence corresponding to the first R sampling points in the displacement sequence, and perform butterfly recursive calculation of fast Fourier transform on the target sequence, and the obtained result is used to determine the sound pressure level information corresponding to the spectral component, wherein the displacement sequence is obtained by performing butterfly parity displacement of fast Fourier transform on the time domain sequence corresponding to the low-frequency noise data.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

11. An electronic device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

12. A vehicle, characterized in that: include: The device as claimed in claim 9, or the electronic device as claimed in claim 11.

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