Noise data processing method and device, electronic equipment and vehicle

Abstract

The invention relates to a noise data processing method and device, electronic equipment and a vehicle, and relates to the technical field of noise data processing, and the method comprises the steps: obtaining first noise data corresponding to a to-be-determined frequency spectrum component in a first time domain window, and the length of the first time domain window is an integral multiple of a step length; second noise data corresponding to the frequency spectrum component in a first step length in the first time domain window is determined, third noise data corresponding to the frequency spectrum component in a last step length in a second time domain window is determined, and the second time domain window is a time domain window obtained after the first time domain window moves the step length; and subtracting the second noise data from the first noise data, and adding the third noise data to obtain fourth noise data corresponding to the frequency spectrum component in the second time domain window. Through application of the technical scheme of the invention, for data overlapping between time domain windows, calculation of overlapped data can be reduced, and calculation power is saved.

Description

Technical Field

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

[0002] The noise data generated by the motor or engine in the vehicle can be used to analyze whether the equipment has abnormal operation. In order to ensure the continuity of these data, the time domain windows are overlapped. For example, the first time domain window corresponds to the noise data between 0 and 1 second, while the second time domain window corresponds to the noise data between 0.1 and 1.1 seconds. The overlapping data of these two time domain windows is the noise data between 0.1 and 1 second.

[0003] At present, when analyzing and calculating the noise data corresponding to each time domain window, even though there is data overlap between the time domain windows, the noise data corresponding to each time domain window will be calculated separately and completely. This will cause the overlapping data to be analyzed and calculated repeatedly, which will cause computational redundancy and waste computing power. Summary of the invention

[0004] In view of this, the present application provides a noise data processing method, device, electronic device and vehicle, the main purpose of which is to improve the technical problem in the current prior art that when analyzing and calculating the noise data corresponding to each time domain window, even if there is data overlap between the time domain windows, the noise data corresponding to each time domain window will be calculated separately and completely, resulting in repeated analysis and calculation of overlapping data, causing calculation redundancy and waste of computing power.

[0005] In a first aspect, the present application provides a noise data processing method, comprising:

[0006] Acquire first noise data corresponding to the spectral component to be determined in a first time domain window, where the length of the first time domain window is an integer multiple of the step length;

[0007] Determine the second noise data corresponding to the first step of the spectral component in the first time domain window, and determine the third noise data corresponding to the last step of the spectral component in the second time domain window, the second time domain window being the time domain window obtained after the first time domain window is moved by the step;

[0008] The second noise data is subtracted from the first noise data, and the third noise data is added to obtain fourth noise data corresponding to the spectrum component in the second time domain window.

[0009] In a second aspect, the present application provides a noise data processing device, comprising:

[0010] An acquisition module is configured to acquire first noise data corresponding to the spectral component to be determined in a first time domain window, wherein the length of the first time domain window is an integer multiple of the step length;

[0011] A determination module is configured to determine second noise data corresponding to a first step of a spectral component in a first time domain window, and to determine third noise data corresponding to a last step of a spectral component in a second time domain window, wherein the second time domain window is a time domain window obtained by moving the first time domain window by a step;

[0012] The processing module is configured to subtract the second noise data from the first noise data and add the third noise data to obtain fourth noise data corresponding to the spectrum component in the second time domain window.

[0013] 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.

[0014] 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.

[0015] 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.

[0016] By means of the above technical solution, the present application provides a noise data processing method, device, electronic device and vehicle, firstly obtaining the first noise data corresponding to the spectral component to be determined in the first time domain window, the length of the first time domain window being an integer multiple of the step length; determining the second noise data corresponding to the spectral component in the first step length in the first time domain window, and determining the third noise data corresponding to the spectral component in the last step length in the second time domain window, the second time domain window being the time domain window obtained after the first time domain window is moved by the step length; then processing the first noise data, the second noise data and the third noise data to obtain the fourth noise data corresponding to the spectral component in the second time domain window. Compared with the current separate and complete calculation of each time domain window data with overlapping data, by applying the technical solution of the present application, when analyzing and calculating the noise data corresponding to each time domain window, the noise data corresponding to the latter time domain window can be accurately inferred by the noise data of the previous time domain window, the noise data in the first step length of the time domain window, and the noise data in the last step length of the latter time domain window combined with the noise data of the last step length of the latter time domain window. In this way, for the overlapping data between two time domain windows, there is no need to repeat the calculation in the calculation of the latter time domain window, thereby reducing the calculation of the overlapping data and saving computing power.

[0017] 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

[0018] 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.

[0019] 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.

[0020] Figure 1 A schematic diagram of a noise data processing method provided in an embodiment of the present application is shown;

[0021] Figure 2 A schematic diagram showing a flow chart of another noise data processing method provided in an embodiment of the present application;

[0022] Figure 3 A schematic diagram showing an example provided by an embodiment of the present application;

[0023] Figure 4 A schematic diagram showing an example provided by an embodiment of the present application;

[0024] Figure 5 A schematic diagram showing an example provided by an embodiment of the present application;

[0025] Figure 6 A schematic diagram of an example process provided by an embodiment of the present application is shown;

[0026] Figure 7 A schematic structural diagram of a noise data processing device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0027] 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.

[0028] In order to improve the technical problem that in the existing technology, when analyzing and calculating the noise data corresponding to each time domain window, even if there is data overlap between the time domain windows, the noise data corresponding to each time domain window will be calculated separately and completely, resulting in repeated analysis and calculation of overlapping data, causing calculation redundancy and wasting computing power. This embodiment provides a noise data processing method, such as Figure 1 As shown, the method includes:

[0029] Step 101: Obtain first noise data corresponding to a spectral component to be determined in a first time domain window.

[0030] The length of the first time domain window is an integer multiple of the step length.

[0031] 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.

[0032] The frequency domain is particularly 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 domain that exists objectively, while the frequency domain is a mathematical category that follows specific rules.

[0033] 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.

[0034] In the embodiment of the present application, the time domain window refers to the time window corresponding to the time domain data. The time window may specifically refer to a certain time period. The first time domain window specifically refers to the time domain data within a time window starting from 0.

[0035] The time step refers to the difference between two time points. In process simulation, the model discretizes the entire process into multiple small processes, and the time required for each step is the time step. When simulating the time response of a system, it is often necessary to set the time step. The size of the time step generally depends on the system properties and the purpose of the model. The larger its absolute value, the less calculation time; the smaller its absolute value, the longer the calculation time, the more detailed the simulation, and the more complex the process.

[0036] In an embodiment of the present application, the time window contains any number of time steps, and the time window is an integer multiple of the time step. For example, when the time window length is 1, the time step can be 0.1, 0.2, etc., which are not listed one by one here.

[0037] Optionally, the first noise data specifically refers to spectral component data corresponding to the data in the first time domain window.

[0038] Step 102: Determine the second noise data corresponding to the first step of the spectral component in the first time domain window, and determine the third noise data corresponding to the last step of the spectral component in the second time domain window.

[0039] The second time domain window is a time domain window obtained by moving the first time domain window by a step size.

[0040] 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.

[0041] 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.

[0042] In the embodiment of the present application, taking the first time domain window range as 0-1 and the step size as 0.1 as an example, the second time domain window range is the time domain window obtained after the first time domain window moves the step size, that is, 0.1-1.1, the first step size range of the first time domain window is 0-0.1, and the last step size range of the second time domain window is 1-1.1. Correspondingly, the overlapping part of the first time domain window and the second time domain window is 0.1-1.

[0043] Step 103: Subtract the second noise data from the first noise data, and add the third noise data to obtain fourth noise data corresponding to the spectral component in the second time domain window.

[0044] In the embodiment of the present application, the fourth noise data is the spectrum component data corresponding to the second time domain window obtained by moving the first time domain window by one step.

[0045] Compared with the current complete calculation of each time domain window data for data overlap, by applying the technical solution of this embodiment, when analyzing and calculating the noise data corresponding to each time domain window, the noise data of the previous time domain window and the noise data in the first step of the time domain window are combined with the noise data of the last step of the next time domain window, and the noise data corresponding to the next time domain window can be accurately calculated. In this way, for the overlapping data between the two time domain windows, there is no need to repeat the calculation in the calculation of the next time domain window, thereby reducing the calculation of the overlapping data and saving computing power.

[0046] 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 2 The specific method shown includes:

[0047] Step 201: Obtain first time domain data corresponding to a spectral component in a first time domain window.

[0048] Step 202: Perform a butterfly operation of a fast Fourier transform on the first time domain data to obtain first noise data.

[0049] In the embodiment of the present application, the first noise data is the noise spectrum data corresponding to the first time domain window data, and the noise spectrum data satisfies the matrix shown in the following formula 1:

[0050]

[0051] Optionally, the unit root in formula 1 is an operator of fast discrete Fourier transform, which is defined as shown in formula 2 below:

[0052]

[0053] For example, for a single frequency domain component Y k t , can be obtained by corresponding multiplication of time domain data and unit root. The corresponding relationship of multiplication is as follows: Figure 3 shown.

[0054] Optionally, step 202 may specifically include: obtaining a target frequency corresponding to a spectral component, and determining a Nyquist frequency corresponding to the target frequency; obtaining the number of time domain samples Z required for the target frequency based on the Nyquist frequency; in a displacement sequence corresponding to the first time domain data, selecting a target sequence corresponding to the first Z sampling points to perform butterfly recursive calculation of a fast Fourier transform to obtain first noise data, wherein the displacement sequence is obtained by performing butterfly parity displacement of a fast Fourier transform on the first time domain data.

[0055] 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.

[0056] 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.

[0057] 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, and the first K numbers after the permutation are the permutation sequence of downsampling the original sequence of N numbers to a sequence of K numbers. 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].

[0058] 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.

[0059] The new sequence is permuted, that is, after the original sequence is permuted, the first R numbers are taken for butterfly recursive calculation of fast Fourier transform to obtain a sequence with the same minimum number of samples in the sample set, and the noise spectrum data corresponding to the downsampled sequence can be obtained.

[0060] Optionally, the above method is a first method for obtaining the first noise data corresponding to the first time domain window, specifically obtaining the first noise data corresponding to the first time domain window through the butterfly recursive calculation of the fast Fourier transform, and then performing the butterfly recursive calculation of the fast Fourier transform on the first step in the first time domain window to obtain the noise spectrum data corresponding to the first step in the first time domain window.

[0061] Optionally, step 202 specifically also includes: obtaining second time domain data corresponding to the spectral component in each step in the first time domain window; performing butterfly operation of fast Fourier transform on each of the second time domain data, and serially synthesizing each calculation result to obtain first noise data.

[0062] Optionally, step 202 specifically also includes: performing butterfly operations of fast Fourier transform on each second time domain data to obtain polynomials corresponding to each second time domain data, and combining the polynomials corresponding to each second time domain data with unit roots to synthesize a target polynomial for the first noise data, wherein the target polynomial is used to determine the first noise data.

[0063] Optionally, step 202 specifically further includes: calculating and obtaining first noise data using formula 2 shown below;

[0064]

[0065] Among them, k represents the spectral component, Y k 1 represents the polynomial of the first noise data, R represents the number of steps in the first time domain window, and Y r represents the polynomial of the second time domain data corresponding to the rth step, s represents the number of data in the second time domain data, W N k represents a unit root.

[0066] For example, there are 4096 data in the first time domain window, and one step has 1024 data, so R is 4096 / 1024=4; then one window = a series of step 1+step 2+step 3+step 4, and r represents 1, 2, 3, 4.

[0067] Optionally, the above method is a second method for obtaining the first noise data corresponding to the first time domain window, specifically performing fast Fourier butterfly recursive calculation on the time domain data corresponding to each step in the first time domain window to obtain the noise spectrum data corresponding to each step in the first time domain window.

[0068] Exemplarily, the second noise data is noise spectrum data corresponding to the first step length of the first time domain window.

[0069] In an embodiment of the present application, the processing of the first time domain window data may include the above two methods. The first method is to directly perform fast Fourier butterfly recursive calculation to complete the spectrum calculation of the first window. The advantage is that the logic is simple, and the disadvantage is that when updating the first step, it is necessary to recalculate the time domain data in the first step of the first window that has been updated through the fast Fourier butterfly recursive calculation again; the second method is to perform local fast Fourier butterfly recursive calculation on the data in each step, and then serially synthesize the spectrum of the first window.

[0070] Step 203: determine the second noise data corresponding to the first step of the spectral component in the first time domain window, and determine the third noise data corresponding to the last step of the spectral component in the second time domain window.

[0071] The second time domain window is a time domain window obtained by moving the first time domain window by a step size.

[0072] Exemplarily, based on step 202, during the time domain window data collection process, the time domain window moves according to the step length. When the window moves forward by one step length, the corresponding relationship of a single time domain data Ykt is as follows: Figure 4 As shown, the left frame part and the middle frame part are the noise spectrum data corresponding to the first time domain window, the middle frame and the right frame part are the noise spectrum data corresponding to the second time domain window, the time domain data of the left frame is moved outside the window, and the data of the right frame is moved into the window, and the data of the middle frame is the overlapping part of the two windows. To calculate the new frequency component Ykt+1, it is necessary to subtract the left frame part from Ykt and add the right frame part. Since the corresponding relationship between the time domain data and the unit root in the middle frame has changed, it is also necessary to correct the middle frame. The correction relationship is shown in Formula 3 below:

[0073] Y k t+1 =Y k t ·(W N k ) -s -[x0·(W N k ) 0 +x1·(W N k ) 1 ...+x s-1 ·(W N k ) s-1 ]·(W N k ) -s +[x N ·(W N k ) N-s +x N+1 ·(W N k ) N-s+1 ...+x N+s-1 ·(W N k ) N-1 ] (Formula 3)

[0074] Optionally, multiply the first term in Formula 3 by (W N k ) -s , so when subtracting the left frame from the second term, you also need to multiply by (W N k ) -sThe content in the right box of the third part is the discrete Fourier transform of the time domain data entering the window within the new step size. N kN =1 Therefore, the third term in Formula 3 can be extracted in turn W N kN , W N -sk Therefore, Formula 3 can be written as Formula 4 as shown below:

[0075] Y k t+1 =Y k t ·(W N k ) -s -[x0·(W N k ) 0 +x1·(W N k ) 1 ...+x s-1 ·(W N k ) s-1 ]·(W N k ) -s +[x N ·(W N k ) 0 +x1·(W N k ) 1 ...+x s-1 ·(W N k ) s-1 ]·(W N k ) -s (Formula 4)

[0076] Exemplarily, when separately calculating the third noise data corresponding to the third time domain data corresponding to the last step in the second time domain window, the number of recursions is log2(S), and the butterfly recursive calculation of the local fast Fourier transform is performed according to the number of recursions. Since the extracted unit root is also different from the unit root in the above steps, this calculation process is called the local fast Fourier transform. For example, based on the third time domain data corresponding to the spectral component a in the last step in the second time domain window, when it is necessary to calculate the sound pressure level information corresponding to the spectral component a, first obtain the target frequency corresponding to the spectral component a; then determine the Nyquist frequency corresponding to the target frequency; then, based on the Nyquist frequency, obtain the number X of time domain samples required for the target frequency; select the target sequence corresponding to the first X sampling points in the displacement sequence, and perform butterfly recursive calculation of the fast Fourier transform on the target sequence, and the result is used to determine the sound pressure level information corresponding to the spectral component a, wherein the displacement sequence is obtained by performing butterfly parity displacement of the fast Fourier transform on the third time domain data. As Figure 5 As shown, the first polynomial of the third layer is selected as the target sequence in the displacement sequence, and then according to the calculated recursive number = 1, the first polynomial of the third layer is subjected to butterfly recursive calculation to obtain the first polynomial of the second layer, and then the sound pressure level information corresponding to the spectral component a can be determined based on the calculation result of the first polynomial of the second layer.

[0077] Optionally, in addition to the number of recursions, the unit root extracted from each level of the parity polynomial is as shown in Formula 5 below:

[0078]

[0079] Step 204: subtract the second noise data from the first noise data, and add the third noise data to obtain fourth noise data corresponding to the spectral component in the second time domain window.

[0080] Compared with the current complete calculation of each time domain window data for data overlap, by applying the technical solution of this embodiment, when analyzing and calculating the noise data corresponding to each time domain window, the noise data of the previous time domain window and the noise data in the first step of the time domain window are combined with the noise data of the last step of the next time domain window, and the noise data corresponding to the next time domain window can be accurately calculated. In this way, for the overlapping data between the two time domain windows, there is no need to repeat the calculation in the calculation of the next time domain window, thereby reducing the calculation of the overlapping data and saving computing power.

[0081] Further, such as Figure 6 As shown, specifically, first complete the spectrum calculation of the first window, as the window spectrum Y corresponding to time t0 t0, in calculating the next window spectrum Y t1 (window spectrum corresponding to time t1, the window takes an integer R times the step length), get the window spectrum Y corresponding to time t0 t0 The data corresponding to the first step in, that is, S1, and the window spectrum Y corresponding to time t1 t1 The data corresponding to the last step in S R+1 , through Y t1 =Y t0 -S1+S R+1 , and then calculate the window spectrum Y corresponding to time t1 t1 .

[0082] By applying the technical solution of this embodiment, repeated calculations can be avoided and computing power can be reduced. The number of multiplication calculations in the butterfly recursive calculation process of the original fast Fourier transform is Nlog2(N), while the number of multiplications in the butterfly recursive calculation of the rolling fast Fourier transform is Nlog2(S). Therefore, the amount of calculation saved per unit time is as shown in the following formula 6:

[0083]

[0084] Therefore, a lot of calculations can be saved when the base frequency is low and the step size is small. For example, for commonly used audio 16384HZ and a step size of 2048 sampling points, 393216 multiplication calculations can be saved per second, saving more than 20% of the calculation amount.

[0085] Compared with the current existing technology, by applying the technical solution of this embodiment, when analyzing and calculating the noise data corresponding to each time domain window, if there is data overlap between time domain windows, the calculation of the overlapping data can be reduced, saving computing power.

[0086] Further, as Figure 1 to Figure 2 The specific implementation of the method shown in the embodiment provides a noise data processing device, such as Figure 7 As shown, the device includes: an acquisition module 31, a determination module 32, and a processing module 33.

[0087] An acquisition module 31 is configured to acquire first noise data corresponding to a spectral component to be determined in a first time domain window, wherein the length of the first time domain window is an integer multiple of a step length;

[0088] A determination module 32 is configured to determine second noise data corresponding to the spectral component within a first step in the first time domain window, and to determine third noise data corresponding to the spectral component within a last step in a second time domain window, wherein the second time domain window is a time domain window obtained by moving the first time domain window by the step;

[0089] The processing module 33 is configured to subtract the second noise data from the first noise data and add the third noise data to obtain fourth noise data corresponding to the spectrum component in the second time domain window.

[0090] In a specific application scenario, the acquisition module 31 is specifically configured to acquire first time domain data corresponding to the spectral component in the first time domain window; perform a butterfly operation of fast Fourier transform on the first time domain data to obtain the first noise data.

[0091] In a specific application scenario, the acquisition module 31 is further configured to acquire the target frequency corresponding to the spectral component and determine the Nyquist frequency corresponding to the target frequency; based on the Nyquist frequency, acquire the number of time domain samples Z required for the target frequency; in the displacement sequence corresponding to the first time domain data, select the target sequence corresponding to the first Z sampling points to perform butterfly recursive calculation of fast Fourier transform to obtain the first noise data, wherein the displacement sequence is obtained by performing butterfly parity displacement of fast Fourier transform on the first time domain data.

[0092] In a specific application scenario, the acquisition module 31 is further configured to obtain the second time domain data corresponding to the spectral component in each step in the first time domain window; perform butterfly operation of fast Fourier transform on each of the second time domain data, and serially synthesize the calculation results to obtain the first noise data.

[0093] In a specific application scenario, the acquisition module 31 is specifically configured to perform butterfly operations of fast Fourier transform on each of the second time domain data to obtain polynomials corresponding to each of the second time domain data, and combine the polynomials corresponding to each of the second time domain data with unit roots to synthesize a target polynomial for the first noise data, and the target polynomial is used to determine the first noise data.

[0094] In a specific application scenario, the acquisition module 31 is further configured to calculate the first noise data using the following formula: Wherein, k represents the spectral component, Y k 1 represents the polynomial of the first noise data, R represents the number of steps in the first time domain window, Y r represents the polynomial of the second time domain data corresponding to the rth step length, s represents the number of data in the second time domain data, W N k represents a unit root.

[0095] It should be noted that for other corresponding descriptions of the functional units involved in the noise data processing device provided in this embodiment, reference can be made to Figure 1 to Figure 2 The corresponding description in will not be repeated here.

[0096] Based on the above Figure 1 to Figure 2 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 Figure 1 to Figure 2 The method shown.

[0097] 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.

[0098] Based on the above Figure 1 to Figure 2 The method shown, and Figure 6 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 Figure 1 to Figure 2 The method shown.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Furthermore, based on the above Figure 1 to Figure 2 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 Figure 1 to Figure 2 The method shown.

[0103] Through the description of the above implementation methods, those skilled in the art 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 technical solution of this embodiment, when analyzing and calculating the noise data corresponding to each time domain window, the noise data of the previous time domain window, the noise data within the first step of the time domain window, and the noise data of the last step in the next time domain window can be accurately inferred. The noise data corresponding to the next time domain window. In this way, for the overlapping data between the two time domain windows, there is no need to repeat the calculation in the calculation of the next time domain window, thereby reducing the calculation of the overlapping data and saving computing power.

[0104] 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.

[0105] 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 noise data processing method, characterized in that: include: Acquire first noise data corresponding to the spectral component to be determined in a first time domain window, wherein the length of the first time domain window is an integer multiple of the step length; Determine second noise data corresponding to the spectral component in a first step in the first time domain window, and determine third noise data corresponding to the spectral component in a last step in a second time domain window, where the second time domain window is a time domain window obtained by moving the first time domain window by the step; The second noise data is subtracted from the first noise data, and the resultant is added to the third noise data to obtain fourth noise data corresponding to the spectrum component in the second time domain window.

2. The method according to claim 1, characterized in that The obtaining of first noise data corresponding to the spectral component to be determined in the first time domain window includes: Acquire first time domain data corresponding to the spectral component in the first time domain window; The first time domain data is subjected to a butterfly operation of a fast Fourier transform to obtain the first noise data.

3. The method according to claim 2, characterized in that The first time domain data is subjected to a butterfly operation of a fast Fourier transform to obtain the first noise data, including: Acquire a target frequency corresponding to the spectral component, and determine a Nyquist frequency corresponding to the target frequency; According to the Nyquist frequency, obtaining the number of time domain samples Z required for the target frequency; In the displacement sequence corresponding to the first time domain data, the target sequence corresponding to the first Z sampling points is selected to perform butterfly recursive calculation of fast Fourier transform to obtain the first noise data, wherein the displacement sequence is obtained by performing butterfly parity displacement of fast Fourier transform on the first time domain data.

4. The method according to claim 1, characterized in that The obtaining of first noise data corresponding to the spectral component to be determined in the first time domain window includes: Acquire second time domain data corresponding to the spectral component in each step in the first time domain window; The second time domain data are respectively subjected to butterfly operation of fast Fourier transform, and the calculation results are serially synthesized to obtain the first noise data.

5. The method according to claim 4, characterized in that The method of performing butterfly operation of fast Fourier transform on each of the second time domain data and serially synthesizing the calculation results to obtain the first noise data includes: Perform butterfly operation of fast Fourier transform on each of the second time domain data to obtain polynomials corresponding to each of the second time domain data, and combine the polynomials corresponding to each of the second time domain data with unit roots to synthesize a target polynomial of the first noise data, and the target polynomial is used to determine the first noise data.

6. The method according to claim 5, characterized in that The method of performing butterfly operation of fast Fourier transform on each of the second time domain data to obtain polynomials corresponding to each of the second time domain data, and combining the polynomials corresponding to each of the second time domain data with unit roots to synthesize the target polynomial of the first noise data includes: The first noise data is calculated using the following formula: Wherein, k represents the spectral component, Y k 1 represents the target polynomial of the first noise data, R represents the number of steps in the first time domain window, and Y r represents the polynomial of the second time domain data corresponding to the rth step length, s represents the number of data in the second time domain data, W N k represents a unit root.

7. The method according to any one of claims 1 to 6, characterized in that The determining of the third noise data corresponding to the last step of the spectrum component in the second time domain window comprises: Acquire third time domain data corresponding to the last step of the spectral component in the second time domain window; The third time domain data is subjected to a butterfly operation of a fast Fourier transform to obtain the third noise data.

8. A noise data processing device, characterized in that: include: An acquisition module is configured to acquire first noise data corresponding to the spectral component to be determined in a first time domain window, wherein the length of the first time domain window is an integer multiple of the step length; a determination module configured to determine second noise data corresponding to the spectral component within a first step in the first time domain window, and to determine third noise data corresponding to the spectral component within a last step in a second time domain window, wherein the second time domain window is a time domain window obtained by moving the first time domain window by the step; The processing module is configured to subtract the second noise data from the first noise data and add the third noise data to obtain fourth noise data corresponding to the spectrum component in the second time domain window.

9. 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 7 is implemented.

10. 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 7 is implemented.

11. A vehicle, characterized in that: include: The device as claimed in claim 8, or the electronic device as claimed in claim 10.

Images