Noise data processing method and apparatus, and electronic device and vehicle

Abstract

A noise data processing method and apparatus, and an electronic device and a vehicle, which relate to the technical field of noise data processing. The method comprises: acquiring first noise data, corresponding to a spectrum component to be determined, in a first time-domain window (101), wherein the length of the first time-domain window is an integer multiple of a step size; determining second noise data corresponding to the spectrum component within the first step size in the first time-domain window, and determining third noise data corresponding to the spectrum component within the last step size in a second time-domain window (102), wherein the second time-domain window is a time-domain window that is obtained after the first time-domain window is moved by the step size; and subtracting the second noise data from the first noise data, and adding the third noise data, so as to obtain fourth noise data corresponding to the spectrum component in the second time-domain window (103). By means of applying the technical solution of the present application, with regard to overlapping data between time-domain windows, the computation of the overlapping data can be reduced, thus saving on computing power.

Description

Noise data processing method, device, electronic equipment and vehicle

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to the Chinese patent application filed with the China Patent Office on June 19, 2023, with application number 202310724699.5 and application name “Noise data processing method, device, electronic equipment and vehicle”, the entire contents of which are incorporated by reference into this application. Technical Field

[0003] The present application relates to the technical field of noise data processing, and is related to but not limited to a noise data processing method, device, electronic equipment and vehicle. Background Art

[0004] The noise audio generated by devices like motors and engines in vehicles can be used to analyze whether they are operating abnormally. To ensure continuity in data analysis, overlapping time windows are used. For example, the first time window captures noise audio between 0 and 1 second, while the second time window captures noise audio between 0.1 and 1.1 seconds. The overlapping portion of these two time windows contains the noise audio between 0.1 and 1 second.

[0005] Currently, when analyzing and calculating the noise audio corresponding to each time domain window, even though there is data overlap between the time domain windows, the noise audio corresponding to each time domain window is 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.

[0006] Summary of the Invention

[0007] In view of this, the present application provides a noise data processing method, device, electronic device and vehicle, which can reduce the calculation of overlapping data and save computing power.

[0008] In a first aspect, the present application provides a noise data processing method, comprising:

[0009] Obtaining 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 size;

[0010] Determining second noise data corresponding to a first step of the spectral component in a first time domain window, and determining third noise data corresponding to a last step of the spectral component 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 size;

[0011] 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 spectral component in the second time domain window.

[0012] In a second aspect, the present application provides a noise data processing device, comprising:

[0013] an acquiring part configured to 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;

[0014] a determining portion configured to determine second noise data corresponding to a first step of the spectral component in a first time domain window, and to determine third noise data corresponding to a last step of the spectral component 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 size;

[0015] The processing part 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.

[0016] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the noise data processing method described in the first aspect.

[0017] In a fourth aspect, the present application provides an electronic device, comprising:

[0018] a memory configured to store a computer program;

[0019] The processor is configured to implement the noise data processing method described in the first aspect when executing the computer program stored in the memory.

[0020] 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.

[0021] In a sixth aspect, the present application provides a computer program product, comprising computer executable instructions or a computer program, which, when executed by a processor, implements the noise data processing method described in the first aspect.

[0022] By means of the above technical solution, the present application provides a noise data processing method, device, electronic device and vehicle, which first obtains 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; determines second noise data corresponding to the spectral component in the first step length of the first time domain window, and determines third noise data corresponding to the spectral component in the last step length of the second time domain window, where the second time domain window is the time domain window obtained by moving the first time domain window by the step length; then processes the first noise data, the second noise data and the third noise data to obtain fourth noise data corresponding to the spectral component in the second time domain window. Compared with the current method of calculating the data of each time domain window with overlapping data separately and completely, 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 using the noise data of the previous time domain window, the noise data within the first step length of the time domain window, and the noise data in the last step length of the latter 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 latter time domain window, thereby reducing the calculation of the overlapping data and saving computing power.

[0023] 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

[0024] 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.

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0026] FIG1 is a schematic diagram showing a flow chart of a noise data processing method provided in an embodiment of the present application;

[0027] FIG2 is a schematic diagram showing a flow chart of another noise data processing method provided in an embodiment of the present application;

[0028] FIG3 shows a schematic diagram of an example provided by an embodiment of the present application;

[0029] FIG4 shows a schematic diagram of an example provided by an embodiment of the present application;

[0030] FIG5 shows a schematic diagram of an example provided by an embodiment of the present application;

[0031] FIG6 shows a schematic diagram of an example process provided by an embodiment of the present application;

[0032] FIG7 shows a schematic structural diagram of a noise data processing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0033] In order to more clearly understand the above-mentioned objectives, features and advantages of the present application, the scheme of the present application will be further described below. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0034] In order to improve the technical problem in the current related 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 computational redundancy and wasting computing power, this embodiment provides a noise data processing method, as shown in Figure 1, the method comprising:

[0035] Step 101: Obtain first noise data corresponding to a spectral component to be determined in a first time domain window.

[0036] The length of the first time domain window is an integer multiple of the step length.

[0037] The time domain is the real world, the only domain that actually exists. When evaluating the performance of digital products, analysis is usually performed in the time domain because that is where the product's performance is ultimately measured.

[0038] The frequency domain is particularly common in radio frequency and communication systems, and is also encountered in high-speed digital applications. Its most important property is that it is not real, but a mathematical construct. While the time domain is the only objective domain, the frequency domain is a mathematical domain that follows specific rules. Some scholars have even referred to the frequency domain as the "God's perspective."

[0039] The time domain represents dynamic signal relationships using time as the coordinate, while the frequency domain represents signals using frequency as the coordinate. Generally speaking, the time domain is more vivid and intuitive, while the frequency domain is more concise, allowing for deeper and more convenient analysis. Currently, signal analysis in the time and frequency domains is interconnected, each indispensable and complementary.

[0040] In the embodiments of the present application, the time domain window refers to the time window corresponding to the time domain data. In some embodiments, the time window may refer to a certain time period, and the first time domain data may refer to the time domain data within a time window starting from 0.

[0041] In some embodiments, the step size includes a time step, which can be calculated as the difference between two consecutive time points. In process simulation, the model discretizes the entire process into multiple smaller processes, and the time required for each step is the time step. Setting a time step is often necessary when simulating the time response of a system. The size of the time step generally depends on the system properties and the purpose of the model. A larger absolute value reduces the computation time; a smaller absolute value increases the computation time, resulting in a more refined simulation and a more complex process.

[0042] 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.

[0043] Optionally, the first noise data refers to spectral component data corresponding to the data in the first time domain window.

[0044] 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.

[0045] The second time domain window is a time domain window obtained by moving the first time domain window by a step size.

[0046] A spectrum is a representation of a time-domain signal in the frequency domain. It can be obtained by performing a Fourier transform on the signal. The resulting graphs are plotted with amplitude and phase on the vertical axis and frequency on the horizontal axis. However, phase information is sometimes omitted, leaving only the amplitude data corresponding to different frequencies. The "amplitude spectrum" is sometimes used to show how amplitude varies with frequency, while the "phase spectrum" shows how phase varies with frequency. Simply put, a spectrum shows which frequencies of sinusoidal waves make up a signal, and also reveals information such as the magnitude and phase of each frequency.

[0047] Sound is a mechanical vibration that can travel through matter in all its states. These substances that can propagate sound are called media. Sound cannot propagate in a vacuum. The sound we hear is also a sound wave with a certain frequency. The human ear's frequency range is approximately 20 to 20,000 Hz; frequencies outside this range are imperceptible to the human ear. Sounds below 20 Hz are considered infrasound, while sounds above 20 kHz are considered ultrasonic. The higher the frequency, the higher the pitch, while the lower the frequency, the lower the pitch.

[0048] In an embodiment of the present application, taking the first time domain window range as 0 to 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 to 1.1. The first step size range of the first time domain window is 0 to 0.1, and the last step size range of the second time domain window is 1 to 1.1. Correspondingly, the overlapping part of the first time domain window and the second time domain window is 0.1 to 1.

[0049] Exemplarily, the second noise data is noise spectrum data corresponding to the first step length of the first time domain window.

[0050] 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.

[0051] 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.

[0052] Compared to the current practice of performing a complete calculation for each time domain window with overlapping data, 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 (such as the first time domain window) and the noise data within the first step of the time domain window (the first time domain window) are combined with the noise data of the last step of the next time domain window (such as the second 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 (the second time domain window), thereby reducing the calculation of the overlapping data and saving computing power.

[0053] Furthermore, as a refinement and extension of the above embodiment, this embodiment provides a noise data processing method as shown in FIG2 , which includes:

[0054] Step 201: Acquire first time domain data corresponding to a spectral component in a first time domain window.

[0055] Step 202: Perform a butterfly operation of a fast Fourier transform on the first time domain data to obtain first noise data.

[0056] In the embodiment of the present application, the first noise data is noise spectrum data corresponding to the first time domain data, and the noise spectrum data satisfies the matrix shown in the following formula 1:

[0057] Optionally, the unit root in Formula 1 is an operator of the fast discrete Fourier transform, which is defined as shown in Formula 2 below:

[0058] For example, for a single frequency domain component Y k t , can be obtained by the corresponding multiplication of time domain data and unit root, and the corresponding relationship of multiplication is shown in Figure 3.

[0059] Optionally, step 202 may include: obtaining a target frequency corresponding to a spectral component, and determining the 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; Z is an integer not less than 1; in a permutation sequence corresponding to the first time domain data, selecting a target sequence corresponding to the first Z sampling points and performing butterfly recursive calculation of a fast Fourier transform to obtain first noise data, wherein the permutation sequence is obtained by performing butterfly parity permutation of a fast Fourier transform on the first time domain data.

[0060] Fast Fourier Transform (FFT), also known as Fast Fourier Transform, is a fast algorithm for Discrete Fourier Transform (DFT). It is obtained by improving the DFT algorithm based on the odd, even, imaginary, and real characteristics of DFT.

[0061] The butterfly recursive calculation 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 calculation of the polynomial parity decomposition to accelerate the algorithm. The premise of the recursion is to perform butterfly parity permutation, that is, to rearrange the original sequence (such as the first time domain data) according to the butterfly parity distribution.

[0062] In an embodiment of the present application, a permutation sequence contains 16 numbers, such as data 0-15, the number of time domain samples Z 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. For example, a butterfly parity 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. For example, if the permutation sequence [0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] is downsampled to 8, the new permutation sequence is [0 2 4 6 8 10 12 14].

[0063] 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 are not given in detail here.

[0064] The new sequence is permuted, that is, after the original sequence is permuted, the first Z numbers are taken to perform butterfly recursive calculation of the 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.

[0065] Optionally, the above method is a first method for obtaining first noise data corresponding to the first time domain window, including:

[0066] The first noise data corresponding to the first time domain window is obtained by performing butterfly calculation of fast Fourier transform on the first time domain data corresponding to the determined spectral component in the first time domain window.

[0067] Optionally, step 202 further includes: obtaining second time domain data corresponding to each step of the spectral component in the first time domain window; performing butterfly operations of fast Fourier transform on each second time domain data, and serially synthesizing each calculation result to obtain first noise data.

[0068] Optionally, step 202 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, and the target polynomial is used to determine the first noise data.

[0069] Optionally, step 202 further includes: calculating first noise data using formula 2 shown below;

[0070] Among them, 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, s represents the number of data in the second time domain data, W N k represents a unit root.

[0071] 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, and 4.

[0072] Optionally, the above method is a second method for obtaining the first noise data corresponding to the first time domain window, including: performing a 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 of the first time domain window, and serially synthesizing the first noise data corresponding to the first time domain window based on the noise spectrum data corresponding to each step of the first time domain window. In an embodiment of the present application, the processing of the first time domain data can include the above two methods. The first method is to directly perform a 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 perform a fast Fourier butterfly recursive calculation again to calculate the time domain data in the first step that has been updated in the first window; the second method is to perform a local fast Fourier butterfly recursive calculation on the data in each step, and then serially synthesize the spectrum of the first window.

[0073] 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.

[0074] The second time domain window is a time domain window obtained by moving the first time domain window by a step size.

[0075] Exemplarily, based on step 202, during the time domain window data acquisition process, the time domain window moves according to the step size. When the window moves forward by one step size, the corresponding relationship of a single time domain data is shown in FIG4 , wherein the left frame portion and the middle frame portion are the noise spectrum data corresponding to the first time domain window, the middle frame portion and the right frame portion are the noise spectrum data corresponding to the second time domain window, the time domain data of the left frame moves outside the window, and the data of the right frame moves into the window, and the data of the middle frame is the overlapping part of the two windows, and the new frequency component Y is calculated. k t+1 Need to be in Y k t Based on the above, the left frame is subtracted and the right frame is added. Since the correspondence between the time domain data and the unit root in the middle frame has changed, the middle frame needs to be corrected. For example, the corrected correspondence can be shown in the following formula 3: 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)

[0076] Optionally, multiply the first term in Formula 3 by (W N k ) -s , so when subtracting the left frame part from the second term, you also need to multiply it by (W N k ) -s The 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: 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)

[0077] 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 a butterfly recursive calculation of a 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 a local fast Fourier transform. For example, based on the third time domain data corresponding to the last step in the second time domain window of the spectral component a, 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 permutation sequence, and perform a butterfly recursive calculation of a 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 permutation sequence is obtained by performing a butterfly parity permutation of a fast Fourier transform on the third time domain data. As shown in FIG5 , the first polynomial of the third layer is selected as the target sequence in the displacement sequence. Then, according to the calculated recursion number = 1, the first polynomial of the third layer is subjected to butterfly recursive calculation to obtain the first polynomial of the second layer. Then, based on the calculation result of the first polynomial of the second layer, the sound pressure level information corresponding to the spectral component a can be determined.

[0078] Optionally, in addition to the number of recursions, the unit root extracted from each level of the parity polynomial is as shown in the following formula 5:

[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 to the current practice of performing a complete calculation for each overlapping time-domain window, the technical solution of this embodiment allows accurate deduction of the noise data corresponding to each time-domain window by analyzing and calculating the noise data corresponding to the previous time-domain window, the noise data within the first step of the previous time-domain window, and the noise data within the last step of the next time-domain window. This eliminates the need for repeated calculations for the overlapping data between two time-domain windows in the calculation of the next time-domain window, thereby reducing the calculation of the overlapping data and saving computing power.

[0081] Furthermore, as shown in FIG6 , illustratively, the spectrum calculation of the first window is completed first, and the spectrum Y of the window corresponding to the time t0 is taken as t0 , in calculating the next window spectrum Y t1 (window spectrum corresponding to time t1, the window takes an integer R times the step size), 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 shown in the following formula 6:

[0083] Therefore, a lot of calculations can be saved when the fundamental 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.

[0084] Compared with the current related technologies, 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.

[0085] Furthermore, this embodiment provides a noise data processing device, as shown in FIG7 , which includes: an acquisition part 31 , a determination part 32 , and a processing part 33 .

[0086] An acquiring part 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 the step length;

[0087] a determining portion 32 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, where the second time domain window is a time domain window obtained by shifting the first time domain window by the step;

[0088] The processing part 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 spectral component in the second time domain window.

[0089] In some embodiments, the acquisition part 31 is further configured to acquire first time domain data corresponding to the spectral component in the first time domain window; and perform a butterfly operation of fast Fourier transform on the first time domain data to obtain the first noise data.

[0090] In some embodiments, the acquisition part 31 is further configured to obtain the target frequency corresponding to the spectral component and determine the Nyquist frequency corresponding to the target frequency; based on the Nyquist frequency, obtain 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.

[0091] In some embodiments, the acquisition part 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 second time domain data, and serially synthesize the various calculation results to obtain the first noise data.

[0092] In some embodiments, the acquisition part 31 is also 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.

[0093] Formula, s represents the number of data in the second time domain data, W N krepresents a unit root.

[0094] It should be noted that for other corresponding descriptions of the various functional units involved in the noise data processing device provided in this embodiment, reference can be made to the corresponding descriptions of the noise data processing method embodiment provided in this application. For example, reference can be made to the corresponding descriptions in Figures 1 to 2, which will not be repeated here.

[0095] Based on the noise data processing method provided in the present application, this 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 noise data processing method provided in the present application is implemented.

[0096] The present application provides a computer program product, including computer executable instructions or a computer program, which, when executed by a processor, implements the noise data processing method provided in the present application.

[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.), and includes 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 embodiments of the noise data processing method and the noise data processing device provided in this application, to achieve the above-mentioned purpose, the embodiments of this application also provide an electronic device that can be configured on the side of an automobile (such as a new energy vehicle) or a server, etc., and the electronic device includes a memory and a processor. The memory is used to store computer programs, and the processor is used to implement the noise data processing method provided in this application when executing the computer programs stored in the memory.

[0099] Optionally, the physical device may further include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a Wi-Fi module, and the like. The user interface may include a display, an input unit such as a keyboard, and the like. Optional user interfaces may also include a USB interface, a card reader interface, and the like. The network interface may optionally include a standard wired interface, a wireless interface (such as a Wi-Fi interface), and the like.

[0100] Those skilled in the art will understand 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 component arrangements.

[0101] The memory may also include an operating system and a network communication component. The operating system is a program that manages the hardware and software resources of the physical device, supporting the execution of information processing programs and other software and / or programs. The network communication component is used to enable communication between components within the memory, as well as with other hardware and software in the physical information processing device.

[0102] Furthermore, based on the noise data processing method embodiment provided in this application, the noise data processing device embodiment provided in this application, and the above-mentioned electronic device embodiment, this embodiment also provides a vehicle, which may include the above-mentioned electronic device and further execute the noise data processing method provided in this application.

[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 corresponding to the previous time domain window and the noise data within the first step of the time domain window are combined with the noise data of the last step in the next time domain window to accurately infer 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 document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any 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 that includes 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 presence of other identical elements in the process, method, article or device that includes the elements.

[0105] The foregoing is merely a list of specific embodiments of the present application, intended to enable those skilled in the art to understand and implement the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments described herein, but is intended to conform to the broadest scope consistent with the principles and novel features of the present application. Industrial Applicability

[0106] The embodiment of the present application provides a noise data processing method, device, electronic device and vehicle, first 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 of the first time domain window, and determining the third noise data corresponding to the spectral component in the last step length of 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 method of calculating the data of each time domain window with overlapping data separately and completely, 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 within the first step length of the time domain window, and the noise data in the last step length of the latter 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 latter time domain window, thereby reducing the calculation of the overlapping data and saving computing power.

Claims

1. A noise data processing method, comprising: 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, wherein: 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, wherein: The step of performing a butterfly operation of a fast Fourier transform on the first time domain data to obtain the first noise data includes: 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, wherein: 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, wherein: 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, wherein: 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, wherein: 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, comprising: An acquisition part 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 part is configured to 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, wherein the second time domain window is a time domain window obtained by moving the first time domain window by the step; The processing part 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, wherein the computer program implements the method according to any one of claims 1 to 7 when executed by a processor.

10. An electronic device comprising: a memory configured to store a computer program; The processor is configured to implement the method according to any one of claims 1 to 7 when executing the computer program stored in the memory.

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

12. A computer program product, comprising computer executable instructions or a computer program, wherein the computer executable instructions or the computer program, when executed by a processor, implements the noise data processing method according to any one of claims 1 to 7.

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