CN111751658A - Signal processing method and device - Google Patents

Signal processing method and device Download PDF

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CN111751658A
CN111751658A CN202010595616.3A CN202010595616A CN111751658A CN 111751658 A CN111751658 A CN 111751658A CN 202010595616 A CN202010595616 A CN 202010595616A CN 111751658 A CN111751658 A CN 111751658A
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刘识
程志华
纪鑫
王宏刚
杨成月
赵晓龙
徐玲玲
邵进
余婷
武同心
崔琪
李建芳
杨志
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Big Data Center Of State Grid Corp Of China
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    • G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
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Abstract

The embodiment of the application provides a signal processing method and a signal processing device, wherein the method comprises the following steps: and processing according to the sampling signals to obtain noise variance estimation after noise reduction, and compressing and reconstructing signals according to the noise variance estimation after noise reduction and a set sampling sequence, wherein the set sampling sequence is a sequence of two-dimensional space signals. Compared with the prior art, the method provided by the application can effectively improve the data compression ratio and ensure the reconstruction efficiency.

Description

Signal processing method and device
Technical Field
The embodiment of the application relates to the field of power grid data processing, in particular to a signal processing method and device.
Background
In the field of power grids, mass data can be generated by adopting monitoring equipment with high sampling frequency to collect data in the transient state or fault process of the power grids, which brings challenges to data storage and rapid response. The solution currently adopted is to perform data compression. However, noise introduced by the sampling link can restrict the compression ratio of the power grid data. In addition, when the data sampling is carried out by utilizing the traditional Nyquist sampling mode, the sampling rate is required to be more than 2 times (generally 4-10 times) of the highest frequency of a signal, and the high-frequency power quality data acquisition, compression and storage need a complex and high-cost sampling front end, a high-performance processor and a storage medium. Moreover, when analyzing the acquired data, the compressed massive offline data needs to be reconstructed, which may occupy high-performance processor resources.
Disclosure of Invention
In order to solve at least one of the above technical problems, embodiments of the present application provide the following solutions.
In a first aspect, an embodiment of the present application further provides a signal processing method, where the method includes:
processing is carried out according to the sampling signal, and noise variance estimation after noise reduction is obtained;
performing signal compression and reconstruction according to the noise variance estimation after noise reduction and a set sampling sequence;
the set sampling sequence is a sequence of two-dimensional space signals.
In a second aspect, an embodiment of the present application further provides a signal processing apparatus, including:
the processing module is used for processing according to the sampling signal to obtain noise variance estimation after noise reduction;
the compression and reconstruction module is used for compressing and reconstructing signals according to the noise variance estimation after noise reduction and a set sampling sequence;
the set sampling sequence is a sequence of two-dimensional space signals.
The embodiment of the application provides a signal processing method and a signal processing device, wherein the method comprises the following steps: and processing according to the sampling signals to obtain noise variance estimation after noise reduction, and compressing and reconstructing signals according to the noise variance estimation after noise reduction and a set sampling sequence, wherein the set sampling sequence is a sequence of two-dimensional space signals. Compared with the prior art, the method provided by the application can effectively improve the data compression ratio and ensure the reconstruction efficiency.
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Fig. 1 is a flowchart of a signal processing method in an embodiment of the present application;
fig. 2 is a noise reduction process in an embodiment of the present application;
fig. 3 is a schematic structural diagram of a signal processing apparatus in an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be further noted that, for the convenience of description, only some of the structures related to the present application are shown in the drawings, not all of the structures.
In addition, in the embodiments of the present application, the words "optionally" or "exemplarily" are used for indicating as examples, illustrations or explanations. Any embodiment or design described herein as "optionally" or "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the words "optionally" or "exemplarily" etc. is intended to present the relevant concepts in a concrete fashion.
Fig. 1 is a flowchart of a signal processing method provided in an embodiment of the present application, where the method may be applied in the field of power grids for processing a high-frequency power signal to obtain a high compression ratio and a high-precision signal reconstruction efficiency. As shown in fig. 1, the method includes:
and S101, processing according to the sampling signal to obtain noise variance estimation after noise reduction.
In this step, the sampling signal may be a dynamic power signal sampled by a power grid, and the signal may include an original pure signal and a noise signal.
For example, assume that the original pure signal collected is x (t) and the noise signal is (t) (where the mean of the noise signal is 0 and the variance is σ)2) And f (t) is the sampling signal, the time domain formula (1) is satisfied among the three signals, as follows:
f(t)＝x(t)+(t) (1)
the equivalent discrete formula is:
f(i)＝x(i)+(i) (2)
for example, wavelet transform processing may be performed on the sampled signal to obtain noise variance estimation after noise reduction
And S102, compressing and reconstructing according to the noise variance estimation after noise reduction and the set sampling sequence.
The sampling sequence in this step may be a sequence of two-dimensional spatial signals, for example, the sequence is represented in the form of a matrix h (w), which may represent a transform relationship between the shift-invariant sampling space S and the signal space V in the frequency domain.
Therefore, when the one-dimensional signal is cut and converted into the two-dimensional space, because the amplitude of the sequence between cycles is stable in the row direction of the matrix, compared with the one-dimensional signal, the number of adjacent points of the two-dimensional space signal is changed from 2 to 8, the redundancy is higher, the correlation between the points of the space data can be removed, the redundancy of the cycle data is reduced, and the compression performance and the reconstruction quality are further improved in a mode of keeping the data information quantity in the cycle.
Optionally, in an embodiment, the implementation manner of step S101 may be that wavelet coefficient transformation is performed according to the sampled signal data, denoising processing is performed on the transformed wavelet coefficients, and a denoised noise variance estimate is obtained.
The wavelet coefficient after the noise reduction is processed according to a wavelet scale noise reduction data compression algorithm to obtain a wavelet coefficient after the noise reduction, the maximum likelihood estimation variance of the wavelet coefficient after the noise reduction is determined according to the wavelet coefficient after the noise reduction, the maximum likelihood estimation variance is subjected to the noise reduction, and the noise variance estimation after the noise reduction is obtained.
For example, after performing wavelet transform on the above formula (2), a regression model of the wavelet domain is obtained as follows:
Figure BDA0002555903830000041
wherein the content of the first and second substances,
Figure BDA0002555903830000042
the detail wavelet coefficients of f at scale m are represented,
Figure BDA0002555903830000043
representing the detail wavelet coefficients of x at scale m,
Figure BDA0002555903830000044
showing the detail wavelet coefficients at scale m.
Therefore, the noise reduction can be separately performed on the normally distributed white Gaussian noise and the non-Gaussian colored noise of the wavelet coefficient of each original layer of detail, and then the noise reduction processing is performed on the zero-mean white Gaussian noise by using the properties that the wavelet coefficient is not only related to the adjacent coefficient of the scale where the wavelet coefficient is located, but also related to the coefficient of the whole scale, and the processing process is shown in FIG. 2.
Since the wavelet coefficients can be modeled as independent gaussian random variables with zero mean values within an unknown interval of determined variance, the original pure signal wavelet coefficients
Figure BDA0002555903830000045
The Minimum Mean Square Error (MMSE) estimate of (a) is linear. Pure signal wavelet coefficient of supposing m scale
Figure BDA0002555903830000046
Variance of (2)
Figure BDA0002555903830000047
Known as then
Figure BDA0002555903830000048
The following formula is satisfied:
Figure BDA0002555903830000049
wherein the content of the first and second substances,
Figure BDA00025559038300000410
the wavelet coefficients of the original pure signal after the noise reduction processing.
Due to the fact that
Figure BDA00025559038300000411
Actually unknown, the variance of the maximum likelihood estimation can be used
Figure BDA00025559038300000412
Substitution
Figure BDA00025559038300000413
Then
Figure BDA0002555903830000051
Can be expressed as:
Figure BDA0002555903830000052
since the maximum likelihood estimation relies on statistics of locally acquired signal data, the estimation of the variance for each wavelet coefficient is based on the data points of the neighborhood, i.e., the values in the neighborhood range. Therefore, in a small-range neighborhood, the wavelet coefficients of the local data in the neighborhood range can be regarded as the same variance, and then the ith wavelet coefficient of m layers can be obtained
Figure BDA0002555903830000053
The maximum likelihood estimation variance of (a) is:
Figure BDA0002555903830000054
wherein, in the above formula
Figure BDA0002555903830000055
Represents a mean value of zero and a variance of
Figure BDA0002555903830000056
Is a Gaussian distribution, ^iRepresenting wavelet coefficients
Figure BDA0002555903830000057
L length neighborhood, σ2Is the Median Absolute Deviation (MAD) used to represent the noise variance, σ, of each scale2Specifically, the following are shown:
σ2＝(MAD/η)2(7)
the MAD in the above formula represents the average absolute value of the current m-scale wavelet coefficient, and eta is a constant of 0.6745, which can make the MAD estimate in a normal distribution range.
Since the interference signal in the power signal has wavelet coefficients with larger amplitude at a finer scale, which is usually determined by the significant parent elements of the coarse scale layer, and the wavelet coefficients of the noise are smaller when the scale is larger at the time of multi-scale decomposition, the maximum likelihood estimation variance can be denoised, for example, by:
Figure BDA0002555903830000058
t in the above formula is the minimum noise value in a certain neighborhood, and the value range of T is more than or equal to 0.
In an embodiment, the signal compression and reconstruction in step S102 may be implemented by constructing an electrical energy signal according to the noise variance estimation after noise reduction and a set sampling sequence, establishing a total variation minimization model according to the electrical energy signal, processing the total variation minimization model to obtain an optimized total variation minimization model, and obtaining a reconstructed signal according to the optimized total variation minimization model.
For example, assume that the element in the sample sequence H (w) can be hl，r(w), which may be specifically represented by the following formula:
Figure BDA0002555903830000061
where R ∈ R, L ∈ L, R denotes the dimension of the translation invariant signal space V, and L denotes the dimension of the sampling space S.
The constructed power signal Y can then be expressed as:
Y＝Hx+ (10)
wherein H is a measurement matrix of dimension R x L, H [ H ]1(t)，H2(t)，......，HR(t)]And t is 1, 2, … …, N, which is the noise variance after noise reduction.
It is understood that if R ≦ L, then this indicates that the oversampling mode is currently in use; if R is larger than L, the current work is in the undersampling mode. If the signal has certain sparsity, the signal reconstruction can be realized by obtaining a unique solution of an equation set.
In the embodiment of the application, a one-dimensional signal is converted into a two-dimensional space signal by truncation, linear singular characteristics can be presented in the horizontal direction, the longitudinal direction and the diagonal direction, the Total Variation (TV) solution for reconstruction by using the directional singular characteristics of an image technology is met, and the sparsity of the signal is not required to be known in advance, so that when signal compression and reconstruction are performed based on noise variance estimation after noise reduction and a set sequence of the two-dimensional space signal, the data compression ratio can be improved, and the reconstruction efficiency is ensured.
Illustratively, the full variation minimization model established according to the electric energy signal may be:
Figure BDA0002555903830000062
in the above formula, phi represents a measurement matrix, psi represents a sparse basis, η represents coefficients after sparse basis transformation, lambda represents a regularization parameter, and OTVRepresenting the total variation operator, i.e. the sum of the discrete gradients of the image.
Further, in the implementation manner, the obtaining of the optimized total variation minimization model may be implemented by processing a total variation operator in the total variation minimization model to obtain an updated total variation minimization model, and then modifying the updated total variation minimization model to obtain the optimized total variation minimization model.
The method for processing the total variation operator in the total variation minimization model can be to perform minimization optimization on the total variation operator and optimize the coefficient of the total variation operator.
For example, the total variation operator is optimized in the minimum by the formula (12), and the coefficient of the total variation operator is optimized by the formula (13).
Figure BDA0002555903830000071
In the above formula Q (x)i,jRepresenting the total variation of pixel Xi, j, | Q (X)i,jThe function W of | isW＝g(Q(x))。
Figure BDA0002555903830000072
Wherein, A is a regular term used for protecting the full variation value from becoming smaller, and B is a fidelity term used for keeping the optimization weight consistent with the pre-estimation value.
In the total variation minimization model, the quality of image reconstruction can be partially improved aiming at an enhanced sparsity description, and the problem essence of sparsity can be enhanced by determining a proper weight W to reflect the characteristics of an image. For a two-dimensional image of a signal, in an abrupt signal change region, it is necessary that the weight is as small as possible, and in a region where the smooth abrupt change of the image is small, it is necessary that a larger weight is given. When the weighted variation model is solved iteratively, each solution has a relation with the historical solution, and the analysis is caused by signal noise, so that the full variation value of the weight map is larger than the actual value. Therefore, by the above optimization method, an updated total variation minimization model can be obtained. The model is shown as follows:
Figure BDA0002555903830000081
since the nonlinear conjugate gradient descent method and the linear backtracking method are used for solving in the above minimization model, it is still possible to generate a non-globally convergent point column. Therefore, the model can be modified to obtain an optimized full variation minimization model.
Illustratively, a modified PRP conjugate gradient method may be employed, i.e., solving for min { Y | x ∈ RnWhen the method is applied, the iterative formula can be as follows:
xk+1＝xk+tkdk(15)
wherein, tkDenotes the step size, dkIndicates a search direction, wherein dkCan be determined in the following manner in the prior art.
Figure BDA0002555903830000082
The parameters β in the above formula are modified according to the general PRP formula, equation (17)kTo avoid the continuous generation of small steps and improve the numerical calculation efficiency and global convergence, as shown in equation (18).
Figure BDA0002555903830000083
Figure BDA0002555903830000084
Wherein, γkAnd a history value gkCorrelation, gkA filter impulse response sequence corresponding to a wavelet function, and gammakEquation (19) is satisfied.
Figure BDA0002555903830000085
After the optimized total variation minimization model is obtained through the above manner, further, in step S102, a manner of reconstructing a signal according to the optimized total variation minimization model may be that a reconstructed signal is determined according to a sparse basis and a coefficient after sparse transformation in the optimized total variation minimization model.
For example, a two-dimensional reconstructed image X of the electric energy signal is determined by the formula (20).
X＝Ψη (20)
Fig. 3 is a signal processing apparatus according to an embodiment of the present application, which may be used to process a high-frequency power signal in a power grid field to obtain a high compression ratio and a high-precision signal reconstruction efficiency. As shown in fig. 3, the apparatus includes: a processing module 301 and a compression reconstruction module 302;
the processing module is used for processing according to the sampling signal to obtain noise variance estimation after noise reduction;
the compression and reconstruction module is used for compressing and reconstructing signals according to the noise variance estimation after noise reduction and a set sampling sequence;
the set sampling sequence is a sequence of two-dimensional space signals.
In one example, the processing module is configured to perform wavelet coefficient transformation on the sampled signal data and perform denoising processing on the transformed wavelet coefficients to obtain a denoised noise variance estimate.
Optionally, the processing module may process the transformed wavelet coefficient according to a wavelet scale denoising data compression algorithm to obtain a denoised wavelet coefficient, determine a maximum likelihood estimation variance of the denoised wavelet coefficient according to the denoised wavelet coefficient, and perform denoising processing on the maximum likelihood estimation variance to obtain a denoised noise variance estimation.
In one example, the compression and reconstruction module may include a construction unit and a processing unit;
the device comprises a construction unit, a data processing unit and a data processing unit, wherein the construction unit is used for constructing an electric energy signal according to noise variance estimation after noise reduction and a set sampling sequence, and establishing a total variation minimization model according to the electric energy signal;
and the processing unit is used for processing the total variation minimization model to obtain an optimized total variation minimization model and obtaining a reconstructed signal according to the optimized total variation minimization model.
Optionally, the processing unit may be configured to process a total variation operator in the total variation minimization model to obtain an updated total variation minimization model, and modify the updated total variation minimization model to obtain an optimized total variation minimization model.
More specifically, the processing unit may perform minimization optimization on the total variation operator, optimize a coefficient of the total variation operator, and obtain an updated total variation minimization model according to the optimized total variation operator and the coefficient of the total variation operator.
Optionally, the processing unit may further determine the reconstructed signal according to the sparse basis in the optimized full-variational minimization model and the sparsely transformed coefficient.
The signal processing device can realize the signal processing method provided by fig. 1, and has corresponding devices and beneficial effects in the method.
From the above description of the embodiments, it is obvious for those skilled in the art that the present application can be implemented by software and necessary general hardware, and certainly can be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) to implement the functions described in the embodiments of the present application.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present application and the technical principles employed. It will be understood by those skilled in the art that the present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the application. Therefore, although the present application has been described in more detail with reference to the above embodiments, the present application is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present application, and the scope of the present application is determined by the scope of the appended claims.

Claims (10)

1. A signal processing method, comprising:
processing is carried out according to the sampling signal, and noise variance estimation after noise reduction is obtained;
performing signal compression and reconstruction according to the noise variance estimation after noise reduction and a set sampling sequence;
wherein the set sampling sequence is a sequence of two-dimensional space signals.
2. The method of claim 1, wherein the processing from the sampled signal to obtain a noise variance estimate after noise reduction comprises:
performing wavelet coefficient transformation according to the sampled signal data;
and denoising the transformed wavelet coefficient to obtain denoised noise variance estimation.
3. The method according to claim 2, wherein the denoising the transformed wavelet coefficients to obtain a denoised noise variance estimate comprises:
processing the transformed wavelet coefficient according to a wavelet scale denoising data compression algorithm to obtain a denoised wavelet coefficient;
determining the maximum likelihood estimation variance of the denoised wavelet coefficient according to the denoised wavelet coefficient;
and carrying out noise reduction processing on the maximum likelihood estimation variance to obtain noise variance estimation after noise reduction.
4. The method according to any one of claims 1-3, wherein the performing signal compression and reconstruction according to the noise variance estimation after noise reduction and the set sampling sequence comprises:
constructing an electric energy signal according to the noise variance estimation after noise reduction and a set sampling sequence;
establishing a total variation minimization model according to the electric energy signal;
processing the total variation minimization model to obtain an optimized total variation minimization model;
and obtaining a reconstructed signal according to the optimized total variation minimization model.
5. The method of claim 4, wherein the processing the fully-variational minimization model to obtain an optimized fully-variational minimization model comprises:
processing the total variation operator in the total variation minimization model to obtain an updated total variation minimization model;
and correcting the updated total variation minimization model to obtain an optimized total variation minimization model.
6. The method of claim 5, wherein the processing the total variation operators in the total variation minimization model to obtain an updated total variation minimization model comprises:
performing minimization optimization on the total variation operator;
optimizing the coefficient of the total variation operator;
and obtaining an updated total variation minimization model according to the optimized total variation operator and the coefficient of the total variation operator.
7. The method of claim 4, wherein deriving the reconstructed signal according to the optimized full-variational minimization model comprises:
and determining a reconstructed signal according to the sparse basis in the optimized full-variational minimization model and the coefficient after sparse transformation.
8. A signal processing apparatus, characterized by comprising:
the processing module is used for processing according to the sampling signal to obtain noise variance estimation after noise reduction;
the compression and reconstruction module is used for compressing and reconstructing signals according to the noise variance estimation after noise reduction and a set sampling sequence;
wherein the set sampling sequence is a sequence of two-dimensional space signals.
9. The apparatus of claim 8, wherein the processing module is configured to perform wavelet coefficient transformation on the sampled signal data and perform denoising on the transformed wavelet coefficients to obtain a denoised noise variance estimate.
10. The apparatus according to claim 8 or 9, wherein the compression reconstruction module comprises a construction unit and a processing unit;
the construction unit is used for constructing an electric energy signal according to the noise variance estimation after noise reduction and a set sampling sequence, and establishing a total variation minimization model according to the electric energy signal;
and the processing unit is used for processing the total variation minimization model to obtain an optimized total variation minimization model and obtaining a reconstructed signal according to the optimized total variation minimization model.
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