CN113742985B - Processing method and device for ecosystem carbon flux measurement signals and electronic equipment - Google Patents
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Abstract
The application relates to the technical field of carbon circulation research, in particular to a processing method and device of an ecosystem carbon flux measurement signal and electronic equipment. The method comprises the following steps: performing singular value decomposition denoising on the ecosystem carbon flux measurement signal to obtain a first signal subjected to preliminary denoising treatment; performing empirical mode decomposition on the first signal to obtain a plurality of linear steady-state signals included in the first signal, and determining one of the linear steady-state signals as a signal to be processed; and performing filtering noise reduction processing based on a particle swarm algorithm on the signal to be processed to obtain a processed signal. According to the application, singular value decomposition denoising, secondary denoising based on empirical mode decomposition and filtering denoising processing based on a particle swarm algorithm are sequentially carried out on the ecosystem carbon flux measurement signals, so that the processing of the ecosystem carbon flux measurement signals is realized, and the accuracy of the processing result of the ecosystem carbon flux measurement signals containing complex noise signals is ensured.
Description
Technical Field
The invention relates to the technical field of carbon circulation research, in particular to a processing method and device of an ecosystem carbon flux measurement signal and electronic equipment.
Background
Carbon flux (Carbon flux) is one of the most basic concepts in Carbon recycling research, and represents the total amount of Carbon elements of an ecosystem passing through a certain ecological section. The estimation of the carbon flux of the ecosystem is a key technical link for researching carbon circulation and carbon balance in the global change background, and it is to be noted that the research of the carbon flux of the ecosystem is generally realized by researching carbon element related gas of the ecosystem, and is exemplified by carbon dioxide gas participating in the breathing of the ecosystem, and industrial gases such as methane, carbon monoxide and the like participating in the circulation of the ecosystem.
The measurement of the carbon flux of the ecosystem is usually carried out outdoors, the measurement signal is influenced by a plurality of outdoor factors, more uncertain noise is contained, and the problem to be solved is urgently needed in the research of the carbon flux of the ecosystem when the measurement signal of the quantity of the ecosystem is accurately processed.
Disclosure of Invention
The embodiment of the application aims to provide a processing method and device for an ecosystem carbon flux measurement signal and electronic equipment, so as to solve the defects in the prior art.
To achieve the above objective, the embodiment of the present invention discloses a method for processing an ecosystem carbon flux measurement signal, comprising:
performing singular value decomposition denoising on the ecosystem carbon flux measurement signal to obtain a first signal subjected to preliminary denoising treatment;
Performing empirical mode decomposition on the first signal to obtain a plurality of linear steady state signals included in the first signal, and determining one of the linear steady state signals as a signal to be processed;
And performing filtering noise reduction processing based on a particle swarm algorithm on the signal to be processed to obtain a processed signal.
Optionally, the performing singular value decomposition denoising on the ecosystem carbon flux measurement signal to obtain a first signal subjected to preliminary denoising treatment includes:
constructing a singular value matrix of the carbon flux measurement signal of the ecological system through a singular value decomposition algorithm;
And obtaining a first signal of preliminary noise reduction processing according to diagonal elements of the singular value matrix.
Optionally, the obtaining the first signal of the preliminary noise reduction processing according to the diagonal element of the singular value matrix includes:
summing singular value elements on diagonal lines of the singular value matrix to obtain a singular value sum;
Obtaining singular value entropy corresponding to each singular value element on a diagonal of the singular value matrix based on the singular value sum;
and obtaining a first signal of the preliminary noise reduction processing according to each singular value entropy.
Optionally, the obtaining the first signal of the preliminary noise reduction processing according to each singular value entropy includes:
obtaining singular value entropy quotient corresponding to each two adjacent singular value elements based on the singular value entropy and all the two adjacent singular value elements on the diagonal of the singular value matrix;
Acquiring singular value elements which keep changing of all the singular value entropy quotient as target singular value elements;
determining a decomposition signal of the ecosystem carbon flux measurement signal corresponding to the target singular value element as a first sub-signal,
A first signal of a preliminary noise reduction process is obtained based on the first sub-signal.
Optionally, the performing empirical mode decomposition on the first signal to obtain a plurality of linear steady-state signals included in the first signal, and determining one of the linear steady-state signals as a signal to be processed includes:
decomposing the first signal into a plurality of linear steady-state signals and a residual component based on an empirical mode decomposition algorithm;
selecting a reconstruction signal reflecting the most local features of the signal and the residual component as a signal to be processed, wherein: the calculation of the representation factor of the signal local feature is as follows:
C＝(1-DE)+r
wherein: c is a representation factor of a signal local feature, DE is a dispersion entropy value of the steady-state signal, and r is a degree of correlation of the residual component and the first signal.
Optionally, the filtering noise reduction processing based on the particle swarm algorithm is performed on the signal to be processed to obtain a processed signal, which includes:
The filter order and the filter window in the SG filter algorithm are used as optimization objects of the particle swarm algorithm, and the signal-to-noise ratio and root mean square error of the filtered signal relative to the signal before filtering are used as construction basis of the fitness function of the particle swarm algorithm to optimize the SG filter algorithm, so as to obtain an improved filter algorithm;
And filtering and denoising the signal to be processed based on the improved filtering algorithm to obtain a processed signal.
Optionally, the optimizing the SG filtering algorithm by using a filtering order and a filtering window in the SG filtering algorithm as an optimization object of the particle swarm algorithm and using a signal-to-noise ratio and a root mean square error of a filtered signal relative to a signal before filtering as a construction basis of an fitness function of the particle swarm algorithm includes:
The filter order and the filter window in the SG filter algorithm are used as optimization objects for improving the particle swarm algorithm, and the signal-to-noise ratio and root mean square error of the filtered signal relative to the signal before the filter are used as construction basis for improving the fitness function of the particle swarm algorithm to optimize the SG filter algorithm; wherein: the improved particle swarm algorithm includes an inertial weight and a learning factor that improve the particle swarm algorithm.
Based on the same conception, another embodiment of the present application further provides a processing device for an ecosystem carbon flux measurement signal, including:
the preliminary noise reduction device is configured to perform singular value decomposition denoising on the ecosystem carbon flux measurement signal to obtain a first signal subjected to preliminary noise reduction treatment;
the signal processing device is configured to perform empirical mode decomposition on the first signal to obtain a plurality of linear steady-state signals included in the first signal, and determine one of the linear steady-state signals as a signal to be processed;
And the filtering and noise reduction device is configured to perform filtering and noise reduction processing based on a particle swarm algorithm on the signal to be processed to obtain a processed signal.
Based on the same conception, another embodiment of the present application also provides an electronic device, including: a processor;
A processor;
a memory for storing processor-executable instructions;
Wherein the processor implements the method of any of the above by executing the executable instructions.
Based on the same conception, another embodiment of the present application also provides a computer readable storage medium having stored thereon computer instructions, characterized in that the instructions when executed by a processor implement the steps of the method according to any of the above.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the drawings without giving inventive effort to those skilled in the art.
FIG. 1 is a schematic flow chart of a method for processing an ecosystem carbon flux measurement signal according to an embodiment of the present application;
FIG. 2 is a graph showing the results of singular value decomposition of an ecosystem carbon flux measurement signal according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a filtering result based on a standard PSO and a schematic diagram of a filtering result based on an IPSO (i.e. modified PSO algorithm) algorithm of an ecosystem carbon flux measurement signal according to an embodiment of the present application;
FIG. 4 is a schematic flow diagram of a method of treating carbon flux of seed respiring carbon dioxide in an ecosystem in accordance with one embodiment of the present application;
FIG. 5 is a schematic diagram of a processing device for measuring a carbon flux of an ecosystem according to an embodiment of the present application;
FIG. 6 is a schematic diagram of an electronic device for implementing a processing apparatus for an ecosystem carbon measurement signal according to an embodiment of the present application;
Detailed Description
The following description of the embodiments of the present invention will be made with reference to the accompanying drawings, in which it is evident that the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 is a schematic flow chart of a processing method of an ecosystem carbon flux measurement signal according to an embodiment of the present application, as shown in fig. 1, where the processing method of an ecosystem carbon flux measurement signal according to an embodiment of the present application includes:
step S1, performing singular value decomposition denoising on an ecosystem carbon flux measurement signal to obtain a first signal subjected to preliminary denoising treatment;
step S2, performing empirical mode decomposition on the first signal to obtain a plurality of linear steady-state signals included in the first signal, and determining one of the linear steady-state signals as a signal to be processed;
and step S3, filtering and noise reduction processing based on a particle swarm algorithm is carried out on the signal to be processed, and a processed signal is obtained.
According to the method, singular value decomposition denoising, secondary denoising based on empirical mode decomposition and filtering denoising processing based on a particle swarm algorithm are sequentially carried out on the ecosystem carbon flux measurement signals through the steps S1 to S3, so that the processing of the ecosystem carbon flux measurement signals is realized, and the accuracy of the processing result of the ecosystem carbon flux measurement signals containing complex noise signals is ensured.
Optionally, in step S1, performing singular value decomposition denoising on the ecosystem carbon flux measurement signal to obtain a first signal subjected to preliminary denoising processing, where the denoising process includes:
S11, constructing an odd-value matrix of the ecosystem carbon flux measurement signal through a singular value decomposition algorithm;
Specifically, the principle of the singular value decomposition algorithm is that for a noisy signal a (n), it can be described as a noise-free signal b (n) and a noise signal c (n), as shown in the following formula:
a(n)＝b(n)+c(n)；n＝1,2,...,N
The noisy signal may be constructed as a Hankel matrix Aa of dimension e x k:
wherein 1< k < N, N is the number of elements in a (N), and e+k=n+1.
For matrixThere is an orthogonal matrix U in the e x m dimension and an orthogonal matrix V in the k x m dimension such that Aa can perform singular value decomposition such that aa=uΣvt, where Σ is a non-negative diagonal matrix of m x m, and Σ can be expressed as:
Where p=diag (σ1, σ2, …, σk), where σ1 Σ2 Σ … Σk … >0.σ1, σ2, …, σk are diagonal singular values of matrix a a.
For Hankel matrix a a, its singular value decomposition can be expressed as:
equivalent to
Wherein Σ x and Σh are singular value diagonal matrices generated by signals and noise respectively, so that a singular value decomposition algorithm can complete the division of an effective part and a noise part in a noise-containing signal, and singular value elements p=diag (sigma 1, sigma 2, …, sigma k) on the diagonal of the singular value matrix correspond to decomposition signals for decomposing the ecological system carbon flux measurement signal through the singular value decomposition algorithm.
And step S12, obtaining a first signal of the preliminary noise reduction processing according to diagonal elements of the singular value matrix.
It should be noted that, obtaining the first signal of the preliminary noise reduction processing according to the diagonal element of the singular value matrix may be achieved by:
summing singular value elements on diagonal lines of the singular value matrix to obtain a singular value sum; obtaining singular value entropy corresponding to each singular value element on a diagonal of the singular value matrix based on the singular value and the singular value entropy; and obtaining a first signal of the preliminary noise reduction processing according to each singular value entropy.
In particular, the method comprises the steps of,
Wherein S (i, i) is a singular value element on a diagonal of the singular value matrix,Is singular value entropy.
It should be noted that the singular value reflects the intrinsic component of the sub-decomposition signal, and the singular value entropy quotient represents the variation of the singular value; FIG. 2 is a graph showing the results of singular value decomposition of an ecosystem carbon flux measurement signal according to an embodiment of the present application; as in fig. 2, from the seventh singular value, the singular values are substantially the same, all by a small amount; the corresponding singular value entropy quotient remains substantially unchanged, thus employing the first 7 singular values as the effective signal reconstruction signal. Thus, optionally, obtaining the first signal of the preliminary noise reduction process according to each singular value entropy includes: obtaining singular value entropy quotient corresponding to each two adjacent singular value elements based on the singular value entropy and all the two adjacent singular value elements on the diagonal of the singular value matrix; acquiring singular value elements which keep changing of all the singular value entropy quotient as target singular value elements; determining a decomposition signal of the ecosystem carbon flux measurement signal corresponding to the target singular value element as a first sub-signal, and obtaining a first signal of preliminary noise reduction processing based on the first sub-signal.
The above process is a process of determining the first signal of the preliminary noise reduction process according to the change rule of all the singular value entropy quotient, and it is to be noted that any singular value entropy quotient reflects the magnitude of two adjacent singular values, all the change rule of the singular value entropy quotient reflects the change of all the singular values, the singular value entropy quotient is relative to the singular value entropy, the sub-decomposition signal corresponding to the singular value where the change of the singular value entropy quotient is basically kept unchanged is an invalid signal, the sub-decomposition signal corresponding to the singular value where the change of the singular value entropy quotient is kept changed and linked on the diagonal is an effective signal, that is, the first sub-signal can be used for reconstructing the first signal of the preliminary noise reduction process.
Optionally, the performing empirical mode decomposition on the first signal in the step S2 to obtain a plurality of linear steady-state signals included in the first signal, and determining one of the linear steady-state signals as a signal to be processed includes:
step S21, decomposing the first signal into a plurality of linear steady-state signals and a residual component based on an empirical mode decomposition algorithm;
In particular, the empirical mode decomposition technique is to decompose an arbitrary signal (especially a non-stationary nonlinear time series signal) into a linear steady state signal (IMF); the core is that any free signal is decomposed into a plurality of inherent mode functions (IntrinsicModeFunction, IMF) and a residual component, each IMF represents the oscillation change of different frequency segments of the original signal and reflects the local characteristics of the signal, and the final residual component reflects the slow change in the signal, and the specific operation process is as follows:
(1) Connecting all local extreme points of x (t) of an original signal by adopting different upper and lower spline curves respectively, so that all data points of the signal are between the two envelope curves, and defining a sequence formed by upper and lower envelope curve average values as m (t);
(2) Subtracting m (t) from the original signal x (t)
h1(t)＝x(t)－m(t)
Whether h1 (t) meets two requirements of IMF is detected, and if so, h1 (t) is taken as an eigenmode component c1 (t) and re-extraction is not met.
It should be noted that, the two requirements of IMF refer to that one IMF with realistic physical meaning and high reliability should satisfy the following two conditions: 1) In the whole extremum data, the number of extremum must be equal to the number of zero crossing points or at most only one extremum can be different; 2) At any point in time, the local mean envelope defined by the maximum envelope and the minimum envelope has a value of 0.
(3) After obtaining an eigen-mode component, c1 (t) is subtracted from the original signal x (t) to obtain a residual sequence r1 (t)
r1(t)＝x(t)－c1(t)
(4) Repeating steps (1) - (3) with r1 (t) as a new original signal to obtain IMF components of each order, denoted as c1 (t), c2 (t), cn (t), stopping until a preset stopping criterion is met, and finally leaving the next remainder r n (t).
The method is centered on decomposing the original signal x (t) into a number of eigen-mode components ci (t) and a residual r n (t), as shown in
The reconstructed signal reflecting the IMF and residual components with the largest local features of the signal can be selected as the signal to be processed. It should be noted that the signal to be processed is still a noise-containing signal.
Step S22, selecting a reconstruction signal reflecting the IMF and residual components with the largest local characteristics of the signal as a signal to be processed, wherein: the calculation of the representation factor of the signal local feature is as follows:
C＝(1-DE)+r
wherein: c is a representation factor of a signal local feature, DE is a dispersion entropy value of the steady-state signal, and r is a degree of correlation of the residual component and the first signal.
The above process is a process of determining the noise-containing portion of the eigenmode component IMF. For the determination of the noisy portion of the eigen-mode component IMF, there are various methods, such as energy entropy, permutation entropy, correlation coefficient, etc., and herein, the joint parameters of both scatter entropy and correlation coefficient are used as evaluation criteria for efficient signal reconstruction.
The dispersion entropy (dispersion entropy, DE) is a method proposed by ROSTAGHI and the like for detecting the dynamic change or irregularity degree of the time sequence, the calculation speed is high, the robustness is strong, the relation between the amplitude values is considered, the larger the dispersion entropy is, the higher the irregularity degree is, the smaller the dispersion entropy is, the lower the irregularity degree is, and therefore, the more stable the IMF signal is, the smaller the dispersion entropy is. During the decomposition of EMD, some extraneous information component, i.e., spurious components, is generated due to the need to manually set the number of iterations and termination conditions. For this problem, correlation coefficients may be utilized to identify spurious components. The dispersion entropy DE is inversely related to the effective component of the original signal, and the correlation coefficient r is positively related to the linear correlation degree of the original signal. The following IMF component screening indexes are adopted, and the IMF signal with the largest coefficient is taken for signal reconstruction:
C＝(1-DE)+r。
Wherein: the specific calculation of the scatter entropy DE and the correlation coefficient r is well known to those skilled in the art and will not be described in detail here.
When the embodiment is implemented, the SG filtering algorithm can be adopted to carry out smoothing processing on the signal to be processed, so as to obtain an accurate signal. In consideration of the defect of limited filtering effect of the SG filtering algorithm, the particle swarm algorithm can be adopted to optimize the size of a filtering window and the filtering order of the SG filtering algorithm. Therefore, the smoothing processing of the signal to be processed in step S3 of this embodiment to obtain an accurate signal specifically includes:
Step S31, optimizing the SG filter algorithm by taking the filter order and the filter window in the SG filter algorithm as optimization objects of the particle swarm algorithm and taking the signal-to-noise ratio and root mean square error of the filtered signal relative to the signal before the filter as construction basis of the fitness function of the particle swarm algorithm to obtain an improved filter algorithm;
And step S32, filtering and denoising the signal to be processed based on the improved filtering algorithm to obtain a processed signal.
Optionally, in step S31, the filtering order and the filtering window in the SG filtering algorithm are used as an optimization object of the particle swarm algorithm, and the SG filtering algorithm is optimized by using the signal-to-noise ratio and root mean square error of the filtered signal relative to the signal before filtering as the construction basis of the fitness function of the particle swarm algorithm, so as to obtain an improved filtering algorithm, which may also be:
And optimizing the SG filtering algorithm by taking the filtering order and the filtering window in the SG filtering algorithm as optimization objects of the particle swarm algorithm and taking the signal-to-noise ratio and root mean square error of the filtered signal relative to the signal before filtering as construction basis of the fitness function of the improved particle swarm algorithm to obtain the improved filtering algorithm. Wherein: the improved particle swarm algorithm mainly comprises inertia weight and learning factors of the improved particle swarm algorithm.
Specifically, the original Particle Swarm Optimization (PSO) algorithm is to find the optimal solution through collaboration and information sharing between the population and the individual. The expression is
vi+1＝ωvi+c1r(pbesti-xi)+c2r(gbesti-xi)
xi+1＝xi+vi
Wherein i ε M, M is the total number of particles; omega is the inertial weight; v i is the velocity of the particles; r is a random number between (0, 1); x i is the current position of the particle; x i+1 particles are the next time position; c 1、c2 is a learning factor; pbesti and gbesti are the self-and population-awareness values, respectively, of each iteration evolution.
The value of the inertia weight omega is a fixed value, and is usually set to 0.9, and the value of the global search c 1、c2 is usually set to 2, so as to adjust the searching speed of the best position of the particle and the moving speed towards the global best position.
The inventor finds that the fixed omega and c 1、c2 values are often unfavorable for rapid convergence of particles, so that the PSO algorithm is improved, and the improved PSO algorithm (Improvingparticle swarm optimization, IPSO) is provided, which is specifically as follows:
(1) The inertia weight is improved, and the improvement method comprises the following steps:
dynamically adjusting the value of w, wherein the maximum value of wmax is set to 0.9, the minimum value wmin of w is set to 0.4, and the value of w (i) is formed by the following formula.
w(i)＝wmax-(wmax-wmin)*atan(3*pi*i/n)/1.5
W (i) is the inertial weight at different search times.
(2) Improved learning factor
The self learning factor and the global learning factor are improved, and the improvement method is shown as the following formula:
c1(i)＝c1max-(c1max-c1min)*atan(2*pi*i/n)/1.5
c2(i)＝c2min+(c1max-c1min)*atan(2*pi*i/n)/1.5
Taking the order and window size in the SG (savitzky-golay) algorithm as optimization objects, taking the (1/SNR+RMSE) value of the filtered signal and the original signal as a fitness function, wherein SNR is the signal-to-noise ratio of the filtered signal relative to the signal before filtering, RMSE is the signal-to-noise ratio of the filtered signal relative to the signal before filtering, comparing a standard PSO algorithm with an IPSO algorithm, and FIG. 3 is a schematic diagram of a filtering result of an ecosystem carbon flux measurement signal based on the standard PSO algorithm and a schematic diagram of a filtering result of an algorithm based on the IPSO (i.e. an improved PSO algorithm) provided by the embodiment of the application, and the result is shown in FIG. 3. As can be seen from fig. 3, the improved PSO algorithm, which has a faster convergence speed and a smaller fitness value than the standard PSO algorithm, can be used for fast filtering by the improved PSO adaptive SG (Improvingparticle swarm optimization Adaptive Savitzky-Golay, IPSOASG). The embodiment of the application provides a processing method of an ecosystem carbon flux measurement signal, which can ensure the accuracy of the processing result of the ecosystem carbon flux measurement signal.
It should be noted that, in the implementation of the above embodiment, in the singular value decomposition, the initial dimension of the Hankel matrix needs to be set according to the data size of the carbon flux measurement signal, so as to ensure the processing effect of the data.
Fig. 4 is a schematic flow chart of a treatment mode of carbon flux of carbon dioxide breathed by seeds in an ecosystem according to an embodiment of the present application, and as shown in fig. 4, a treatment method of carbon flux of carbon dioxide breathed by seeds in an ecosystem includes:
Step S1, starting;
S2, preprocessing data;
seed respiration data can be acquired within 8 hours, and 2000 sample points can be acquired as preprocessed data;
S3, constructing a Hankel matrix for the preprocessed data, and completing singular value decomposition of the preprocessed data; and completing the reconstruction of the signals according to the decomposition result to obtain a first signal subjected to preliminary noise reduction.
Specific: an initial dimension of 100 rows for constructing a Hankel matrix can be set, the singular value decomposition of the preprocessed data is completed, and a signal for reconstruction is determined according to a singular value entropy quotient, wherein: the number of signals for reconstruction is a first value, illustratively 1000 rows;
The current signal-to-noise ratio of the reconstructed signal is SRmax, and then whether the current signal-to-noise ratio is SRmax is larger than a second threshold value Sri or not is judged;
If the number of the signals is smaller than the preset value, the number of the signals for reconstruction is increased, and for example, 100 lines are increased each time, the step of calculating the current signal-to-noise ratio of the signals obtained by reconstruction to be SRmax and then judging whether the current signal-to-noise ratio is SRmax which is larger than the size of a second threshold value Sri is executed again;
If the number of the lines of the maximum signal-to-noise ratio is larger than the number of the lines of the maximum signal-to-noise ratio, the singular value denoising is completed, and a reconstructed signal, namely a first signal subjected to preliminary denoising treatment, is obtained; step S4, performing empirical mode decomposition (i.e. EMD decomposition) on the first signal subjected to the preliminary noise reduction processing to obtain a plurality of linear steady-state signals (IMF signals) included in the first signal, solving a dispersion entropy NDEi and a correlation coefficient ri of each IMF signal, and obtaining a representation factor of a local feature of the signal according to a formula ci= (1-NDEi) +ri, and obtaining first 4 larger IMFs and residual values (i.e. residual components) of Ci to reconstruct the signal as a signal to be processed.
And S5, optimizing the order and window size parameters of the SG filter based on the improved PSO algorithm, finishing the filtering of the reconstructed signal (i.e. the signal to be processed), and outputting a filtering result, so as to finish.
It should be noted that the above specific arrangement is only a specific arrangement of the present embodiment, and does not represent the protective idea of the present example, and those skilled in the art can make the required arrangement under the idea.
Based on the same concept, as shown in fig. 5, still another embodiment of the present application provides a processing apparatus of an ecosystem carbon flux measurement signal, including:
The preliminary noise reduction device 501 is configured to perform singular value decomposition denoising on the ecosystem carbon flux measurement signal to obtain a first signal subjected to preliminary noise reduction;
A signal processing device 502 configured to perform empirical mode decomposition on the first signal to obtain a plurality of linear steady-state signals included in the first signal, and determine one of the linear steady-state signals as a signal to be processed;
And the filtering noise reduction device 503 is configured to perform filtering noise reduction processing based on a particle swarm algorithm on the signal to be processed to obtain a processed signal.
An embodiment of the present application further provides an electronic device, as shown in fig. 6, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to implement the method of any of the method embodiments described above by executing the executable instructions.
A further embodiment of the application provides a computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the steps of the method of any of the method embodiments described above.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having some function. A typical implementation device is a computer, which may be in the form of a personal computer, laptop computer, cellular telephone, camera phone, smart phone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or a combination of any of these devices.
In a typical configuration, a computer includes one or more processors (CPUs), input/output interfaces, a network interface, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may be implemented in any method or technology for information storage. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic disk storage, quantum memory, graphene-based storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing describes certain embodiments of the present application. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The terminology used in one or more embodiments of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of one or more embodiments of the application. As used in one or more embodiments of the application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items. It should be understood that while the terms first, second, third, etc. may be used in one or more embodiments of the application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of one or more embodiments of the application. The word "if" as used herein may be interpreted as "at … …" or "at … …" or "in response to a determination" depending on the context. The foregoing description of the preferred embodiment(s) of the application is not intended to limit the embodiment(s) of the application, but is to be accorded the widest scope consistent with the principles and spirit of the embodiment(s) of the application.
While the foregoing is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.
Claims (7)
1. A method for processing an ecosystem carbon flux measurement signal, comprising:
performing singular value decomposition denoising on the ecosystem carbon flux measurement signal to obtain a first signal subjected to preliminary denoising treatment;
performing empirical mode decomposition on the first signal to obtain a plurality of linear steady-state signals included in the first signal, and determining one of the linear steady-state signals as a signal to be processed;
Performing filtering noise reduction processing based on a particle swarm algorithm on the signal to be processed to obtain a processed signal;
wherein: the empirical mode decomposition of the first signal is performed to obtain a plurality of linear steady-state signals included in the first signal, and one of the linear steady-state signals is determined to be a signal to be processed, including:
Decomposing the first signal into a plurality of linear steady-state signals and a residual component based on an empirical mode decomposition algorithm;
Selecting a reconstruction signal reflecting the most local features of the signal and the residual component as a signal to be processed, wherein: the calculation of the representation factor of the signal local feature is as follows:
C = (1-DE)+r
wherein: c is a representation factor of a signal local feature, DE is a dispersion entropy value of the steady-state signal, and r is a correlation degree of the residual component and the first signal;
the filtering noise reduction processing based on the particle swarm algorithm is carried out on the signal to be processed to obtain a processed signal, and the method comprises the following steps:
The filter order and the filter window in the SG filter algorithm are used as optimization objects of the particle swarm algorithm, and the signal-to-noise ratio and root mean square error of the filtered signal relative to the signal before filtering are used as construction basis of the fitness function of the particle swarm algorithm to optimize the SG filter algorithm, so that an improved filter algorithm is obtained;
Filtering and noise reduction processing is carried out on the signal to be processed based on the improved filtering algorithm, and a processed signal is obtained;
The method for optimizing the SG filter algorithm by taking the filter order and the filter window in the SG filter algorithm as optimization objects of the particle swarm algorithm and taking the signal-to-noise ratio and root mean square error of the filtered signal relative to the signal before the filter as construction basis of the fitness function of the particle swarm algorithm comprises the following steps:
Optimizing the SG filtering algorithm by taking the filtering order and the filtering window in the SG filtering algorithm as optimization objects for improving the particle swarm algorithm and taking the signal-to-noise ratio and root mean square error of the filtered signal relative to the signal before filtering as construction basis for improving the fitness function of the particle swarm algorithm; wherein: the improved particle swarm algorithm comprises inertia weights and learning factors of the improved particle swarm algorithm, wherein the method for improving the inertia weights comprises the following steps:
Dynamically adjusting the inertia weight w under different searching times i to be w (i), setting the maximum value wmax to be 0.9, setting the minimum value wmin to be 0.4, ；
The self-learning factor c1 (i) and the global learning factor c2 (i) are improved by adopting the following formula:
；
。
2. The method for processing the ecosystem carbon flux measurement signal according to claim 1, wherein the performing singular value decomposition denoising on the ecosystem carbon flux measurement signal to obtain the first signal of the preliminary denoising process comprises:
constructing a singular value matrix of the carbon flux measurement signal of the ecological system through a singular value decomposition algorithm;
And obtaining a first signal of preliminary noise reduction processing according to diagonal elements of the singular value matrix.
3. The method for processing an ecosystem carbon flux measurement signal according to claim 2, wherein the obtaining a preliminary noise reduction processed first signal from diagonal elements of the singular value matrix includes:
summing singular value elements on diagonal lines of the singular value matrix to obtain a singular value sum;
obtaining singular value entropy corresponding to each singular value element on a diagonal of the singular value matrix based on the singular value sum;
and obtaining a first signal of the preliminary noise reduction processing according to each singular value entropy.
4. A method of processing an ecosystem carbon flux measurement signal according to claim 3, wherein said obtaining a preliminary noise reduction processed first signal from each of said singular value entropies comprises:
Obtaining singular value entropy quotient corresponding to each two adjacent singular value elements based on the singular value entropy and all the two adjacent singular value elements on the diagonal of the singular value matrix;
Acquiring singular value elements which keep changing of all the singular value entropy quotient as target singular value elements;
Determining a decomposition signal of the ecosystem carbon flux measurement signal corresponding to the target singular value element as a first sub-signal,
A first signal of a preliminary noise reduction process is obtained based on the first sub-signal.
5. A device for processing an ecosystem carbon flux measurement signal, comprising:
The preliminary noise reduction device is configured to perform singular value decomposition denoising on the ecosystem carbon flux measurement signal to obtain a first signal subjected to preliminary noise reduction treatment;
The signal processing device is configured to perform empirical mode decomposition on the first signal to obtain a plurality of linear steady-state signals included in the first signal, and determine one of the linear steady-state signals as a signal to be processed;
The filtering and noise reduction device is configured to perform filtering and noise reduction processing based on a particle swarm algorithm on the signal to be processed to obtain a processed signal;
wherein: the empirical mode decomposition of the first signal is performed to obtain a plurality of linear steady-state signals included in the first signal, and one of the linear steady-state signals is determined to be a signal to be processed, including:
Decomposing the first signal into a plurality of linear steady-state signals and a residual component based on an empirical mode decomposition algorithm;
Selecting a reconstruction signal reflecting the most local features of the signal and the residual component as a signal to be processed, wherein: the calculation of the representation factor of the signal local feature is as follows:
C = (1-DE)+r
wherein: c is a representation factor of a signal local feature, DE is a dispersion entropy value of the steady-state signal, and r is a correlation degree of the residual component and the first signal;
the filtering noise reduction processing based on the particle swarm algorithm is carried out on the signal to be processed to obtain a processed signal, and the method comprises the following steps:
The filter order and the filter window in the SG filter algorithm are used as optimization objects of the particle swarm algorithm, and the signal-to-noise ratio and root mean square error of the filtered signal relative to the signal before filtering are used as construction basis of the fitness function of the particle swarm algorithm to optimize the SG filter algorithm, so that an improved filter algorithm is obtained;
Filtering and noise reduction processing is carried out on the signal to be processed based on the improved filtering algorithm, and a processed signal is obtained;
The method for optimizing the SG filter algorithm by taking the filter order and the filter window in the SG filter algorithm as optimization objects of the particle swarm algorithm and taking the signal-to-noise ratio and root mean square error of the filtered signal relative to the signal before the filter as construction basis of the fitness function of the particle swarm algorithm comprises the following steps:
Optimizing the SG filtering algorithm by taking the filtering order and the filtering window in the SG filtering algorithm as optimization objects for improving the particle swarm algorithm and taking the signal-to-noise ratio and root mean square error of the filtered signal relative to the signal before filtering as construction basis for improving the fitness function of the particle swarm algorithm; wherein: the improved particle swarm algorithm comprises inertia weights and learning factors of the improved particle swarm algorithm, wherein the method for improving the inertia weights comprises the following steps:
Dynamically adjusting the inertia weight w under different searching times i to be w (i), setting the maximum value wmax to be 0.9, setting the minimum value wmin to be 0.4, ；
The self-learning factor c1 (i) and the global learning factor c2 (i) are improved by adopting the following formula:
；
。
6. An electronic device, comprising: a processor;
A processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to implement the method of any of claims 1-4 by executing the executable instructions.
7. A computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the steps of the method according to any of claims 1-4.
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