A method and system for extracting absolute values of non-periodic disturbances

Abstract

The application discloses a kind of non-periodic disturbance absolute value extraction method and system, the method includes: obtaining the first probability density distribution of original data set and carries out Gaussian fitting to determine the first variation range corresponding to the disturbance signal in the original data set;The original data set is processed by white spectrum method to obtain disturbance relative amount;Obtain the second distribution feature of the disturbance relative amount and carry out Gaussian fitting to obtain the second variation range of disturbance relative amount;The disturbance absolute value is obtained by the product of the disturbance relative amount and the proportional coefficient;The proportional coefficient is equal to the first variation range divided by second variation range.The conversion from relative value to specific value is realized, and the scene demand of needing to quantify disturbance intensity is met.At the same time, the disturbance is separated from the background signal, so that the background information also has discussability, which further expands the application range.

Description

Technical Field

[0001] The present invention relates to the field of satellite meteorological technology, and in particular to a method and system for extracting the absolute value of a non-periodic disturbance. Background Art

[0002] Separating disturbances from their background has always been a core and challenging task in studying disturbances in various regions of space weather. Traditional disturbance extraction methods, such as the monthly median method and the 27-day sliding monthly median method, often define the monthly median (sliding monthly median) of observational data at a given local time as background and the difference between it and the original value as disturbance, respectively. However, these methods are insensitive to periodic variations larger than the smoothing window and therefore fail to accurately reflect background information. Wang et al. (2014) proposed a new method for identifying non-periodic disturbances: the Spectral Whitening Method (SWM). This method, derived from the spectral whitening technique in statistical estimation theory, imparts a uniform distribution to the processed data (i.e., a white noise spectrum). The SWM utilizes the upper envelope of the original power spectrum to "level" the original power spectrum, effectively removing the periodic component (background) from the data. This allows the non-periodic component (disturbance) of interest to be clearly discernible in the final result, resulting in a nearly normal distribution.

[0003] Currently, the white spectrum method has been used to construct disturbance indices for the solar, interplanetary, magnetosphere, ionosphere, and thermosphere regions. The basic principles and implementation steps of the existing white spectrum method are as follows:

[0004] Data extension and Fourier transform: By periodically extending the original data set before Fourier transform, the boundary effect generated during the frequency domain conversion process is avoided;

[0005] Whitening of the power spectrum: by dividing it by the background spectrum, the power spectrum is made to appear as white noise;

[0006] Power spectrum intensity restoration: multiply the whitened spectrum by the mode of the extended data to make the spectrum intensity consistent with that before whitening;

[0007] Inverse Fourier transform and truncation of spectrum: Perform inverse Fourier transform on the white spectrum result to the time domain, and truncate the middle part of the extended data to obtain the disturbance.

[0008] For a time series, the SWM algorithm for calculating disturbances is as follows:

[0009] ,

[0010] The above formula is from the original observation data Get the final perturbation result process. for The upper envelope function of the power spectrum, for The value that appears most frequently in The mode of .

[0011] However, the output of the above-mentioned white spectrum method is limited to dimensionless relative values ​​and cannot directly provide the absolute value of the disturbance. Specifically, when the white spectrum method was initially constructed, in the third step (power spectrum intensity recovery), the whitened spectrum was multiplied by the mode of the extended data (i.e., ), is to make the intensity of the spectrum consistent with that before whitening. The white spectrum method believes that As The value that appears most frequently in The mode of , approximately represents the spectral intensity of the disturbance, so that most of the spectral components in the spectrum remain basically the same before and after whitening. However, it is found that the disturbance finally extracted is not the specific value of the disturbance, which may be because It cannot reflect the spectral intensity of the disturbance well, or in the process of reducing the spectral intensity of the periodic component to the noise level during the white spectrum process, some new noise is introduced. However, since the final application of the white spectrum method is to construct an index, that is, there is a step of standardization (dividing the extracted disturbance by its standard deviation), whether or not the formula is multiplied by None of these will affect the final constructed index, because its purpose is to finally obtain only the dimensionless relative quantity after standardization.

[0012] However, this method has the following shortcomings: (1) Limitations of dimensionless results: The results obtained using the white spectrum method are dimensionless relative values ​​and cannot directly reflect the specific physical value of the disturbance. The existing white spectrum method lacks the ability to describe the specific size of the disturbance, which limits the physical analysis or verification work involving the specific value of the disturbance and makes it difficult to easily separate the disturbance value from the background value in the original data set. (2) Limited scope of application: Since the specific disturbance value cannot be provided, the results of the white spectrum method can only be used to analyze the degree of abnormality and cannot meet the needs of certain scenarios that require quantification of the specific value of the disturbance intensity. For example, when analyzing abnormal temperature changes (abnormally high temperature), it is impossible to know how many degrees the temperature is abnormally higher than the normal value. Summary of the Invention

[0013] The present invention provides a method and system for extracting the absolute value of non-periodic disturbances. This approach addresses the problem of the existing white spectrum method, which outputs only dimensionless relative values ​​and cannot directly provide the absolute magnitude of the disturbance. This makes it unsuitable for practical scenarios requiring specific measurement of disturbance intensity. This limits the application and practicality of the white spectrum method in fields such as thermospheric disturbance analysis, space weather monitoring, and even meteorological extreme weather monitoring.

[0014] The present invention includes a method for extracting the absolute value of a non-periodic disturbance, characterized in that the method comprises:

[0015] Obtaining a first probability density distribution of an original data set and performing Gaussian fitting to determine a first variation range corresponding to a disturbance signal in the original data set;

[0016] Processing the original data set by a white spectrum method to obtain a relative amount of disturbance;

[0017] Obtaining a second distribution characteristic of the relative disturbance amount and performing Gaussian fitting to obtain a second variation range of the relative disturbance amount;

[0018] The absolute value of the disturbance is obtained by multiplying the relative amount of the disturbance by a proportional coefficient; the proportional coefficient is equal to the first variation range divided by the second variation range.

[0019] Optionally, before obtaining the first probability density distribution of the original data set, the method further includes: performing detrending processing on the original data set; after processing the original data set using the white spectrum method to obtain the relative amount of disturbance, the method includes: performing detrending processing on the relative amount of disturbance.

[0020] Optionally, performing detrending processing on the original data set includes:

[0021] Fit the original data set by the least squares method The linear trend ,in Indicates the The original data, is the total number of data, is time, slope and intercept They are:

[0022]

[0023]

[0024] Calculate the detrended version of the original dataset: , get the original data set after detrending .

[0025] Calculate the detrended version of the original dataset: , get the original data set after detrending .

[0026] Optionally, obtaining a first probability density distribution of the original data set includes:

[0027] Calculate the original data set after detrending The normalized histogram of is used to obtain the empirical probability density distribution; the histogram is divided into intervals, each interval has a width of , the probability density is approximately: ;

[0028] A normalized histogram is drawn to obtain the first probability density distribution.

[0029] Optionally, the method further includes:

[0030] The original data set after detrending is clustered by K-means algorithm Perform initial grouping and obtain Cluster centers and its sample allocation;

[0031] Get the mixing coefficient , mean ,variance ,in For the A set of clustered samples, is the number of samples;

[0032] Optimize parameters iteratively using the expectation maximization algorithm , and generate a mixed probability density function.

[0033] Optionally, using an expectation-maximization algorithm to iteratively optimize parameters and generate a mixed probability density function includes:

[0034] Calculate the original data after each detrending Belong to The posterior probability of the Gaussian components ;

[0035] according to renew 、 、 ;

[0036] Iterate until the log-likelihood value The change is less than the threshold;

[0037] get Parameters of Gaussian components , and generates a mixed probability density function:

[0038]

[0039] in .

[0040] Optionally, the method further includes: identifying a Gaussian component corresponding to the disturbance signal from the multi-Gaussian fitting result, and determining a first variation range corresponding to the disturbance signal.

[0041] Optionally, the method further includes:

[0042] According to the fitting Gaussian components, analyze their mean ,variance and mixing coefficient ;

[0043] Select the Gaussian component that best matches the disturbance signal characteristics and record it as Gaussian components ;

[0044] Will Defined as The width of the Gaussian components, based on the standard deviation calculate ;in is the coverage factor;

[0045] A first variation range is obtained according to the standard deviation .

[0046] The present invention provides a system for extracting the absolute value of a non-periodic disturbance, the system comprising:

[0047] A first acquisition unit is configured to acquire a first probability density distribution of an original data set to determine a first variation range corresponding to a disturbance signal in the original data set;

[0048] a processing unit, configured to process the original data set by a white spectrum method to obtain a relative amount of disturbance;

[0049] a second acquiring unit, configured to acquire a second distribution characteristic of the relative disturbance amount to acquire a second variation range of the relative disturbance amount;

[0050] A calculation unit is used to obtain the absolute value of the disturbance by multiplying the relative amount of the disturbance by a proportional coefficient; the proportional coefficient is equal to the first variation range divided by the second variation range.

[0051] The present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement any of the methods described above.

[0052] The method described in this invention achieves the conversion from relative values ​​to specific numerical values, meeting the needs of scenarios requiring quantification of disturbance intensity. It also separates disturbances from background signals, making background information accessible and addressing the issue of lack of physical meaning. It also further expands its scope of application. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flow chart of a method for extracting the absolute value of a non-periodic disturbance in an embodiment of the present invention;

[0054] Figure 2 is a structural diagram of a system for extracting the absolute value of a non-periodic disturbance in an embodiment of the present invention;

[0055] Figure 3 is a multi-Gaussian fitting distribution diagram of the original target temperature data after detrending in an embodiment of the present invention;

[0056] Figure 4 is the Gaussian distribution graph corresponding to the white spectrum result after detrending in the embodiment of the present invention;

[0057] Figure 5 Schematic diagram of the relative amount of disturbance obtained by the white spectrum method in an embodiment of the present invention;

[0058] Figure 6 It is a schematic diagram of converting the relative amount of disturbance obtained by the white spectrum method into an absolute value in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all structures.

[0060] It should be understood that in the various embodiments of this document, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this document.

[0061] The prior art white spectrum method itself does not need to and cannot obtain the absolute value of the disturbance. Therefore, the embodiment of the present invention explores the "magnitude" of the disturbance from the original data, that is, the range or scale of the disturbance change, and obtains the proportional coefficient by comparing it with the range of change of the relative amount of the disturbance to help convert the relative amount of the disturbance into an absolute value.

[0062] The embodiment of the present invention provides a method for extracting the absolute value of a non-periodic disturbance, such as Figure 1 As shown, the method includes:

[0063] Step 100: Obtain a first probability density distribution of the original data set and perform Gaussian fitting to determine a first variation range corresponding to the disturbance signal in the original data set. The original data set is the target original data to be analyzed. The original data set is processed to obtain a first probability density distribution, i.e., the data distribution characteristics of the original data set are obtained. Subsequently, the data distribution characteristics are decomposed by Gaussian fitting to obtain a corresponding Gaussian fitting curve and a data variation range corresponding to the Gaussian fitting curve. The data variation range is the first variation range corresponding to the disturbance signal. Specifically, the Gaussian component corresponding to the disturbance signal is identified from the multi-Gaussian fitting results, and its data variation range is determined as the first variation range.

[0064] Step 200: Process the original data set by the white spectrum method to obtain the relative amount of disturbance. Apply the White Spectrum Method (SWM) to extract the relative amount of the disturbance signal , the specific steps of applying the white spectrum method will not be repeated.

[0065] Step 300: Obtain a second distribution characteristic of the relative disturbance amount and perform Gaussian fitting to obtain a second variation range of the relative disturbance amount. The specific execution process is similar to step 100, and the second variation range of the relative disturbance amount is finally obtained, which will not be repeated here.

[0066] Step 400: Obtain the absolute value of the disturbance by multiplying the relative disturbance amount by a proportional coefficient. The proportional coefficient is equal to the first variation range divided by the second variation range. Specifically, the proportional coefficient is obtained by dividing the variation range of the disturbance signal (i.e., noise) by the variation range of the relative disturbance amount after the white spectrum. This proportional coefficient is a key factor in converting the relative disturbance amount to an absolute value. The absolute value of the disturbance is obtained by multiplying the proportional coefficient by the relative disturbance amount.

[0067] Specifically, the first variation range is R1, and the second variation range is R2, and the proportional coefficient used to convert the relative amount of disturbance into the absolute value is .

[0068] Multiply it by the relative amount of disturbance obtained by the White Spectrum method to obtain the final absolute value of the disturbance .

[0069] .

[0070] The method described in the above embodiment of the present invention realizes the conversion from relative values ​​to specific numerical values, meeting the needs of scenarios that require quantification of disturbance intensity. At the same time, the disturbance is separated from the background signal, making the background information also discussable, solving the problem of lack of physical meaning. At the same time, it also further expands the scope of application. The new method can be applied to practical scenarios that require absolute numerical data. For example, in meteorology, the calculation of extreme temperature thresholds. In satellite orbit attenuation monitoring, it provides accurate absolute values ​​of thermospheric disturbances to optimize trajectory planning and life prediction.

[0071] Directly performing probability density distribution and Gaussian fit analysis on raw data has significant limitations. Raw data (such as time-series temperature data) typically contain multiple signal components, including long-term trends, seasonal variations, and short-term disturbances. Direct statistical analysis of the entire data will result in a probability density distribution and Gaussian fit that reflects the mixed statistical characteristics of all these signals and will fail to effectively isolate the characteristics of the disturbance signal. For example, the range of numerical variations obtained through direct fitting may be significantly affected by long-term trends, resulting in magnitudes far greater than the typical magnitude of the disturbance signal, making it impossible to accurately correspond to the distribution characteristics of the relative magnitude of the disturbance.

[0072] Therefore, the method for extracting the absolute value of a non-periodic disturbance described in a specific embodiment of the present invention preferably further comprises, before obtaining the first probability density distribution of the original data set, performing detrending processing on the original data set; and after processing the original data set using the white spectrum method to obtain the relative amount of disturbance, the method further comprises: performing detrending processing on the relative amount of disturbance. Specifically, the purpose of performing detrending processing on the original data set and performing detrending processing on the relative amount of disturbance is to remove long-term linear trends in the signal to eliminate interference from the long-term linear trend.

[0073] In the method for extracting the absolute value of the non-periodic disturbance described in the specific embodiment of the present invention, preferably, performing detrending processing on the original data set includes:

[0074] Fit the original data set by the least squares method The linear trend ,in Indicates the The original data, is the total number of data, is time, slope and intercept They are:

[0075] ,

[0076] ,

[0077] Calculate the detrended version of the original dataset: , get the original data set after detrending The detrending process for the relative disturbance amount can also be performed by using similar steps as above, which will not be described in detail.

[0078] The method for extracting the absolute value of the non-periodic disturbance described in the specific embodiment of the present invention preferably obtains the first probability density distribution of the original data set, including:

[0079] Calculate the original data set after detrending The normalized histogram of is used to obtain the empirical probability density distribution; the histogram is divided into intervals, each interval has a width of , the probability density is approximately: ;

[0080] A normalized histogram is drawn to obtain the first probability density distribution.

[0081] Specifically, the purpose of obtaining the first probability density distribution of the original data set is to perform probability density analysis on the detrended data set and decompose its distribution characteristics through Gaussian fitting.

[0082] In a preferred embodiment, the second probability density distribution of the relative amount of disturbance after detrending is calculated in the same manner, and its distribution characteristics are decomposed by Gaussian fitting.

[0083] The method for extracting the absolute value of the non-periodic disturbance described in the specific embodiment of the present invention preferably further includes:

[0084] The original data set after detrending is clustered by K-means algorithm Perform initial grouping and obtain Cluster centers and its sample allocation;

[0085] Get the mixing coefficient , mean ,variance ,in For the A set of clustered samples, is the number of samples; the mixing coefficient is The weights of the Gaussian distribution.

[0086] Expectation maximization (EM) algorithm is used to iteratively optimize parameters , and generate a mixed probability density function.

[0087] The method for extracting the absolute value of the non-periodic disturbance described in the specific embodiment of the present invention preferably uses an expectation-maximization algorithm to iteratively optimize parameters and generate a mixed probability density function, including:

[0088] Compute each detrended raw data Belong to the Posterior probability of the Gaussian component ;

[0089] According to Update , , ;

[0090] Iterate until the change in log-likelihood value Is less than a threshold value;

[0091] Get The parameters of the Gaussian component , and generate a mixed probability density function:

[0092]

[0093] Where .

[0094] Preferably, the relative quantity data set of the detrended disturbance Calculate its second probability density distribution, and the process of analyzing its statistical characteristics by Gaussian fitting can adopt the similar steps.

[0095] The method for extracting the absolute value of the aperiodic disturbance according to the specific embodiments of the present application, preferably, the method further comprises: identifying the Gaussian component corresponding to the disturbance signal from the multi-Gaussian fitting result, and determining the first change range corresponding to the disturbance signal.

[0096] The method for extracting the absolute value of the aperiodic disturbance according to the specific embodiments of the present application, preferably, the method further comprises:

[0097] According to the obtained Gaussian component, analyze its mean , variance And mixing coefficient ;

[0098] Select the Gaussian component that best matches the characteristics of the disturbance signal, denoted as the Gaussian component ;

[0099] Define As the width of the Gaussian component, based on the standard deviation Calculate ; wherein Is the coverage factor; for example , corresponding to the probability coverage range of about 99.73%.

[0100] A first variation range is obtained according to the standard deviation . Get the first range of change for .

[0101] Preferably, determine the second variation range corresponding to the relative amount of disturbance The same method as above can also be used.

[0102] The specific embodiment of the present invention also provides a system for extracting the absolute value of a non-periodic disturbance, such as Figure 2 As shown, the system includes:

[0103] A first acquiring unit 201 is configured to acquire a first probability density distribution of an original data set to determine a first variation range corresponding to a disturbance signal in the original data set;

[0104] A processing unit 202 is configured to process the original data set using a white spectrum method to obtain a relative amount of disturbance;

[0105] A second acquiring unit 203 is configured to acquire a second distribution characteristic of the relative disturbance amount to acquire a second variation range of the relative disturbance amount;

[0106] The calculation unit 204 is configured to obtain an absolute value of the disturbance by multiplying the relative amount of the disturbance by a proportional coefficient; the proportional coefficient is equal to the first variation range divided by the second variation range.

[0107] The present invention also provides a specific embodiment. This embodiment uses summer average temperature data from 699 meteorological stations in China, derived from the "China Surface Climate Data Daily Value Dataset," as an example to demonstrate the application of the present method. The target original dataset 𝑋 contains the average daily temperature for 62 summer years (defined as June 1st to August 31st each year, a total of 92 days) from 1961 to 2022. The total number of samples 𝑁 = 62 × 92 = 5704.

[0108] First, detrend the target original data set. ,in is the average daily temperature on day i (unit: °C), and the time index 1, 2,…, 5704 represents the number of summer days from 1961 to 2022. Applying the least squares method to fit a linear trend And perform detrending processing to obtain the detrended data set .

[0109] Again Calculate the normalized histogram and set the number of intervals = 30. The histogram shows that the probability density distribution of the detrended data presents multimodal characteristics.

[0110] Perform multi-Gaussian fitting: set the number of Gaussian components =2, use K-means clustering initialization parameters - cluster center, initial variance and initial mixing coefficient (where it is initialized to uniform distribution, so the mixing coefficient ).

[0111] Then determine the Gaussian fitting curve and data variation range R1 corresponding to the disturbance signal in the target original data. In the process of disturbance signal identification, first analyze the two Gaussian components and determine the component with a high mean and a small variance as the summer high temperature disturbance signal. Figure 3 Yellow line (Gaussian fit component 2).

[0112] Range R1: Take the coverage factor (about 99.73% coverage), calculate .

[0113] Apply EM algorithm for iterative optimization and set the convergence condition as the change of log-likelihood value is less than , and get the final parameters.

[0114] Then the target original data is processed by the white spectrum method and the relative amount of disturbance is detrended; specifically, the target original data set As Enter the Bai spectrum method formula:

[0115]

[0116] Get the relative amount of disturbance .

[0117] right Remove the fitted linear trend , and get the relative amount of disturbance after detrending .

[0118] Calculate the probability density distribution of the relative amount of disturbance after detrending and perform Gaussian fitting; specifically, Calculate the normalized histogram, such as Figure 4 The histogram shows that the probability density distribution of the detrended data exhibits a unimodal characteristic. A Gaussian fit is then performed to obtain the mean and variance.

[0119] Determine the data variation range R2 corresponding to the Gaussian fitting curve of the relative amount of disturbance after detrending; also take the coverage factor (about 99.73% coverage), calculate .

[0120] Finally, the absolute value of the disturbance is obtained by multiplying the relative amount of disturbance by R1 / R2. .

[0121]

[0122] Figure 5 and Figure 6 are the relative amount of disturbance obtained by the White Spectrum Method and the absolute value of disturbance calculated by this embodiment. Figure 5 The relative amount of disturbance obtained from the above equation cannot correspond to a specific physical quantity (such as temperature), and the specific intensity of disturbance cannot be obtained. It lacks intuitive physical meaning and interpretability. Figure 6 The specific values ​​of summer temperature disturbances in my country between 1961 and 2022 can be seen in the table. The average summer temperature anomaly in my country between 1961 and 2022 showed an overall significant increasing trend. In particular, since 2005, the absolute value of the disturbances indicates that summer temperatures in my country have been higher than historically for the same period, indicating a thermal anomaly. The summer temperature anomaly in 2022 was abnormally high, reaching its highest value since 1961, by approximately 1.15°C, indicating that the summer temperature and thermal anomaly in 2022 was highly extreme. Extreme climate events are associated with numerous factors, including internal variability in the atmospheric circulation (such as variations in the Western Pacific Subtropical High and the mid-latitude westerlies) and multi-sphere interactions (such as tropical sea-air interactions and Arctic ice-air interactions). The Western Pacific Subtropical High was stronger in the summer of 2022, controlling an exceptionally strong high pressure zone over central and eastern China. This resulted in significantly elevated convective activity, leading to extremely severe high temperatures in central and eastern China. June 2022. Positive anomalies in 500hPa geopotential height over Northwest and North China have led to the development of a strong high-pressure ridge over these regions, weakening convective activity and resulting in an increase in extreme high-temperature events over these regions. These factors may have contributed to the abnormally high summer temperatures in China in 2022.

[0123] A specific embodiment of the present invention further provides a computer-readable storage medium, which stores one or more programs. The one or more programs can be executed by one or more processors to implement the method described in any of the above embodiments.

[0124] The present invention improves the existing white spectrum method and breaks through the limitation of the existing technology that can only extract the relative amount of non-periodic disturbances. The white spectrum method can not only extract relative disturbance information, but also further quantitatively obtain the specific physical value of the disturbance. Compared with the existing technology, the present invention has the following significant advantages: (1) Specific value quantitative analysis capability: The present invention calculates and introduces a proportional coefficient so that the result obtained by the white spectrum method is no longer an abstract relative quantity, but a specific numerical value with clear physical meaning. This means that in many research scenarios, the physical meaning of its abnormal signal can be analyzed more deeply. (2) More direct physical meaning: The specific value of the disturbance calculated by the new method is linked to the actual observation or basic physical quantity, and the output has a clear physical meaning and unit, thereby improving the scientificity and practicality of the result. (3) Wider applicability: The present invention expands the scope of the white spectrum method in practical applications, supports various quantitative analysis tasks that require specific values, and has important value in building more accurate disturbance models. For example, in meteorological research, quantifying the change of temperature disturbance can obtain a specific temperature threshold, thereby better identifying extreme weather.

[0125] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0126] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0127] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 the function specified in the one or more blocks.

[0128] These computer program instructions can also be loaded into computer or other programmable data processing devices, so that a series of operation steps are performed on the computer or other programmable devices to generate computer-implemented processing, so that the instructions executed on the computer or other programmable devices provide a flow Figure 1 the flow or flows and / or blocks Figure 1 the function specified in the one or more blocks.

[0129] The foregoing description of specific exemplary embodiments of the application has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the application to the precise form disclosed, and obviously many modifications and variations are possible in light of the above teaching. It is intended that the scope of the application be limited not with this detailed description, but rather by the claims appended hereto.

Claims

1. A method for extracting the absolute value of a non-periodic disturbance, characterized in that: The method comprises: Obtaining a first probability density distribution of an original data set and performing Gaussian fitting to determine a first variation range corresponding to a disturbance signal in the original data set; Processing the original data set by a white spectrum method to obtain a relative amount of disturbance; Obtaining a second distribution characteristic of the relative disturbance amount and performing Gaussian fitting to obtain a second variation range of the relative disturbance amount; Obtaining an absolute value of the disturbance by multiplying the relative amount of the disturbance by a proportional coefficient; the proportional coefficient is equal to the first variation range divided by the second variation range; Among them, the data distribution characteristics of the original data set are obtained, and then its data distribution characteristics are decomposed through Gaussian fitting to obtain the corresponding Gaussian fitting curve and the data variation range corresponding to the Gaussian fitting curve. The data variation range is the first variation range corresponding to the disturbance signal. Specifically, the Gaussian component corresponding to the disturbance signal is identified from the multi-Gaussian fitting results, and its data variation range is determined as the first variation range.

2. The method for extracting the absolute value of aperiodic disturbance according to claim 1, characterized in that: Before obtaining the first probability density distribution of the original data set, the method further includes performing a detrending process on the original data set; After the original data set is processed by the white spectrum method to obtain the relative amount of disturbance, the method includes: performing detrending processing on the relative amount of disturbance.

3. The method for extracting the absolute value of aperiodic disturbance according to claim 2, characterized in that: Detrending the original data set includes: Fit the original data set by the least squares method The linear trend ,in Indicates the The original data, is the total number of data, is time, slope and intercept They are: ， ， Calculate the detrended version of the original dataset: , get the original data set after detrending .

4. The method for extracting the absolute value of aperiodic disturbance according to claim 3, characterized in that: Obtaining the first probability density distribution of the original data set includes: Calculate the original data set after detrending The normalized histogram of is used to obtain the empirical probability density distribution; the histogram is divided into intervals, each interval has a width of , the probability density is approximately: ; A normalized histogram is drawn to obtain the first probability density distribution.

5. The method for extracting the absolute value of aperiodic disturbance according to claim 4, characterized in that: The method further comprises: The original data set after detrending is clustered by K-means algorithm Perform initial grouping and obtain Cluster centers and its sample allocation; Get the mixing coefficient , mean ,variance ,in For the A set of clustered samples, is the number of samples; Optimize parameters iteratively using the expectation maximization algorithm , and generate a mixed probability density function.

6. The method for extracting the absolute value of aperiodic disturbance according to claim 5, characterized in that: The expectation maximization algorithm is used to iteratively optimize parameters and generate a mixed probability density function including: Calculate the original data after each detrending Belong to The posterior probability of the Gaussian components ; according to renew 、 、 ; Iterate until the log-likelihood value The change is less than the threshold; get Parameters of Gaussian components , and generates a mixed probability density function: ， in .

7. The method for extracting the absolute value of aperiodic disturbance according to claim 6, characterized in that: The method further includes: identifying a Gaussian component corresponding to the disturbance signal from the multi-Gaussian fitting result, and determining a first variation range corresponding to the disturbance signal.

8. The method for extracting the absolute value of aperiodic disturbance according to claim 7, characterized in that: The method further comprises: According to the fitting Gaussian components, analyze their mean ,variance and mixing coefficient ; Select the Gaussian component that best matches the disturbance signal characteristics and record it as Gaussian components ; Will Defined as The width of the Gaussian components, based on the standard deviation calculate ;in is the coverage factor; A first variation range is obtained according to the standard deviation .

9. A system for extracting the absolute value of a non-periodic disturbance, characterized in that: The system comprises: A first acquisition unit is configured to acquire a first probability density distribution of an original data set to determine a first variation range corresponding to a disturbance signal in the original data set; a processing unit, configured to process the original data set by a white spectrum method to obtain a relative amount of disturbance; a second acquiring unit, configured to acquire a second distribution characteristic of the relative disturbance amount to acquire a second variation range of the relative disturbance amount; a calculation unit, configured to obtain an absolute value of the disturbance by multiplying the relative amount of the disturbance by a proportional coefficient; the proportional coefficient being equal to the first variation range divided by the second variation range; Among them, the data distribution characteristics of the original data set are obtained, and then its data distribution characteristics are decomposed through Gaussian fitting to obtain the corresponding Gaussian fitting curve and the data variation range corresponding to the Gaussian fitting curve. The data variation range is the first variation range corresponding to the disturbance signal. Specifically, the Gaussian component corresponding to the disturbance signal is identified from the multi-Gaussian fitting results, and its data variation range is determined as the first variation range.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the method according to any one of claims 1 to 8.

Images