Natural electromagnetic pulse signal processing method based on K-line dynamic modeling

Abstract

The invention discloses a natural electromagnetic pulse signal processing method based on K-line dynamic modeling, and the method comprises the steps: dividing time windows for a natural electromagnetic pulse signal, then introducing an integrated empirical mode decomposition algorithm to process each time window, and extracting an intrinsic signal function of each time window; secondly, gathering amplitude values of intrinsic signal functions of the same number in each time window, obtaining a plurality of sets, numbering the sets in sequence, constructing a K-line rectangular frame for the combination of each number, arranging the K-line rectangular frames of all the number sets according to the frequency from small to large, forming a dynamic K-line graph, enhancing the interpretability of signal features, and obtaining a dynamic K-line graph; the time-energy-frequency three-dimensional coding mechanism of the dynamic K-line graph can intuitively reflect the spatio-temporal evolution law of the pulse signal; and continuously performing high-precision analysis processing on the collected natural electromagnetic pulse signals, and updating the signal change characteristics of the dynamic K-line graph in real time, thereby providing data support for urban geological disaster early warning and power grid safety protection scenes.

Description

Technical Field

[0001] The present invention relates to a method for processing natural electromagnetic pulse signals based on K-line dynamic modeling, belonging to the technical field of signal processing. Background Art

[0002] Urban natural electromagnetic pulse signals are an important part of the complex electromagnetic environment, and their sources include lightning activities (peak electric field intensity up to 10 4 V / m), micro-vibrations of geological structures (such as piezoelectric effects generated by fault friction), and electromagnetic radiation caused by underground fluid movements. Such signals have a wide frequency domain (0.1 Hz - 100 kHz), a low signal-to-noise ratio (<10 dB), and non-steady state characteristics. They are not only the precursor information carriers for geological disaster early warnings but also the key indicators for evaluating the safety of urban power grids (electromagnetic pulses will affect the power grids). Traditional signal extraction and processing methods mainly rely on time-frequency analysis techniques (such as Fourier transform, wavelet analysis), but they have significant deficiencies in dynamic characterization, anti-interference, and real-time performance: on the one hand, conventional algorithms are difficult to analyze the time-frequency coupling characteristics of transient pulses, resulting in insufficient correlation modeling of signal energy, phase, and duration; on the other hand, artificial noises such as 5G base stations (3.5 GHz frequency band) and new energy charging piles in the urban environment are spectrally mixed with natural signals, causing effective signal loss in the filtering process of existing methods, and thus the signal change characteristics cannot be accurately obtained.

[0003] In recent years, with the upgrade of sensor technology and the development of intelligent algorithms, signal processing has entered the stage of dynamic analysis, but there is still a lack of visual expression of multi-dimensional features. In addition, the requirements for real-time acquisition of the characteristic changes of natural electromagnetic pulses at the minute level in the scenarios of urban geological disaster early warnings and power grid safety protection further highlight the necessity of constructing a dynamic processing framework.

[0004] Therefore, how to provide a new method for processing natural electromagnetic pulse signals, which can perform high-precision analysis and processing on the collected urban natural electromagnetic pulse signals, and update the signal change characteristics in real time dynamically, providing data support for the scenarios of urban geological disaster early warnings and power grid safety protection, is the research direction required by the present invention. Summary of the Invention

[0005] In view of the problems existing in the above-mentioned prior art, the present invention provides a method for processing natural electromagnetic pulse signals based on K-line dynamic modeling, which can perform high-precision analysis and processing on the collected urban natural electromagnetic pulse signals, and update the signal change characteristics in real time dynamically, providing data support for the scenarios of urban geological disaster early warnings and power grid safety protection.

[0006] To achieve the above object, the technical solution adopted by the present invention is: a method for processing natural electromagnetic pulse signals based on K-line dynamic modeling, including the following steps:

[0007] Step 1: Obtain a segment of natural electromagnetic pulse signal and divide it into multiple time windows.

[0008] Step 2: Perform modal decomposition on each time window in Step 1 to extract the intrinsic signal functions, number the respective intrinsic signal functions in sequence from low frequency to high frequency, and record the main frequencies corresponding to each number; at the same time, number the amplitude values corresponding to the main frequencies of each intrinsic signal function.

[0009] Step 3: Aggregate the amplitude values of the intrinsic signal functions with the same number in each time window after the processing in Step 2, obtain multiple aggregates and number them in sequence.

[0010] Step 4: Select the first numbered aggregate and calculate the maximum value, minimum value, mean value, and median value of the amplitudes within this aggregate.

[0011] Step 5: Use the amplitude data obtained in Step 4 to create an initial electromagnetic signal K-line rectangle corresponding to the first numbered aggregate.

[0012] Step 6: Calculate the amplitude value distribution density inside the initial electromagnetic signal K-line rectangle, and then obtain the corresponding distribution probability; and fill the inside of the K-line rectangle with different gray values for different distribution probabilities, thus finally forming the K-line rectangle of the first numbered aggregate.

[0013] Step 7: Repeat Steps 4 to 6 to form the K-line rectangles of all numbered aggregates.

[0014] Step 8: Arrange the K-line rectangles of all numbered aggregates in ascending order of frequency, thereby forming the dynamic K-line chart of this segment of natural electromagnetic pulse signal.

[0015] Step 9: Subsequently, obtain the natural electromagnetic pulse signals of different time periods, and sequentially repeat Steps 1 to 8, so as to continuously obtain the dynamic K-line charts of the natural electromagnetic pulse signals of different time periods.

[0016] Further, the numbers of the respective intrinsic signal functions in Step 2 are IMF1 to IMF in sequence from low frequency to high frequency n , and the corresponding main frequencies are denoted as f im1 ~f imn , and each main frequency contains M amplitude values, denoted as A1 to A m .

[0017] Further, the specific method for performing modal decomposition on each time window in Step 2 to extract the intrinsic signal functions is as follows:

[0018] ① Detect the extreme points, detect all local maximum and minimum points of the electromagnetic signal E(t) of the current time window, and form the extreme value sequences {p max (t)} and {p min (t)}.

[0019] ② Generate the envelope line, use cubic spline interpolation to connect the maximum and minimum points to generate the upper and lower envelope lines p max (t) and p min (t).

[0020] ③ Calculate the average envelope line

[0021] ④ Subtract the average envelope line from the original electromagnetic signal in the current time window to obtain the intermediate signal E k (t) = E k-1 (t) - m(t), let IMF be the intrinsic signal function, check whether E k (t) meets the IMF conditions: that is, the difference between the number of extreme points and zero-crossing points is ≤ 1, and the local envelope mean is zero; if not, repeat steps ① to ④ until E k (t) meets the IMF conditions.

[0022] ⑤ Separate the current IMF as the k-th IMF, denoted as IMF k = E k (t), and at this time the residual term

[0023] ⑥ Repeat the above steps ① to ⑤ to process the residual term r(t) until it becomes monotonic or constant, so as to obtain the remaining IMF and complete the extraction of the intrinsic signal function of the current time window.

[0024] Further, the specific content of step five is: use the width of the time window divided in step one as the width of the rectangular frame, take 25% of the median value A 1med as the lower limit value, 175% of the median value A 1med as the upper limit value, the difference between the two is the length of the rectangular frame, draw a perpendicular line from the midpoint of the lower edge width to the minimum value A 1min , draw a perpendicular line from the midpoint of the upper edge width to the maximum value A 1max , mark and display the mean value A 1avg and the median value A 1med in the rectangular frame to complete the production of the initial electromagnetic signal K-line rectangular frame.

[0025] Further, the specific content of step six is: divide the inside of the initial electromagnetic signal K-line rectangular frame into n consecutive ΔA intervals according to the amplitude from the lower limit value to the upper limit value, which are A Δ1 < A Δ2 < … < A Δn , record the total number of amplitude values that meet the upper and lower limit requirements of the initial electromagnetic signal K-line rectangular frame in the first numbered set IMF1 as L; then count the data frequencies L1, L2…Ln in each ΔA interval, and further calculate the distribution density of the amplitude values in each ΔA interval, ρ i = Li / L, where i = 1, 2... n; thus, ρ1, ρ2... ρ for a continuous plurality of ΔA intervals are obtained. n After normalizing them, the distribution probabilities ρ of each ΔA interval are obtained. 1Nor ρ 2Nor ... ρ nNor ; The distribution probabilities of each ΔA interval are filled in the corresponding interval positions of the K-line rectangle frame using different gray values for display, thereby finally forming the K-line rectangle frames of the first number set.

[0026] Further, the specific content of step eight is as follows: Arrange the K-line rectangle frames of all number sets IMFi in ascending order of frequency, where the abscissa is the main frequency value of the intrinsic signal function and the ordinate is the amplitude value. Continuously connect the median values and mean values of adjacent intrinsic signal functions IMF to form a median value curve and a mean value curve, and finally form the dynamic K-line diagram of this section of the natural electromagnetic pulse signal.

[0027] Compared with the prior art, the present invention has the following advantages:

[0028] (1) Intrinsic signal extraction: The present invention first divides the time window of a section of natural electromagnetic pulse signal, and then introduces the ensemble empirical mode decomposition algorithm to process each time window, extracting the intrinsic signal functions of each time window, thereby effectively separating interference noise and reducing the influence of noise on the pulse signal.

[0029] (2) Multidimensional signal fusion: The present invention provides a new idea for dynamic modeling using the K-line diagram technology in the financial field. Combining the time-domain pulse waveform characteristics and the frequency-domain energy distribution, first aggregate the amplitude values of the intrinsic signal functions with the same number in each time window, obtain multiple aggregates and number them in sequence. Construct K-line rectangle frames for each combination of numbers, arrange the K-line rectangle frames of all number sets in ascending order of frequency, and form the dynamic K-line diagram of the natural electromagnetic pulse signal, enhancing the interpretability of signal characteristics. And the time-energy-frequency three-dimensional coding mechanism of this dynamic K-line diagram can intuitively reflect the spatio-temporal evolution law of the pulse signal.

[0030] (3) Real-time dynamic update: The present invention performs high-precision analysis and processing through the dynamic coding of the period, amplitude, and spectral parameters of the natural electromagnetic pulse signal, and real-time updates the signal change characteristics of the dynamic K-line diagram, thereby providing data support for scenarios such as urban geological disaster warning and power grid security protection. Description of the Drawings

[0031] Figure 1 is a schematic diagram of the intrinsic signal function obtained after processing a section of natural electromagnetic pulse signal in the present invention;

[0032] Figure 2 is a schematic diagram of the initial electromagnetic signal K-line rectangle frame of a single number set established in the present invention;

[0033] Figure 3 It is the dynamic K-line chart of the natural electromagnetic pulse signal formed after the treatment of the present invention. Specific embodiments

[0034] The present invention will be further described below.

[0035] As Figure 1 shown, the present invention includes the following steps:

[0036] Step 1: Obtain a segment of natural electromagnetic pulse signal with a sampling rate of F s and divide it into multiple time windows, each time window having a length of M points, thereby obtaining T1 to T n time windows, and the duration of each time window is T = M / F s .

[0037] Step 2: Perform modal decomposition on each time window in Step 1 to extract the intrinsic signal function, specifically:

[0038] ① Detect extreme points, detect all local maximum and minimum points of the electromagnetic signal E(t) in the current time window, and form extreme value sequences {p max (t)} and {p min (t)};

[0039] ② Generate envelope lines, use cubic spline interpolation to connect the maximum and minimum points to generate upper and lower envelope lines p max (t) and p min (t);

[0040] ③ Calculate the average envelope line

[0041] ④ Subtract the average envelope line from the original electromagnetic signal in the current time window to obtain the intermediate signal E k (t) = E k-1 (t) - m(t), let IMF be the intrinsic signal function, and check whether E k (t) satisfies the IMF condition: that is, the difference between the number of extreme points and the number of zero-crossing points is ≤ 1, and the local envelope mean is zero; if not satisfied, repeat steps ① to ④ until E k (t) satisfies the IMF condition;

[0042] ⑤ Separate the current IMF as the kth IMF, denoted as IMF k = E k (t), and at this time the residual term

[0043] ⑥Repeat the processing of the residual term r(t) according to the above steps ① to ⑤ until it becomes monotonic or constant, so that the remaining IMFs can be obtained, and the extraction of the intrinsic signal function for the current time window is completed.

[0044] Then, number the intrinsic signal functions from low frequency to high frequency as IMF1 to IMF n , and record the main frequencies corresponding to each number as f im1 ~f imn , and each main frequency contains M amplitude values, denoted as A1 to A m .

[0045] Step 3: Aggregate the amplitude values of the intrinsic signal functions with the same number in each time window after the processing in Step 2 to obtain multiple aggregates and number them in sequence; taking the first-numbered aggregate as an example, it consists of the amplitudes A n , A 11 , A 12 …A 1n corresponding to IMF1 with the same number in each of the time windows T1 to T n

[0046] Step 4: Select the first-numbered aggregate and calculate the maximum amplitude A 1max , minimum amplitude A 1min , mean value A 1avg , and median value A 1med in this aggregate; the mean value is The median value is calculated by arranging the amplitude values from smallest to largest. If the number of amplitudes is odd, the middle amplitude value is taken as the median value. If it is even, the average of the middle two amplitude values is taken as the median value.

[0047] Step 5: Use the amplitude data obtained in Step 4 to make the initial electromagnetic signal K-line rectangle corresponding to the first-numbered IMF1 aggregate, as shown in Figure 2 . Specifically: Take the width of the time window divided in Step 1 as the width of the rectangle. Take 25% of the median value A 1med as the lower limit value, 175% of the median value A 1med as the upper limit value. The difference between the two is the length of the rectangle. Draw a perpendicular line from the midpoint of the width of the lower edge of the rectangle to the minimum amplitude A 1min , and draw a perpendicular line from the midpoint of the width of the upper edge of the rectangle to the maximum amplitude A 1max . Mark and display the mean value A 1avg and the median value A 1med in the rectangle to complete the making of the initial electromagnetic signal K-line rectangle.

[0048] Step 6: Divide the inside of the initial electromagnetic signal K-line rectangle into n consecutive ΔA intervals according to the amplitude from the lower limit value to the upper limit value, which are A Δ1 <A Δ2 <…<A Δn, the total number of amplitude values in the first numbered set IMF1 that meet the upper and lower limit requirements of the initial electromagnetic signal K-line rectangle is denoted as L; then, the data frequencies L1, L2... Ln in each ΔA interval are counted, and further, the distribution density ρ of the amplitude values in each ΔA interval is calculated. i = L i / L, where i = 1, 2... n; thus, ρ1, ρ2... ρ of consecutive multiple ΔA intervals are obtained. n , after normalizing it, the distribution probabilities ρ of each ΔA interval are obtained. 1Nor , ρ 2Nor ... ρ nNor ; the distribution probabilities of each ΔA interval are filled in the corresponding interval positions of the K-line rectangle with different gray values, thus finally forming the K-line rectangle of the first numbered IMF1 set.

[0049] Step Seven: Repeat Steps Four to Six to form the K-line rectangles of all numbered IMFi sets.

[0050] Step Eight: Arrange the K-line rectangles of all numbered sets IMFi in ascending order of frequency. Among them, the abscissa is the main frequency value of the intrinsic signal function IMF, and the ordinate is the amplitude value. Connect the median and mean values of adjacent intrinsic signal functions IMF continuously to form a median curve and a mean curve, as Figure 3 shown, and finally form the dynamic K-line diagram of this section of the natural electromagnetic pulse signal.

[0051] Step Nine: Subsequently, obtain the natural electromagnetic pulse signals in different time periods, and repeat Steps One to Eight in sequence, so as to continuously obtain the dynamic K-line diagrams of the natural electromagnetic pulse signals in different time periods.

[0052] The above is only the preferred implementation manner of the present invention. It should be noted that: for those of ordinary skill in the art of this technology, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for processing natural electromagnetic pulse signals based on dynamic K-line modeling, characterized in that, It includes the following steps: Step 1: Obtain a natural electromagnetic pulse signal and divide it into multiple time windows; Step 2: Perform modal decomposition on each time window in Step 1 to extract the intrinsic signal function, number each intrinsic signal function in sequence from low frequency to high frequency, and record the main frequency corresponding to each number; at the same time, number the amplitude values corresponding to the main frequencies of each intrinsic signal function; Step 3: Aggregate the amplitude values of the intrinsic signal functions with the same number in each time window after the processing in Step 2, obtain multiple sets and number them in sequence; Step 4: Select the first numbered set and calculate the maximum value, minimum value, mean value, and median value of the amplitudes within this set; Step 5: Use the amplitude data obtained in Step 4 to create the initial electromagnetic signal K-line rectangle corresponding to the first numbered set; Step 6: Calculate the amplitude value distribution density inside the initial electromagnetic signal K-line rectangle, and then obtain the corresponding distribution probability; and fill the inside of the K-line rectangle with different gray values according to different distribution probabilities, thereby finally forming the K-line rectangle of the first numbered set; Step 7: Repeat Steps 4 to 6 to form the K-line rectangles of all numbered sets; Step 8: Arrange the K-line rectangles of all numbered sets in ascending order of frequency, thereby forming the dynamic K-line diagram of this section of the natural electromagnetic pulse signal; Step 9: Subsequently, obtain the natural electromagnetic pulse signals in different time periods, and sequentially repeat Steps 1 to 8, so as to continuously obtain the dynamic K-line diagrams of the natural electromagnetic pulse signals in different time periods.

2. The natural electromagnetic pulse signal processing method based on K-line dynamic modeling according to claim 1, wherein In the second step, the numbers of the respective intrinsic signal functions are IMF1 to IMF in sequence from low frequency to high frequency n , and their respective main frequencies are denoted as f im1 ~f imn . Each main frequency contains M amplitude values, denoted as A1 to A m .

3. The natural electromagnetic pulse signal processing method based on K-line dynamic modeling according to claim 1, wherein In Step 2, when performing modal decomposition on each time window to extract the intrinsic signal function, specifically: ① Detect extreme points, and detect all local maximum and minimum points of the electromagnetic signal E(t) in the current time window to form extreme value sequences {p max (t)} and {p min (t)}; ②Generate the envelope. Use cubic spline interpolation to connect the maximum and minimum points to generate the upper and lower envelopes p max (t) and p min (t); ③ Calculate the average envelope ④ Subtract the average envelope from the original electromagnetic signal in the current time window to obtain the intermediate signal E k (t) = E k-1 (t) - m(t). Let IMF be the intrinsic signal function, and check whether E k (t) meets the IMF conditions: that is, the difference between the number of extreme points and zero-crossing points is ≤ 1, and the local envelope mean is zero; if not, repeat steps ① to ④ until E k (t) meets the IMF conditions; ⑤Separate the current IMF into the k-th IMF, denoted as IMF k = E k (t), and at this time the residual term ⑥ Repeat the processing of the residual term r(t) according to the above Steps ① to ⑤ until it becomes monotonic or constant, thereby obtaining the remaining IMFs and completing the extraction of the intrinsic signal function of the current time window.

4. The natural electromagnetic pulse signal processing method based on K-line dynamic modeling according to claim 1, wherein Step 5 is specifically: Take the width of the time window divided in Step 1 as the width of the rectangle, take 25% of the median value as the lower limit value, 175% of the median value as the upper limit value, and the difference between the two as the length of the rectangle. Then draw a perpendicular line from the midpoint of the lower edge width of the rectangle to the minimum value, and draw a perpendicular line from the midpoint of the upper edge width to the maximum value. Mark and display the mean value and the median value in the rectangle to complete the creation of the initial electromagnetic signal K-line rectangle.

5. The method for processing natural electromagnetic pulse signals based on K-line dynamic modeling according to claim 1, characterized in that Step six is specifically as follows: The interior of the initial electromagnetic signal K-line rectangle is divided into n consecutive ΔA intervals according to the amplitude from the lower limit value to the upper limit value, which are A Δ1 <A Δ2 <…<A Δn . The total number of amplitude values that meet the upper and lower limit requirements of the initial electromagnetic signal K-line rectangle in the first number set is denoted as L. Then, the data frequencies L1, L2…Ln in each ΔA interval are counted, and further, the distribution density of the amplitude values in each ΔA interval, ρ i =L i / L, where i = 1, 2…n; thus, ρ1, ρ2…ρ n of consecutive multiple ΔA intervals are obtained. After normalizing them, the distribution probabilities ρ 1Nor , ρ 2Nor …ρ nNor of each ΔA interval are obtained. The distribution probabilities of each ΔA interval are filled in the corresponding interval positions of the K-line rectangle with different gray values, thereby finally forming the K-line rectangle of the first number set.

6. The method for processing natural electromagnetic pulse signals based on K-line dynamic modeling according to claim 1, wherein Step 8 is specifically: Arrange the K-line rectangles of all numbered sets in ascending order of frequency, where the abscissa is the main frequency value of the intrinsic signal function and the ordinate is the amplitude value. Continuously connect the median values and mean values of adjacent intrinsic signal functions to form a median value curve and a mean value curve, and finally form the dynamic K-line diagram of this section of the natural electromagnetic pulse signal.

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