Lightning early warning system based on HHT (Hilbert-Huang Transform) and time sequence difference method

Abstract

The invention relates to the technical field of meteorological monitoring and thunder and lightning early warning, in particular to a thunder and lightning early warning system based on an HHT (Hilbert Huang Transform) and a time sequence difference method, which comprises the following contents: a multi-site atmospheric electric field instrument network used for collecting atmospheric electric field signals in real time; the signal processing module is used for performing signal denoising, time sequence differential analysis and HHT (Hilbert-Huang Transform); and the early warning module is used for generating early warning information according to the difference threshold value and the time-frequency spectrum energy threshold value and outputting the early warning information to the display terminal. The method is applied to de-noising processing of original atmospheric electric field signals based on a wavelet transform analysis method, and electric field changes caused by the lightning activity process are reserved. By selecting seven wavelet functions and four threshold analysis methods to compare and analyze the denoising effects of the respective analysis methods, it is found that the effect of the Rigorous SURE threshold method of the sym5 wavelet analysis method is optimal.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological monitoring and lightning early warning, and more particularly to a lightning early warning system based on HHT transformation and time series difference method. Background Art

[0002] Lightning warning technology is an important research area for meteorological disaster prevention. Existing methods mainly rely on multi-source observation data such as lightning locators, Doppler radars, and single-station atmospheric electric field meters. Research on lightning warning based on atmospheric electric field meters has made some progress. For example, lightning activity can be determined by characteristics such as ground electric field strength, rate of change, and polarity reversal. However, existing technologies have significant limitations:

[0003] Limited detection range: The effective detection range of a single-station atmospheric electric field instrument is only 15km, which makes it difficult to comprehensively monitor the dynamics of thunderstorm clouds over a large area, resulting in insufficient ability to track the movement path of lightning.

[0004] Severe noise interference: Measured electric field signals often contain environmental noise. Traditional denoising methods (such as Fourier transform) cannot effectively separate lightning signals from non-thunderstorm electric field interference, affecting the accuracy of data analysis.

[0005] Low threshold warning reliability: During lightning activity, atmospheric electric field values ​​are highly dispersed, and electric field strength varies significantly at different stages. This makes single-threshold warnings prone to missed or false alarms. For example, before the first lightning strike, electric field values ​​fluctuate widely, making it difficult to set a universal threshold.

[0006] Insufficient timeliness: Existing methods rely on the absolute value of electric field intensity or simple rate of change. The warning lead time is usually short (e.g., within half an hour), which cannot meet the emergency response needs of high-risk areas.

[0007] Therefore, we proposed a lightning warning system based on HHT transformation and time series difference method to solve the above problems. Summary of the Invention

[0008] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a lightning warning system based on HHT transformation and a time series difference method to solve the problems raised in the above-mentioned background technology.

[0009] To achieve the above objectives, the present invention provides the following technical solutions: a lightning warning system based on HHT transformation and time series difference method, comprising the following contents:

[0010] Multi-site atmospheric electric field instrument network: used to collect atmospheric electric field signals in real time;

[0011] Signal processing module: used to perform signal denoising, time series difference analysis and HHT transformation;

[0012] Warning module: used to generate warning information based on the differential threshold and time-frequency spectrum energy threshold, and output it to the display terminal.

[0013] In a preferred embodiment, the system further comprises:

[0014] Data fusion module: used to perform spatiotemporal superposition of radar echo data and atmospheric electric field data to generate a lightning activity distribution map and display the lightning movement path in real time through a geographic information layer.

[0015] In a preferred embodiment, a lightning warning method based on HHT transformation and time series difference method includes the following steps:

[0016] Step S1: collecting the original atmospheric electric field signals monitored by the multi-site atmospheric electric field instrument and performing denoising on the original signals;

[0017] Step S2: Performing time series differential analysis on the denoised signal, calculating the differential electric field strength, and determining the lightning warning threshold based on the change in the differential electric field strength;

[0018] Step S3: Perform HHT transform analysis on the denoised signal to generate a time-frequency spectrum and extract the time-frequency spectrum energy threshold;

[0019] Step S4: combining the differential electric field intensity threshold and the time-frequency spectrum energy threshold to provide an early warning of lightning activity.

[0020] In a preferred embodiment, the denoising process in step S1 uses the sym5 wavelet function combined with the RigorousSURE threshold method, specifically including:

[0021] The original signal is decomposed by wavelet, and the noise signal is removed by the Rigorous SURE threshold method to retain the characteristics of the electric field changes caused by lightning activities.

[0022] In a preferred embodiment, the calculation formula for the timing difference analysis in step S2 is:

[0023] ΔE(t)=E(t)-E(t-1)

[0024] Where ΔE(t) is the differential electric field intensity, E(t) is the atmospheric electric field intensity at the current moment, and E(t-1) is the atmospheric electric field intensity at the previous moment;

[0025] The lightning warning threshold is when the absolute value of the differential electric field strength reaches 0.5kV / m, and the lightning warning lead time is 50 minutes.

[0026] In a preferred embodiment, the HHT transformation analysis in step S3 includes:

[0027] The atmospheric electric field signal is decomposed into multiple groups of intrinsic mode functions (IMFs) through ensemble empirical mode decomposition (EEMD);

[0028] Hilbert transform is performed on multiple groups of intrinsic mode function components to generate time-frequency spectrograms, and lightning activities are identified by energy thresholds.

[0029] In a preferred embodiment, the time-spectrum energy threshold is the energy intensity corresponding to the occurrence of lightning activity, and the energy value in the time-spectrum diagram intuitively reflects the lightning activity stage through color changes.

[0030] In a preferred embodiment, the multi-site joint monitoring step is also included:

[0031] Deploy at least four atmospheric electric field instrument sites, forming a monitoring network with each site as the center and a radius of 15 km;

[0032] By integrating multi-site data, the spatial movement path of lightning activities and the characteristics of electric field changes are analyzed.

[0033] In a preferred embodiment, the early warning of lightning activity in step S4 includes:

[0034] When the absolute value of the differential electric field intensity reaches 0.5kV / m for the first time, the primary warning is triggered;

[0035] When the spectrum energy value exceeds the preset threshold, a secondary warning is triggered, and the location of lightning activity is verified in combination with radar echo data.

[0036] Technical effects and advantages of the present invention:

[0037] 1. Multi-site collaborative monitoring: By deploying at least four atmospheric electric field instrument sites to form a monitoring network and combining radar echo data fusion, dynamic tracking of the spatial path of lightning activity can be achieved, significantly improving the monitoring coverage and the ability to analyze lightning movement trends.

[0038] 2. Efficient signal denoising: The sym5 wavelet function combined with the Rigorous SURE threshold method is used to denoise the original electric field signal, effectively eliminating environmental noise while retaining the electric field mutation characteristics caused by lightning activity. The signal-to-noise ratio is significantly improved (minimizing the mean square error), providing a high-quality data foundation for subsequent analysis.

[0039] 3. Precise warning based on time-series differentials: This method uses differential electric field strength analysis to capture dramatic fluctuations in the time-dependent rate of change of the electric field, setting a primary warning threshold of 0.5 kV / m. This method extends the warning lead time to 50 minutes, and the high concentration of differential values ​​significantly reduces the difficulty of threshold setting and the false alarm rate.

[0040] 4. HHT Transform Enhanced Sensitivity: Based on EEMD decomposition and Hilbert transform, a time-frequency spectrum is generated. Energy thresholds (color mapping) are used to visually display the electric field energy changes at each stage of lightning activity (development, maturity, and extinction). The time-frequency spectrum can separate interference signals from target signals, making it particularly suitable for early identification of weak electric field signals, further reducing missed detections.

[0041] 5. Multi-level warning verification mechanism: Combines differential threshold triggering of primary warning with time-frequency spectrum energy threshold triggering of secondary warning, and integrates radar echo data for spatial verification to form multiple judgment criteria, significantly improving warning accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 The atmospheric electric field instrument site distribution diagram and the ground atmospheric electric field original signal waveform diagram in the present invention;

[0043] Figure 2 A distribution diagram of lightning activity in the present invention;

[0044] Figure 3 This is a characteristic analysis diagram of the change of the first atmospheric electric field value in the present invention;

[0045] Figure 4 The first radar echo lightning activity distribution map of the present invention;

[0046] Figure 5 The second radar echo lightning activity distribution map in the present invention;

[0047] Figure 6 This is a characteristic analysis diagram of the second atmospheric electric field value change in the present invention;

[0048] Figure 7 This is the spectrum diagram of the atmospheric electric field HHT transformation in the present invention. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0050] Reference Figure 1-7 The lightning warning system based on HHT transformation and time series difference method includes the following contents:

[0051] Multi-site atmospheric electric field instrument network: used to collect atmospheric electric field signals in real time;

[0052] Signal processing module: used to perform signal denoising, time series difference analysis and HHT transformation;

[0053] Warning module: used to generate warning information based on the differential threshold and time-frequency spectrum energy threshold, and output it to the display terminal.

[0054] The system also includes:

[0055] Data fusion module: used to perform spatiotemporal superposition of radar echo data and atmospheric electric field data to generate a lightning activity distribution map and display the lightning movement path in real time through a geographic information layer.

[0056] The lightning warning method based on HHT transformation and time series difference method includes the following steps:

[0057] Step S1: collecting the original atmospheric electric field signals monitored by the multi-site atmospheric electric field instrument and performing denoising on the original signals;

[0058] The denoising process in step S1 uses the sym5 wavelet function combined with the Rigorous SURE threshold method, which specifically includes:

[0059] The original signal is decomposed by wavelet, and the noise signal is removed by the Rigorous SURE threshold method to retain the characteristics of the electric field changes caused by lightning activities;

[0060] Step S2: Performing time series differential analysis on the denoised signal, calculating the differential electric field strength, and determining the lightning warning threshold based on the change in the differential electric field strength;

[0061] The calculation formula for the timing difference analysis in step S2 is:

[0062] ΔE(t)=E(t)-E(t-1)

[0063] Where ΔE(t) is the differential electric field intensity, E(t) is the atmospheric electric field intensity at the current moment, and E(t-1) is the atmospheric electric field intensity at the previous moment;

[0064] The lightning warning threshold is when the absolute value of the differential electric field intensity reaches 0.5 kV / m, and the lightning warning lead time is 50 minutes;

[0065] Step S3: Perform HHT transform analysis on the denoised signal to generate a time-frequency spectrum and extract the time-frequency spectrum energy threshold;

[0066] The HHT transformation analysis in step S3 includes:

[0067] The atmospheric electric field signal is decomposed into multiple groups of intrinsic mode functions (IMFs) through ensemble empirical mode decomposition (EEMD);

[0068] Perform Hilbert transform on multiple groups of intrinsic mode function components to generate time-frequency spectrum, and identify lightning activities through energy threshold;

[0069] The time-spectrum energy threshold is the energy intensity corresponding to the occurrence of lightning activity, and the energy value in the time-spectrum diagram intuitively reflects the lightning activity stage through color changes;

[0070] The method also includes a multi-site joint monitoring step:

[0071] Deploy at least four atmospheric electric field instrument sites, forming a monitoring network with each site as the center and a radius of 15 km;

[0072] By integrating multi-site data, the spatial movement path of lightning activities and the characteristics of electric field changes are analyzed;

[0073] Step S4: combining the differential electric field intensity threshold and the time-frequency spectrum energy threshold to provide an early warning of lightning activity;

[0074] The early warning of lightning activity in step S4 includes:

[0075] When the absolute value of the differential electric field intensity reaches 0.5kV / m for the first time, the primary warning is triggered;

[0076] When the spectrum energy value exceeds the preset threshold, a secondary warning is triggered, and the location of lightning activity is verified in combination with radar echo data.

[0077] 1. Data Sources and Research Methods

[0078] 1.1 Quality control of atmospheric electric field data:

[0079] A total of 56 thunderstorm weather atmospheric electric field data were screened in the three years from 2018 to 2020. The data came from the joint monitoring data of the ground atmospheric electric field instrument distributed in Xiaolian Village, Xiaolan Economic Development Zone, Tacheng Township and Nanchang County Station in Nanchang. For details, please refer to the attached manual. Figure 1 , Table 1 shows the main parameter indicators.

[0080]

[0081]

[0082] Table 1

[0083] Affected by factors such as the sensitivity of the atmospheric electric field instrument itself and the installation environment, the measured atmospheric electric field signal is often mixed with some other non-thunderstorm electric field signals, that is, noise signals, which poses great difficulties for the analysis and application of atmospheric electric field data.

[0084] When applying atmospheric electric field data, it is necessary to denoise the original signal, retain the changing trend of the electric field in thunderstorm weather and the electric field changes caused by lightning, and use the wavelet transform analysis method to denoise the electric field signal and perform wavelet transform on the original time series electric field signal.

[0085] In the process of research and analysis, with the help of Matlab platform, after multiple simulation experiments, seven wavelet functions (db5, db10, sym5, coif4, bior3, bior4 and haar3) and four threshold analysis methods (Rigorous SURE, Fixedform threshold, Heuristic SURE and Minimax) were selected to compare and analyze the denoising effects. The results show that:

[0086] Among the four threshold analysis methods, the mean square error of the Rigorous SURE threshold analysis method is the smallest, that is, the noise reduction effect is the best, followed by the Minimax threshold method.

[0087] The sym5 wavelet function has the smallest mean square error of the wavelet transformed signal under the Rigorous SURE threshold analysis method, followed by the db10 wavelet function.

[0088] In summary, both db10 and sym5 wavelet functions can achieve good results, but the Rigorous SURE threshold method of sym5 wavelet analysis is more effective when combined with the threshold analysis method.

[0089] The ground electric field data of the Nanchang County station of the gas electric field instrument from 0:00 to 24:00 on June 30, 2018 were selected as a case study. During this process, lightning activities were relatively frequent, and there was a certain amount of noise superimposed on the signal. Figure 1 As shown in the figure, the above wavelet analysis method is used to denoise the atmospheric electric field signal, and the denoised signal waveform is shown as the solid line in the figure.

[0090] Depend on Figure 1 It can be seen that after the electric field signal is denoised, the overlapping part of the waveform is significantly reduced, it is smoother and highlights the main change trend of the atmospheric electric field during lightning activity.

[0091] It shows that this method can significantly reduce the noise in the signal and play a role in quality control for the subsequent analysis and processing of atmospheric electric field data.

[0092] Lightning data is derived from lightning ground flash data collected in the Nanchang area by the Jiangxi lightning monitoring network. This data includes parameters such as lightning occurrence time, geographic location (longitude and latitude), lightning intensity, lightning steepness, polarity (positive and negative lightning), number of location stations, and lightning current amplitude.

[0093] The radar data is from Nanchang's new generation Doppler weather radar. The radar scanning time is 5 to 6 minutes, the scanning mode is VCP21, and the effective detection range is 230 km.

[0094] 1.2 Research Methods:

[0095] The atmospheric electric field time series difference method can regard the atmospheric electric field that changes with time as a one-dimensional signal. The difference technology is applied to the analysis of atmospheric electric field change characteristics. The atmospheric electric field intensity is used as E, so the atmospheric electric field time series difference change value can be written as

[0096] ▽E(x,y,z)=E t (x,y,z)-E t-1 (x,y,z)

[0097] That is, the time rate of change of the atmospheric electric field, which can well reflect the severity of the change of the atmospheric electric field.

[0098] The HHT transform was designed by Huang E based on the mathematical theory of the famous modern mathematician Hilbert. It is an adaptive time-frequency analysis method that can be well applied to the processing of nonlinear and non-stationary signals. This study of the changing characteristics of the atmospheric electric field has brought new ideas. The method is divided into two steps: Ensemble Empirical Mode Decomposition (EEMD) and Hilbert transform.

[0099] EEMD is a noise-assisted data analysis method that can separate the characteristic mode vectors of the atmospheric electric field and analyze different characteristic mode vectors. The atmospheric electric field signal interfered by white noise can be denoised by the EEMD method.

[0100] In the target data (atmospheric electric field data) X k (t) and add Gaussian white noise sequence n(t) in turn to obtain m groups of composite sequences X k (t)(1,2,3...m), and then use EEMD decomposition to obtain m groups of eigenfunction IMF components.

[0101] The specific principles are:

[0102]

[0103] Z(t)=X(t)+iY(t)=a(t)e iθ(t) (3),

[0104] Phase

[0105]

[0106] Instantaneous frequency

[0107]

[0108] (1) where c iis the IMF component of each layer, X(t) is the real part of the Hilbert transform signal, r n is the trend item;

[0109] (2) where Y(t) is the imaginary part of the Hilbert transform signal;

[0110] (3) Where Z(t) is the Hilbert signal value.

[0111] Time series difference analysis of atmospheric electric field in thunderstorm weather:

[0112] The sample data of 56 thunderstorm days are from the lightning data monitored by the Jiangxi Lightning Monitoring Network from 2018 to 2020. There are lightning activities in the Nanchang area. The atmospheric electric field changes of the four atmospheric electric field meters have large fluctuation amplitudes. A total of 224 time series of atmospheric electric field data after wavelet function transformation are collected. Two examples are selected for atmospheric electric field time series difference analysis. The atmospheric electric field data of other stations and time series are not analyzed one by one in this article, but all sample data are analyzed in detail during the research and analysis process.

[0113] In order to intuitively analyze lightning activity, all radar echo data are converted into rectangular coordinates with the Nanchang radar site location as the origin. At the same time, based on the basic reflectivity data of radar echoes, the radar echo characteristic parameter combined reflectivity CR is calculated through relevant algorithms. This realizes the unified superposition of radar echo characteristic data and lightning data in time (Beijing time) and two-dimensional space, and more intuitively integrates these two types of observation data.

[0114] For details, please refer to the attached manual Figure 2 :

[0115] Figure 2 The ground lightning location points within a radius of 15km in Nanchang area from 0:00 to 5:00 on June 30, 2018, with the latitude and longitude of each atmospheric electric field meter station as the center, are superimposed on the Nanchang radar file geographic information map layer. The time of lightning occurrence in the figure is consistent with the time of radar echo. After superimposing the atmospheric electric field meter position layer, the final result is shown in the figure.

[0116] from Figure 2 As can be seen from (a), the thunderclouds electrified very quickly from 02:15 to 02:16. This is because the charge in the thunderclouds changed suddenly and instantaneously. The lightning occurred in and near the areas with strong radar echoes. The radar echoes near the Xiaolan Economic Development Zone and Nanchang County Station were strong.

[0117] Figure 2 -(b) Moderate lightning moves from east to northwest along with strong radar echoes and approaches Xiaolan Economic Development Zone and Nanchang County Station. Simultaneously, lightning activity occurs near Xiaolian Village.

[0118] Figure 2 -(c) The lightning and strong radar echo continue to move northward until the strong radar echo dissipates and the lightning disappears;

[0119] Figure 2 -(d) shows strong radar echoes and lightning activity near the Xiaolan Economic Development Zone and Xiaolian Village in the northern part of Nanchang area.

[0120] For details, please refer to the attached manual Figure 3 :

[0121] Figure 3 -(a) shows the atmospheric electric field change characteristics before and after the occurrence of lightning within 15 km of each atmospheric electric field instrument from 0:00 to 5:00. Analysis of the superposition results of lightning activity and atmospheric electric field shows that the atmospheric electric field near Xiaolan Economic Development Zone and Xiaolian Village Station changed first, followed by Nanchang County Station, and finally Tacheng Township.

[0122] Analysis of the changing characteristics of the differential electric field at four stations revealed significant changes in both the atmospheric electric field and the differential electric field before the first lightning strike, with the exception of Tacheng Township. Due to the relatively low frequency of lightning near Tacheng Township, the fluctuations in the atmospheric electric field and the differential electric field over time are relatively small. This suggests that combined monitoring of atmospheric electric field instruments at multiple stations can more accurately reflect changes in the atmospheric electric field before and during lightning activity.

[0123] Figure 3 -(a) Figure 3 -(c) is the temporal variation curve of the atmospheric electric field and differential electric field, and superimposes the lightning activities occurring at each time within 15 km of the atmospheric electric field instrument at each station. As can be seen from the figure, when there was no lightning activity near the atmospheric electric field instrument stations from 0:00 to 1:30 on June 30, 2018, the fluctuation amplitude of the electric field time series differential change value was relatively small, and the atmospheric electric field variation curve was relatively stable. Until the time period from 1:30 to 2:00, the atmospheric electric field intensity showed a small fluctuation, indicating that the charge in the nearby thunderstorm cloud caused the change of the atmospheric electric field.

[0124] The time series differential changes of the atmospheric electric field show more obvious changes compared with the original atmospheric electric field in the period from 1:30 to 2:00.

[0125] When conditions are ripe, thunderclouds electrify very quickly. This is because the charge in the thundercloud undergoes a sudden, instantaneous change, which is reflected in the ground-level atmospheric electric field as a pulsed change. The first lightning strike near each atmospheric electric field instrument station occurred at 2:16 a.m., and the interval from the change in the fluctuation amplitude of the electric field time series differential change value to the occurrence of the first lightning strike was approximately 50 minutes, effectively improving the timeliness of lightning warnings. Due to the varying distances between the lightning location and each station, the atmospheric electric field variation characteristics at each station differ significantly, and both the atmospheric electric field and the electric field time series differential change values ​​fluctuate significantly at different times. The variation characteristics of the differential electric field at different time periods during lightning activity are more distinct, and can even undergo drastic changes, effectively reducing the missed alarm rate of lightning warnings.

[0126] from Figure 3 -(b) As can be seen from the atmospheric electric field intensity histogram, the atmospheric electric field intensity values ​​are relatively dispersed, and the distribution range of the atmospheric electric field intensity values ​​before the first lightning occurs in different lightning activities is relatively large. It is difficult to select and determine the lightning warning threshold of the atmospheric electric field, which will affect the accuracy of lightning warning.

[0127] from Figure 3 The -(d) electric field strength histogram shows that the differential electric field strength values ​​are relatively concentrated, narrowing the range of lightning warning thresholds and ensuring relatively accurate determination of the thresholds, which is conducive to improving lightning warning effectiveness. Before a lightning strike, the differential electric field strength fluctuates significantly, with the threshold exceeding 0.5 kV / m. During lightning activity, the differential electric field strength at each station generally fluctuates between -2 kV / m and 2 kV / m.

[0128] For details, please refer to the attached manual Figure 4 and instructions attached Figure 5 :

[0129] Figure 4 、 Figure 5 A total of 515 ground lightning flashes occurred in Nanchang from 14:30 to 17:00 on June 30, 2018, with the latitude and longitude of the atmospheric electric field meters of the four stations as the center and a radius of 15 km, superimposed on the geographic information map layer of the Nanchang radar file.

[0130] As can be seen from the figure, this lightning activity moved from west to northeast along with the strong radar echo, passing through various atmospheric electric field instrument stations along the way.

[0131] For details, please refer to the attached manual Figure 6 :

[0132] Figure 6The graph shows the change curves of the atmospheric electric field and differential electric field over time from 12:00 to 18:00 on June 30, and the lightning activities occurring at various times within 15km of the atmospheric electric field instrument at each station are superimposed. It can be seen from the figure that, by comparing and analyzing the characteristics of the atmospheric electric field and differential electric field change curves, in the development stage of lightning activity, before the first lightning occurred, the electric field change curves of the atmospheric electric field instruments at each station changed relatively slowly and the changes were not obvious, but the differential electric field intensity change curves had obvious fluctuations of different amplitudes, especially the fluctuations that occurred at 12:30 in Xiaolan Economic Development Zone. The differential electric field intensity of the atmospheric electric field instruments at each station reached more than 0.5kv / m at different times. One hour later, the first lightning occurred near each station one after another. The time when the first lightning occurred in Xiaolan Economic Development Zone was 13:31. The lightning activities near other stations are shown in the figure.

[0133] As lightning activity continues to mature, the atmospheric electric field curve changes significantly, and the atmospheric electric field intensity also increases significantly, from 0.21kV / m to 10kV / m. This is a significant change, with relatively dramatic electric field pulses, and even more dramatic changes in differential electric field intensity. The above analysis shows that before a lightning strike, the differential electric field intensity changes significantly, reaching above 0.5kV / m, which serves as the threshold for lightning occurrence.

[0134] During lightning activity, the differential electric field intensity at each station generally fluctuates between -2 kV / m and 2 kV / m. During the lightning activity's extinction phase, the atmospheric electric field curve still exhibits varying degrees of pulse variation, but the differential electric field intensity pulse variation is significantly reduced. An analysis of lightning activity over the past three years, based on observational data from four atmospheric electric field meters, revealed that the characteristics of the electric field curves at each stage of lightning activity vary significantly, with significant variations in the peak electric field intensity at each stage. Furthermore, the atmospheric electric field values ​​for lightning activity of varying intensities also vary significantly. Using atmospheric electric field intensity as a lightning warning threshold is prone to missed and false alarms. The above comprehensive analysis suggests that the atmospheric electric field time-series differential method can effectively address this issue to a certain extent.

[0135] 1. HHT transformation analysis of atmospheric electric field:

[0136] Changes in the atmospheric electric field are closely related to lightning activity. During thunderstorms, the amplitude of the atmospheric electric field increases dramatically as thunderclouds accumulate charge. However, before lightning activity occurs, the atmospheric electric field is relatively small and changes more slowly, resulting in a relatively weak atmospheric electric field signal. Early detection of weak signals amidst ambient noise is crucial for local lightning warnings. Therefore, the HHT transform spectrum is used to further confirm whether there are any atmospheric electric field anomalies within the noisy signal.

[0137] The atmospheric electric field from 0:00 to 5:00 on June 30, 2018 was selected from 56 thunderstorm days as the research object. The atmospheric electric field sample data after wavelet function transformation was subjected to HHT transform analysis. The simulation of the HHT transform of the atmospheric electric field signal was realized through programming. After comparing the simulation results of the atmospheric electric field before and after the lightning occurred and analyzing the lightning data of the same day, it was found that the spectrum based on EEMD Hilbert-Huang transform can more accurately and clearly show the changing characteristics of the atmospheric electric field. After EEMD decomposition, 9 groups of IMF components were obtained.

[0138] The HHT transform time-frequency spectrum of each group of IMF components based on EEMD decomposition is obtained by Hilbert transform:

[0139]

[0140] The purpose of processing and spectral analysis of atmospheric electric field signals is to extract the temporal changes of the signal's spectral content so that the energy or intensity of the signal can be represented simultaneously in time and spectrum.

[0141] from Figure 7 As can be seen in the figure, the change curves of the atmospheric electric field raw signals at each station and the superposition of the atmospheric electric field spectrum based on the EEMD Hilbert-Huang transform fully reveal the spectrum. The Hilbert-Huang transform spectrum is concentrated in the low-frequency range, which can separate the interference source and target source in the atmospheric electric field signal and form the eigenmode vectors of each station. By synthesizing the target source to form the eigenmode vector, the interference signal can be effectively suppressed. Therefore, the change process of the ground atmospheric electric field signal can be more clearly presented in the EEMD Hilbert-Huang transform spectrum. The purple curve represents the change curve of the atmospheric electric field raw signal. The first lightning occurred near the Nanchang County station at 2:16 a.m. The change curve of the raw atmospheric electric field value over time shows that the atmospheric electric field intensity began to rise at 1:30 a.m. and reached its peak at 2:00 a.m., but no lightning occurred. During the development stage of lightning activity, the signal energy in the atmospheric electric field HHT transform spectrum gradually increased, and the change curve of the raw atmospheric electric field intensity also showed oscillation. The first lightning occurred with the sudden drop in the atmospheric electric field intensity. During the mature stage of lightning activity, the atmospheric electric field energy in the time-frequency spectrum is strong, and the atmospheric electric field intensity curve shows large fluctuations. Until the extinction stage of lightning activity, the atmospheric electric field still fluctuates slightly, but the atmospheric electric field signal energy gradually weakens until the lightning activity ends. The atmospheric electric field and time-frequency spectrum at other stations show different changing characteristics at different times.

[0142] from Figure 7-(b) shows that, in addition to the Xiaolan Economic Development Zone, lightning activity continued to occur near the Nanchang County Station, Xiaolian Village, and Tacheng Township after 3:30 AM. However, it is difficult to provide timely lightning warnings based solely on the atmospheric electric field and differential electric field change curve characteristics and thresholds during this period, as the curve fluctuations are relatively gentle and the atmospheric electric field intensity values ​​are low. To address this issue, the HHT transform-based spectrum near the Xiaolan Economic Development Zone during this period exhibits strong energy, effectively addressing the problem of missed warnings caused by warnings based on atmospheric electric field and differential electric field thresholds. Although sudden changes and thresholds in the original atmospheric electric field intensity and differential electric field intensity can effectively predict the occurrence of lightning activity, lightning activity of varying intensities can result in missed and false warnings within a certain period. Therefore, the use of the atmospheric electric field HHT transform spectrum can further improve the accuracy of lightning warnings, as the spectrum based on the EEMD Hilbert-Huang transform more accurately and clearly represents the characteristics of atmospheric electric field changes.

[0143] (1) The wavelet transform analysis method is applied to the denoising of the original atmospheric electric field signal, retaining the electric field changes caused by the lightning activity process.

[0144] By selecting 7 wavelet functions and 4 threshold analysis methods to compare the denoising effects of each analysis method, it was found that the Rigorous SURE threshold method of the sym5 wavelet analysis method has the best effect.

[0145] (2) After denoising the original signal with the help of wavelet analysis, the time difference and HHT transform methods are used to study and analyze the temporal characteristics of the differential atmospheric electric field and the Hilbert-Huang transform time-frequency spectrum based on EEMD during the development, maturity and extinction stages of lightning activity.

[0146] The temporal difference of the atmospheric electric field effectively reflects the temporal rate of change of the atmospheric electric field and the intensity of the change. Moreover, the changing characteristics of the differential electric field at different stages of lightning activity are more obvious than those of the atmospheric electric field.

[0147] Through analysis of 56 lightning activity cases, the study found that the differential electric field strength during non-thunderstorm conditions ranges from -0.02 to 0.02 kV / m. During thunderstorms, the absolute value of 2.0 kV / m can be used as a lightning warning threshold. Using differential electric fields for threshold lightning warnings provides more accurate predictions of lightning occurrence times.

[0148] (3) HHT transform can separate the interference source and target source in the atmospheric electric field signal, form the characteristic mode vector of each station, and effectively suppress the interference signal by synthesizing the target source. Therefore, the change process of the ground atmospheric electric field signal can be more clearly presented in the spectrum diagram based on EEMD Hilbert-Huang transform, which serves as the spectrum energy threshold of HHT transform for lightning activity.

[0149] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A lightning warning system based on HHT transformation and time series difference method, characterized by: Includes the following: Multi-site atmospheric electric field instrument network: used to collect atmospheric electric field signals in real time; Signal processing module: used to perform signal denoising, time series difference analysis and HHT transformation; Warning module: used to generate warning information based on the differential threshold and time-frequency spectrum energy threshold, and output it to the display terminal.

2. The lightning warning system based on HHT transformation and time series difference method according to claim 1 is characterized in that: The system also includes: Data fusion module: used to perform spatiotemporal superposition of radar echo data and atmospheric electric field data to generate a lightning activity distribution map and display the lightning movement path in real time through a geographic information layer.

3. The lightning early warning method based on HHT transformation and time series difference method according to any one of claims 1-2, characterized in that: The following steps are involved: Step S1: collecting the original atmospheric electric field signals monitored by the multi-site atmospheric electric field instrument and performing denoising on the original signals; Step S2: Performing time series differential analysis on the denoised signal, calculating the differential electric field strength, and determining the lightning warning threshold based on the change in the differential electric field strength; Step S3: Perform HHT transform analysis on the denoised signal to generate a time-frequency spectrum and extract the time-frequency spectrum energy threshold; Step S4: combining the differential electric field intensity threshold and the time-frequency spectrum energy threshold to provide an early warning of lightning activity.

4. The lightning early warning method based on HHT transformation and time series difference method according to claim 3 is characterized by: The denoising process in step S1 uses the sym5 wavelet function combined with the Rigorous SURE threshold method, which specifically includes: The original signal is decomposed by wavelet, and the noise signal is removed by the Rigorous SURE threshold method to retain the characteristics of the electric field changes caused by lightning activities.

5. The lightning early warning method based on HHT transformation and time series difference method according to claim 3 is characterized by: The calculation formula for the timing difference analysis in step S2 is: ΔE(t)=E(t)-E(t-1) Where ΔE(t) is the differential electric field intensity, E(t) is the atmospheric electric field intensity at the current moment, and E(t-1) is the atmospheric electric field intensity at the previous moment; The lightning warning threshold is when the absolute value of the differential electric field strength reaches 0.5kV / m, and the lightning warning lead time is 50 minutes.

6. The lightning early warning method based on HHT transformation and time series difference method according to claim 3 is characterized by: The HHT transformation analysis in step S3 includes: The atmospheric electric field signal is decomposed into multiple groups of intrinsic mode functions (IMFs) through ensemble empirical mode decomposition (EEMD); Hilbert transform is performed on multiple groups of intrinsic mode function components to generate time-frequency spectrograms, and lightning activities are identified by energy thresholds.

7. The lightning early warning method based on HHT transformation and time series difference method according to claim 3 is characterized by: The time-spectrum energy threshold is the energy intensity corresponding to the occurrence of lightning activity, and the energy value in the time-spectrum diagram intuitively reflects the stage of lightning activity through color changes.

8. The lightning early warning method based on HHT transformation and time series difference method according to claim 3 is characterized by: The method also includes a multi-site joint monitoring step: Deploy at least four atmospheric electric field instrument sites, forming a monitoring network with each site as the center and a radius of 15 km; By integrating multi-site data, the spatial movement path of lightning activities and the characteristics of electric field changes are analyzed.

9. The lightning early warning method based on HHT transformation and time series difference method according to claim 3 is characterized by: The early warning of lightning activity in step S4 includes: When the absolute value of the differential electric field intensity reaches 0.5kV / m for the first time, the primary warning is triggered; When the spectrum energy value exceeds the preset threshold, a secondary warning is triggered, and the location of lightning activity is verified in combination with radar echo data.

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