CN116643294B - Ionosphere disturbance detection method, device and medium based on double coefficients and double sequences - Google Patents
Ionosphere disturbance detection method, device and medium based on double coefficients and double sequences Download PDFInfo
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Abstract
The invention belongs to the technical field of disturbance detection, and particularly discloses an ionosphere disturbance detection method, device and medium based on double coefficients and double sequences. The method comprises the steps of constructing a background field, wherein the background field is similar to a track to be detected; and extracting ionosphere disturbance according to the difference value between the track to be detected and the background field. The invention utilizes the characteristic that the solar synchronous orbit is fixed and keeps basically stable when passing through the same place and is local, and can lead the historical data to participate in ionosphere disturbance detection more, thereby eliminating some ionosphere anomalies which can occur at the detection place frequently and leading the detection result to be more accurate.
Description
Technical Field
The invention belongs to the technical field of disturbance detection, and particularly relates to an ionosphere disturbance detection method, device and medium based on double coefficients and double sequences.
Background
The magnetic cluster anomaly detection algorithm, the electron density logarithmic algorithm and the monorail electron density anomaly detection algorithm which are developed by Desantis and are based on spline analysis are most commonly used at present for processing magnetic field and plasma data. All three algorithms need to divide the data into tracks firstly, then use polynomial fitting to remove long trend, finally use sliding window to extract according to a certain threshold, and the specific flow is shown in figure 1. The method mainly removes the influence of long trend through various fitting functions, and finally judges whether the method is abnormal or not by selecting a sliding window.
Among the three methods, the electron density logarithmic algorithm (NeLog) and the single-track electron density anomaly detection algorithm (NeSTAD) are detection methods based on in-situ observation data, which are currently applied to ionosphere detection research, and the implementation processes of the two methods are briefly described below.
The input data of the electron density logarithmic algorithm is the Swarm electron density data of 2Hz, and the detection time ranges of one month before and after the earthquake are used for detecting the earthquake pregnancy period and the aftershock period. The detection range is + -5 degrees near the Dobrovelsky area, and the detection track is from 10 hours at night to 6 hours at morning so as to avoid the interference of ionosphere activities such as equatorial ionosphere abnormality. After the track to be detected is selected, taking the logarithm taking 10 as the base of the electron density of the track to be detected; and performing polynomial fitting on the logarithmic latitudinal cross section curve, and taking the fitted curve as a background field for detection. And calculating the overall root mean square error (RMS) of the residual error, taking k times of the intermediate error as a threshold value, and checking the position of which the numerical value is larger than the threshold value in the residual error as an abnormality. To avoid sporadic errors and fitting edge errors, only samples exceeding 10 consecutive outlier points are recorded and outlier results within 5 ° of the edge latitude are not signaled.
The single-track electron density anomaly detection algorithm uses Swarm Langmuir in-situ electron density observation, plasma bubble data, and magnetic storm loop current index (Dst) for ionospheric disturbance detection. The method mainly uses a secondary difference value as a basis for abnormality judgment by calculating:
after traversing the track, a secondary difference sequence can be calculated. Checking the secondary difference sequence using a threshold based on quartiles, the secondary difference being greater than an upper bound (K up ) Or less than the lower limit (K) down ) Is marked as ionospheric disturbance. The definition of the upper and lower bounds is shown, where Q1 and Q3 are the first and third quartiles, respectively, IQR is the quartile range, and k is 1.5 or 3. For the abnormal position of the mark,further screening using the Swarm plasma bubble data is required to exclude interference from the ionosphere itself.
K up ＝Q3+k·IQR；K down ＝Q1-k·IQR (2)
From fig. 2, it can be seen that the ability of neog to detect an abnormality in electron density at night is relatively high. The device has quite strong detection capability no matter the device is positioned at the abnormality of high magnetic latitude or low magnetic latitude; and has certain identification capability for positive and negative anomalies. But the method can only be used at night, and the application range of the method is greatly limited.
NeSTAD is used with electromagnetic satellites other than Swarm. The Swarm ion bubble data product cannot be used, and therefore the method cannot be fully applied to the data products of other satellites such as Zhang Yi. From fig. 3, it can be seen that nastid has a certain detection capability, and a simulated electron density abnormality can be identified. However, the defect is obvious, namely, the defined abnormal range is too large (21% of data of the track to be detected is considered as abnormal), which indicates that the method has a large number of abnormal false identifications and cannot meet the requirement of accurate extraction.
Disclosure of Invention
The present invention has been made to solve the above-mentioned problems occurring in the prior art. Therefore, there is a need for a dual-coefficient and dual-sequence based ionospheric disturbance detection method, device and medium, which are developed for the data products based on the swart constellation, which is a near polar orbit, for the in-situ data ionospheric disturbance detection method of electromagnetic satellites currently in use. The invention can make the history data more participate in ionosphere disturbance detection by utilizing the characteristic, thereby eliminating ionosphere abnormality which can occur frequently at the detection place and making the detection result more accurate.
According to a first aspect of the present invention, there is provided a method for ionospheric disturbance detection based on double coefficients and double sequences, the method comprising:
constructing a background field, wherein the background field is similar to a track to be detected;
and extracting ionosphere disturbance according to the difference value between the track to be detected and the background field.
Further, the construction of the background field specifically includes:
acquiring input data, wherein the input data comprises second-level electron density data of a Langmuir probe number one, time-space information of an event, detected space coverage and a time window;
determining a track to be tested based on the input data;
determining an adjacent track set according to the track to be detected, wherein the connected track set comprises two tracks to be detected and a west track and an east track;
and constructing a background field based on the adjacent track set.
Further, the determining the track to be tested based on the input data specifically includes:
and carrying out track separation on the secondary electron density data of the Langmuir probe number one, wherein each track contains all data from 60 degrees N (S) to 60 degrees S (N) of magnetic latitude, and determining the track to be detected according to the time-space information of the event, the detected space coverage and the time window.
Further, the constructing the background field based on the adjacent track set specifically includes:
for adjacent track sets of each track to be detected, calculating the French coefficients and the Pearson correlation coefficients corresponding to each track in the sets, and respectively forming a French coefficient matrix and a correlation coefficient matrix; the French coefficient matrix or the correlation coefficient matrix is a symmetric matrix with a main diagonal of 1, and the numerical value corresponding to each row/column is the value of the French coefficient or the correlation coefficient of one track in the set and other all tracks;
based on the friendship coefficient matrix and the correlation coefficient matrix, a sequence of G1 to G4 is obtained by the following formulas (3) to (6):
G1＝sum(NFI,1)/n (3)
G2＝NFI(L,:) (4)
G3＝sum(COF,1)/n (5)
G4＝COF(L,:) (6)
wherein n is the number of tracks in the adjacent track set, L is the number corresponding to the track to be detected, sum is the accumulation sum, NFI is the normalized French coefficient, COF is the Pearson correlation coefficient, G1 and G3 sequences represent the similarity degree of the electron density latitudinal profile curve corresponding to each track and other tracks in the set, in the G1 or G3 sequences, the track with larger value is more similar to all other tracks in the set, G2 and G4 represent the similarity degree of the track to be detected and other tracks in the set, and the track with higher value is more similar to the track to be detected;
adding the sequences G1 to G4 to obtain a weight sequence G; and obtaining a background field similar to the track to be tested by taking the serial number with the highest value except the track to be tested.
Further, in calculating the fraiche coefficients and pearson correlation coefficients for all tracks in the set, the method further comprises:
carrying out standardized resampling on the electron density values of the two tracks to enable the data lengths of the two tracks to be the same and corresponding;
the standardized resampling comprises dividing the magnetic latitude of the track into a plurality of fixed grids, taking the magnetic latitude of the center of the grid as the magnetic latitude of the grid, taking the average value of the electron density values in the grid, and taking the average value of the electron density values as the electron density values of the grid.
Further, the extracting ionospheric disturbance according to the difference between the track to be detected and the background field specifically includes:
subtracting the track to be detected from the background field to obtain a residual sequence;
and detecting the residual sequence based on a sliding window, and determining ionosphere abnormity.
Further, before subtracting the track to be measured from the background field to obtain the residual sequence, the method further comprises:
and under the condition that the background field is not in the same year with the track to be detected or the track to be detected is at the night side, carrying out integral translation on the background field to ensure that the median value of the background field is equal to the median value of the track to be detected.
Further, the detecting the residual sequence based on the sliding window, and determining ionospheric anomalies specifically includes:
traversing the residual sequence by utilizing sliding windows, and calculating the ratio of the electron density mean value of the residual in each window to the electron density mean value of the track to be tested to obtain a ratio sequence P;
calculating the correlation coefficient of the track to be detected and the background field by utilizing the sliding window to obtain a correlation coefficient sequence C,
subtracting the ratio sequence P from the correlation coefficient sequence C to obtain a judgment sequence A;
and respectively setting thresholds for the ratio sequence P, the correlation coefficient sequence C and the judgment sequence A, recording the positions smaller than the thresholds in each sequence, and setting zero residual errors of the corresponding positions in residual error sequences to obtain ionosphere anomalies.
According to a second technical scheme of the present invention, there is provided an ionospheric disturbance detection method device based on double coefficients and double sequences, the device comprising:
a construction module configured to construct a background field, the background field being similar to the track to be tested;
and the extraction module is configured to extract the ionospheric disturbance according to the difference value between the track to be detected and the background field.
According to a third aspect of the present invention there is provided a readable storage medium storing one or more programs executable by one or more processors to implement the method as described above.
The invention has at least the following beneficial effects:
the invention detects the sudden disturbance caused by the event outside the ionosphere by using electromagnetic satellite in-situ observation data and extracts the position and the size of the disturbance. The coupling mechanism of events such as earthquake, volcano, tsunami, typhoon, explosion and the like occurring on the earth surface and the ionized layer can be further researched through the extracted disturbance quantity.
The invention utilizes the characteristic that the solar synchronous orbit is fixed and keeps basically stable when passing through the same place and is local, and can lead the historical data to participate in ionosphere disturbance detection more, thereby eliminating some ionosphere anomalies which can occur at the detection place frequently and leading the detection result to be more accurate.
Drawings
FIG. 1 is a flow chart of a prior art method for extracting anomalies from track-by-track analysis;
FIG. 2 is a diagram of the detection results of NeLog on a simulated track under test for 3 months and 7 days 2021;
FIG. 3 is a schematic diagram of the results of daytime simulation of the rail under test on month 4 and 15 of NeSTAD 2019;
FIG. 4 is a flow chart of NCB background field construction in accordance with an embodiment of the present invention;
FIG. 5 is a flow chart of an NCB disturbance extraction method according to an embodiment of the present invention;
FIG. 6 is a graph showing the results of daytime detection of the NCB process 2021, 3 months, 5 days according to an embodiment of the present invention;
fig. 7 is a schematic diagram of the detection result of the NCB method 2020, 1 month and 18 days daytime according to the embodiment of the present invention;
FIG. 8 is a block diagram of an ionospheric disturbance detection device based on dual coefficients and dual sequences in accordance with an embodiment of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the drawings and detailed description to enable those skilled in the art to better understand the technical scheme of the present invention. Embodiments of the present invention will be described in further detail below with reference to the drawings and specific examples, but not by way of limitation. The order in which the steps are described herein by way of example should not be construed as limiting if there is no necessity for a relationship between each other, and it should be understood by those skilled in the art that the steps may be sequentially modified without disrupting the logic of each other so that the overall process is not realized.
In order to better apply in-situ data of a solar synchronous orbit satellite to ionospheric disturbance detection, the embodiment of the invention provides a novel anomaly extraction method, in particular to a double-coefficient and double-sequence based ionospheric disturbance detection method. Hereinafter, the NCB method is abbreviated.
The NCB method is a method for constructing an optimal background field to be detected based on the French coefficient and the correlation coefficient, and calculating double sequences to extract ionosphere disturbance. The NCB method can be divided into two steps, wherein the first step is to construct an NCB background field: searching a background field which is similar to the track to be detected and has a representative property in a specified space-time range; the second step is NCB disturbance extraction method: and extracting ionosphere disturbance according to the difference value between the track to be detected and the background field.
The method can be used completely, and can also be applied independently according to the needs. Researchers can choose to use NCB background field construction method to obtain background field according to specific application cases, and then manually identify ionosphere abnormality; or capturing ionospheric disturbances using an NCB disturbance extraction method on a specified electron density profile. The NCB method is mainly suitable for detecting ionospheric disturbance caused by events with shorter duration such as earthquake, volcano and the like. The method is an abnormal extraction method based on the development of Langmuir No. one secondary electron density products, and can also be suitable for in-situ observation data of other solar synchronous orbit electromagnetic satellites.
Specifically, the method includes steps 1 and 2 described below.
Step 1.NCB background field construction method
The NCB method constructs input data of a background field as follows: the method comprises the steps of weighing space-time information of events such as secondary electron density data of Langmuir probe number one, earthquakes and the like, and detecting space coverage and time window. To reduce the complexity of the subsequent processing, the input tensor one-number data is firstly subjected to track segmentation, and each track contains all data from 60 DEG N (S) to 60 DEG S (N) of magnetic latitude. And determining the track to be detected according to the input event information and the detection range. After all the tracks to be measured are determined, one set of adjacent tracks is calculated for each track to be measured.
The adjacent tracks mean the tracks to be detected and the two tracks facing the west and facing the east, and 5 tracks are arranged in total, so that the tracks in the adjacent track set are ensured to be as close as possible to the tracks to be detected in space, and the first data are tensed and balanced every day. The projection of the track number one on the ground is fixed, i.e. on the same product day of the other years, the corresponding adjacent track can still be searched. The detection year and adjacent tracks in the same time in the two years before and after the detection year are generally selected to jointly form an adjacent track set. After each track to be detected searches for the corresponding adjacent track set, starting track-by-track background field construction, and the whole flow is shown in fig. 4.
For each adjacent track set of the tracks to be tested, calculating the French coefficient and the Pearson correlation coefficient corresponding to each track (including the track itself to be tested) in the set, and respectively forming the French coefficient matrix and the correlation Coefficient (COF) matrix. Before calculating the correlation coefficient, since the number of electron densities contained in each track is generally different, it is necessary to perform normalized resampling on the electron density values of the two tracks so that the data lengths of the two tracks are the same and correspond to each other. Standardized resampling refers to dividing the magnetic latitude where the track is located into a number of fixed grids. Using the magnetic latitude of the grid center as the magnetic latitude of the grid, and taking the average value of the electron density values in the grid as the electron density value of the grid. According to the characteristics of the Zhang Heng satellite, the grid width selected in the research is magnetic latitude 0.2 degrees.
The calculated French coefficient or correlation coefficient matrix is a symmetric matrix with a main diagonal of 1, and the numerical value corresponding to each row/column is the value of the French coefficient or correlation coefficient of one track in the set and other tracks; for example, the Frechet coefficients (2, 4) are Frechet coefficient values of the second track and the fourth track in the adjacent track set. From the two matrices, the G1-G4 sequences can be obtained as shown in the formula. Wherein n is the number of tracks in the adjacent track set, and L is the number corresponding to the track to be tested.
G1＝sum(NFI,1)/n (3)
G2＝NFI(L,:) (4)
G3＝sum(COF,1)/n (5)
G4＝COF(L,:) (6)
The G1 and G3 sequences represent the degree of similarity of the electron density latitudinal profile curve corresponding to each track to other tracks in the set, i.e., in the G1 or G3 sequences, the track with the larger value is more similar to all other tracks in the set. G2 and G4 represent the degree of similarity of the track to be tested to other tracks in the set, the higher the number of tracks being more similar to the track to be tested. Adding G1 to G4 to obtain a final weight sequence G; and taking the serial number with the highest numerical value except the track to be tested, and obtaining a background field which is similar to the track in all the sets and the track to be tested.
Step 2 NCB disturbance extraction method
After the background field construction of the track to be detected is completed, the track to be detected and the background field can be used for carrying out abnormal extraction. If the selected background field is at other years or the track to be measured is at night side, the background field needs to be integrally translated so that the median value of the background field is equal to the median value of the track to be measured. The step can avoid the influence on the subsequent abnormal identification caused by the overlarge integral difference between the track to be detected and the background field.
The residual error can be obtained by subtracting the track to be detected from the background field, and whether the residual error is ionosphere abnormality is checked through a sliding window, wherein the width of the sliding window used in the research is 10 data points. And traversing the residual sequence by using sliding windows, and calculating the ratio of the electron density average value of the residual in each window to the electron density average value of the track to be detected to obtain a ratio sequence P. And simultaneously calculating the correlation coefficient of the track to be detected and the background field by using a sliding window to obtain a correlation coefficient sequence C, and subtracting the two sequences to obtain a final judgment sequence A.
And setting a threshold value for the sequence P, C, A, recording the positions smaller than the threshold value in each sequence, and setting the residual errors of the corresponding positions in the residual error sequence to zero, so as to finally obtain the ionosphere anomaly captured by the NCB disturbance extraction method. The threshold value of each sequence can flexibly change according to the magnetic latitude of the position to be measured and the day and night sides of the track to be measured. The specific flow and threshold settings are shown in fig. 5.
The thresholds of the sequences are respectively denoted as K (P), K (C), and K (a), and the specific arrangement modes are as follows:
when the track is on the day side:
if the magnetic latitude is less than 20 °, K (P) =0.1, K (C) =0.1, K (a) =0.2;
if the magnetic latitude is greater than 20 °, K (P) =0.8, K (C) =0.6, K (a) =1.4;
if the magnetic latitude is greater than 20 ° and less than 40 °, K (P) =0.3, K (C) =0.2, K (a) =0.6.
When the track is at the night side, K (P) =0.6, K (C) =0.2, K (a) =1.4.
In order to better show the technical effect of the NCB method, a simulation experiment for detecting simulation anomalies is designed. The NCB method is used, and an electron density logarithmic algorithm (NeLog) and a single-rail electron density anomaly detection algorithm (NeSTAD) are used for carrying out simulation detection experiments on the ionospheric anomalies which are manually simulated.
Simulation experiments a number of quiet period orbits were selected in the tensegrity number one in situ electron density secondary products 2019 to 2021. The weft profile curve is matched with different simulated ionosphere anomalies to form a simulated event to be tested. The simulation will draw 16 per year for a total of 48 latitudinal profiles from a three year study range. Wherein 4 strips are extracted every season, and 2 strips are respectively extracted on the day side and the night side. The selection of the latitudinal cross section requires manual intervention, and the electron density cross section with relatively calm sun and geomagnetism and relatively stable morphology is selected; the Kp index of the track is less than or equal to 3, and the F10.7 flux is less than or equal to 100. The simulated anomalies are divided into positive anomalies and negative anomalies, the coverage area of the anomalies is about 3 degrees of magnetic latitude, and the corresponding one-number two-level electron density product is about 20 data points. The abnormal amplitude is divided according to the relative difference between the peak/valley value of the simulated abnormality and the electron density value when no abnormality is added, and is divided into three types of low-amplitude abnormality, medium-amplitude abnormality and high-amplitude abnormality. The magnitude of the anomalies added will also vary depending on the magnetic latitude and local time.
The experiment divides the addition position of the abnormality into low magnetic weft abnormality and high magnetic weft abnormality according to the magnetic latitude + -20 degrees as a limit. Each calm weft section is matched with 6 different simulation anomalies in a range of 2 latitudes, and 576 abnormal tracks plus 48 original calm tracks are generated, namely 624 tracks to be tested.
The experiment will evaluate the performance of three methods by three indicators:
1. the accuracy, i.e. the percentage of the number of tracks in the abnormal track to the total number of tracks to be detected, is used to characterize the extraction capacity of the method. The detected abnormal region and the position of the simulated abnormality are smaller than 0.5 degrees of magnetic latitude, namely the detected abnormal region and the position of the simulated abnormality are considered to be extracted correctly.
2. The detection rate, i.e. the percentage of the retrieved abnormal coverage length to the whole track length, is used to characterize the abnormal range extracted by the method. If the detection rate is too large, the method proves that the extraction abnormal range is too large, and the accuracy of the method is low.
3. The false alarm rate is the percentage of the track length which detects abnormality for the calm track to the whole calm track. The method detects too high ionosphere disturbance on the calm orbit, which indicates that the method has lower extraction abnormal precision.
As can be seen from the detection result of the NCB day side shown in fig. 6, even if the simulated anomaly is located near the magnetic equator within 10 ° of the magnetic latitude, the NCB method can perform relatively effective extraction and control the detection rate. As can be seen from fig. 7, even if the simulation abnormality changes the basic form of the track to be measured from unimodal to bimodal, the NCB background field construction method can still avoid the influence of the simulation abnormality, and a unimodal background field is constructed. From the numerical point of view, the constructed background field is also very close to the track to be measured. In a word, the accurate background field construction enables the NCB disturbance extraction method to accurately identify the position of the simulation abnormality and effectively control the detection rate during disturbance extraction.
In fig. 6, it is shown that the NCB method maintains a relatively strong anomaly discrimination even under the unfavorable conditions that the magnetic equator is active and that there is a certain equatorial ionosphere anomaly interference. The electron density peak of the track to be measured with the magnetic latitude of about 0 degrees in FIG. 7 is easily recognized as the electron density abnormality; but very close spikes in the background field also occur. This means that in the adjacent track set of the tracks to be tested, there are a considerable number of tracks that are similar; therefore, the position cannot be considered as ionospheric disturbance caused by an emergency, but rather, the ionospheric abnormality caused by such factors as geomagnetism or the sun. The detection of the solar magnetic equatorial region is always a difficulty in ionosphere disturbance detection, and from the two cases, it can be seen that the NCB method has quite strong disturbance identification capability at the position.
As can be seen from the overall simulation result (table 1) based on the data of Zhang He 1, the detection capability of the NCB method is obviously better than NeSTAD and is equivalent to NeLog; but the electron density logarithmic algorithm can only be used at night. The above experimental results demonstrate that the disturbance detection method developed based on the Swarm data is relatively low in applicability to the tensor data. The NCB method has higher extraction precision and anti-interference capability no matter the whole effect or the specific case, and is an excellent ionosphere anomaly detection method suitable for in-situ observation of the solar synchronous orbit.
Table 1 results of detection by each method in simulation experiments
The embodiment of the invention also provides a device for ionospheric disturbance detection based on double coefficients and double sequences, as shown in fig. 8, the device 800 comprises:
a construction module 801 configured to construct a background field, the background field being similar to the track to be tested;
an extraction module 802 is configured to extract ionospheric disturbances according to the difference between the track under test and the background field.
In some embodiments, the building block is further configured to obtain input data comprising tensegrity number one langmuir probe secondary electron density data, spatiotemporal information of events, spatial coverage of detection, and a time window;
determining a track to be tested based on the input data;
determining an adjacent track set according to the track to be detected, wherein the connected track set comprises two tracks to be detected and a west track and an east track;
and constructing a background field based on the adjacent track set.
In some embodiments, the build module is further configured to:
and carrying out track separation on the secondary electron density data of the Langmuir probe number one, wherein each track contains all data from 60 degrees N (S) to 60 degrees S (N) of magnetic latitude, and determining the track to be detected according to the time-space information of the event, the detected space coverage and the time window.
In some embodiments, the build module is further configured to:
for adjacent track sets of each track to be detected, calculating the French coefficients and the Pearson correlation coefficients corresponding to each track in the sets, and respectively forming a French coefficient matrix and a correlation coefficient matrix; the French coefficient matrix or the correlation coefficient matrix is a symmetric matrix with a main diagonal of 1, and the numerical value corresponding to each row/column is the value of the French coefficient or the correlation coefficient of one track in the set and other all tracks;
based on the friendship coefficient matrix and the correlation coefficient matrix, a sequence of G1 to G4 is obtained by the following formulas (3) to (6):
G1＝sum(NFI,1)/n (3)
G2＝NFI(L,:) (4)
G3＝sum(COF,1)/n (5)
G4＝COF(L,:) (6)
wherein n is the number of tracks in the adjacent track set, L is the number corresponding to the track to be detected, sum is the accumulation sum, NFI is the normalized French coefficient, COF is the Pearson correlation coefficient, G1 and G3 sequences represent the similarity degree of the electron density latitudinal profile curve corresponding to each track and other tracks in the set, in the G1 or G3 sequences, the track with larger value is more similar to all other tracks in the set, G2 and G4 represent the similarity degree of the track to be detected and other tracks in the set, and the track with higher value is more similar to the track to be detected;
adding the sequences G1 to G4 to obtain a weight sequence G; and obtaining a background field similar to the track to be tested by taking the serial number with the highest value except the track to be tested.
In some embodiments, the build module is further configured to:
carrying out standardized resampling on the electron density values of the two tracks to enable the data lengths of the two tracks to be the same and corresponding;
the standardized resampling comprises dividing the magnetic latitude of the track into a plurality of fixed grids, taking the magnetic latitude of the center of the grid as the magnetic latitude of the grid, taking the average value of the electron density values in the grid, and taking the average value of the electron density values as the electron density values of the grid.
In some embodiments, the extraction module is further configured to:
subtracting the track to be detected from the background field to obtain a residual sequence;
and detecting the residual sequence based on a sliding window, and determining ionosphere abnormity.
In some embodiments, the extraction module is further configured to:
and under the condition that the background field is not in the same year with the track to be detected or the track to be detected is at the night side, carrying out integral translation on the background field to ensure that the median value of the background field is equal to the median value of the track to be detected.
In some embodiments, the extraction module is further configured to:
traversing the residual sequence by utilizing sliding windows, and calculating the ratio of the electron density mean value of the residual in each window to the electron density mean value of the track to be tested to obtain a ratio sequence P;
calculating the correlation coefficient of the track to be detected and the background field by utilizing the sliding window to obtain a correlation coefficient sequence C,
subtracting the ratio sequence P from the correlation coefficient sequence C to obtain a judgment sequence A;
and respectively setting thresholds for the ratio sequence P, the correlation coefficient sequence C and the judgment sequence A, recording the positions smaller than the thresholds in each sequence, and setting zero residual errors of the corresponding positions in residual error sequences to obtain ionosphere anomalies.
It should be noted that, the device provided in this embodiment and the method set forth in the foregoing belong to the same technical idea, and are limited to the same working principle, and have the same beneficial effects, which are not repeated here.
Embodiments of the present invention provide a readable storage medium storing one or more programs executable by one or more processors to implement the ionospheric disturbance detection method based on dual coefficients and dual sequences as described in any of the embodiments above.
Furthermore, although exemplary embodiments have been described herein, the scope thereof includes any and all embodiments having equivalent elements, modifications, omissions, combinations (e.g., of the various embodiments across), adaptations or alterations as pertains to the present invention. Elements in the claims are to be construed broadly based on the language employed in the claims and are not limited to examples described in the present specification or during the practice of the present application, which examples are to be construed as non-exclusive. It is intended, therefore, that the specification and examples be considered as exemplary only, with a true scope and spirit being indicated by the following claims and their full scope of equivalents.
The above description is intended to be illustrative and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. For example, other embodiments may be used by those of ordinary skill in the art upon reading the above description. In addition, in the above detailed description, various features may be grouped together to streamline the invention. This is not to be interpreted as an intention that the features of the claimed invention are essential to any of the claims. Rather, inventive subject matter may lie in less than all features of a particular inventive embodiment. Thus, the following claims are hereby incorporated into the detailed description as examples or embodiments, with each claim standing on its own as a separate embodiment, and it is contemplated that these embodiments may be combined with one another in various combinations or permutations. The scope of the invention should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.
Claims (6)
1. An ionospheric disturbance detection method based on double coefficients and double sequences, the method comprising:
constructing a background field, wherein the background field is similar to a track to be detected;
extracting ionosphere disturbance according to the difference value between the track to be detected and the background field;
the construction of the background field specifically comprises the following steps:
acquiring input data, wherein the input data comprises second-level electron density data of a Langmuir probe number one, time-space information of an event, detected space coverage and a time window;
determining a track to be tested based on the input data;
determining an adjacent track set according to the track to be detected, wherein the adjacent track set comprises the track to be detected and two tracks facing west and east respectively;
constructing a background field based on the adjacent track set;
the construction of the background field based on the adjacent track set specifically comprises the following steps:
for adjacent track sets of each track to be detected, calculating the French coefficients and the Pearson correlation coefficients corresponding to each track in the sets, and respectively forming a French coefficient matrix and a correlation coefficient matrix; the French coefficient matrix or the correlation coefficient matrix is a symmetric matrix with a main diagonal of 1, and the numerical value corresponding to each row/column is the value of the French coefficient or the correlation coefficient of one track in the set and other all tracks;
based on the friendship coefficient matrix and the correlation coefficient matrix, a sequence of G1 to G4 is obtained by the following formulas (3) to (6):
G1＝sum(NFI，1)/n (3)
G2＝NFI(L，：) (4)
G3＝sum(COF，1)/n (5)
G4＝COF(L，：) (6)
wherein n is the number of tracks in the adjacent track set, L is the number corresponding to the track to be detected, sum is the accumulation sum, NFI is the normalized French coefficient, COF is the Pearson correlation coefficient, G1 and G3 sequences represent the similarity degree of the electron density latitudinal profile curve corresponding to each track and other tracks in the set, in the G1 or G3 sequences, the track with larger value is more similar to all other tracks in the set, G2 and G4 represent the similarity degree of the track to be detected and other tracks in the set, and the track with higher value is more similar to the track to be detected;
adding the sequences G1 to G4 to obtain a weight sequence G; the serial number with the highest numerical value except the track to be detected is taken to obtain a background field similar to the track to be detected;
the ionosphere disturbance is extracted according to the difference value between the track to be detected and the background field, and the method specifically comprises the following steps:
subtracting the track to be detected from the background field to obtain a residual sequence;
detecting the residual sequence based on a sliding window, and determining ionospheric anomalies;
the method for determining ionosphere anomalies based on the residual sequence detected by the sliding window specifically comprises the following steps:
traversing the residual sequence by utilizing sliding windows, and calculating the ratio of the electron density mean value of the residual in each window to the electron density mean value of the track to be tested to obtain a ratio sequence P;
calculating the correlation coefficient of the track to be detected and the background field by utilizing the sliding window to obtain a correlation coefficient sequence C;
subtracting the ratio sequence P from the correlation coefficient sequence C to obtain a judgment sequence A;
and respectively setting thresholds for the ratio sequence P, the correlation coefficient sequence C and the judgment sequence A, recording the positions smaller than the thresholds in each sequence, and setting zero residual errors of the corresponding positions in residual error sequences to obtain ionosphere anomalies.
2. The method according to claim 1, wherein determining the track to be measured based on the input data comprises:
and carrying out track separation on the secondary electron density data of the Langmuir probe number one, wherein each track contains all data from 60 degrees N to 60 degrees S of magnetic latitude, and determining the track to be detected according to the time-space information of the event, the detected space coverage and the time window.
3. The method of claim 1, wherein in computing the frachment coefficients and pearson correlation coefficients for all tracks in the set for each pair, the method further comprises:
carrying out standardized resampling on the electron density values of the two tracks to enable the data lengths of the two tracks to be the same and corresponding;
the standardized resampling comprises dividing the magnetic latitude of the track into a plurality of fixed grids, taking the magnetic latitude of the center of the grid as the magnetic latitude of the grid, taking the average value of the electron density values in the grid, and taking the average value of the electron density values as the electron density values of the grid.
4. The method of claim 1, wherein prior to subtracting the track to be measured from the background field to obtain the residual sequence, the method further comprises:
and under the condition that the background field is not in the same year with the track to be detected or the track to be detected is at the night side, carrying out integral translation on the background field to ensure that the median value of the background field is equal to the median value of the track to be detected.
5. Ionospheric disturbance detection device based on double coefficients and double sequences, applied to the method according to any of claims 1 to 4, characterized in that it comprises:
a construction module configured to construct a background field, the background field being similar to the track to be tested;
and the extraction module is configured to extract the ionospheric disturbance according to the difference value between the track to be detected and the background field.
6. A readable storage medium storing one or more programs executable by one or more processors to implement the method of any of claims 1-4.
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