A method and system for identifying ionospheric irregularities

Abstract

The application discloses a method and system for identifying ionospheric irregularities, and the method comprises the following steps: pre-processing the night disk observation data by using a data integrity check and invalid value removal method, obtaining an effective radiation data set, and extracting a fixed-wavelength radiation data set in the southern and northern hemispheres from the effective radiation data set respectively; merging the fixed-wavelength radiation data sets in the southern and northern hemispheres, and drawing a radiation distribution map by combining a two-dimensional grid interpolation method; identifying ionospheric irregularities by using a radiation difference threshold identification method on the radiation distribution map to obtain a preliminary ionospheric irregularity identification result; and matching the preliminary identification result with total electron content data, calculating and determining a final ionospheric irregularity identification result. The method does not depend on densely distributed ground-based GNSS receiving stations, can significantly reduce the calculation complexity, and improves the processing efficiency.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of satellite navigation and ionosphere, and more particularly, relates to a method and system for identifying ionospheric irregularities. BACKGROUND

[0002] The ionosphere is a region of the Earth's atmosphere, about 60-1000 kilometers from the ground, rich in free electrons and ions, formed by solar ultraviolet and X-ray ionization. It has a layered structure (D, E, F layers), and has an important influence on radio communication, navigation systems and satellite signals. The ionosphere is dynamically regulated by solar activity, geomagnetic disturbances and atmospheric gravity waves, and often exhibits phenomena such as equatorial ionospheric anomalies and equatorial plasma bubbles. Research on the ionosphere is crucial for space weather warning, satellite positioning correction (such as GPS) and communication.

[0003] Equatorial Plasma Bubble (EPB) is a plasma density depletion structure that occurs in the F layer of the ionosphere (about 150-800 km altitude), extending vertically like a "bubble", usually occurring after sunset to midnight. Its cause is related to Rayleigh-Taylor instability, when low-layer high-density plasma passes through the high-layer low-density region under the action of gravity, causing density perturbations and developing upward, forming large-scale irregularities (scale up to hundreds of kilometers). EPB can significantly interfere with radio signals (such as GPS, satellite communication), causing ionospheric scintillation and signal delay, affecting navigation and communication systems. Its activity is modulated by solar activity, geomagnetic conditions and lower atmospheric wave fluctuations, and is particularly active during magnetic storms. Research on EPB is of great significance for space weather forecasting and ionospheric disturbances.

[0004] Equatorial Ionization Anomaly (EIA) is a unique double-peak structure phenomenon in the ionosphere near the equator, characterized by the presence of electron density enhancement bands on both sides of the magnetic equator at about ±15° latitude (north and south latitude), while the upper equatorial space forms a low-density valley. Its cause is mainly the fountain effect of the equatorial electric field: the daytime equatorial electric field drives the plasma to rise vertically, then diffuses along the magnetic field lines to both sides and sinks, which has an important influence on global satellite navigation system (GNSS) signal propagation and shortwave communication.

[0005] At present, there are many problems in the identification technology of ionospheric irregularities using low-orbit satellites, and the most prominent problem is that the existing model based on carrier phase and Doppler framework training has data missing in IGS station area, single identification point and only observing at a certain time point, and the identification based on image needs multiple processing, which is complex and affects the processing efficiency and accuracy.

[0006] The prior art patent with publication number CN118731996A proposes a new ionospheric three-dimensional disturbance monitoring method, which obtains the three-dimensional electron density distribution of the ionosphere by inversion of ground-based GNSS observation data, combines the spatial electron density changes at different times, constructs the ROI (Rate of Ionospheric Electron Density) disturbance index, and identifies and monitors the traveling ionospheric disturbance (TID) in the ionosphere, realizes the judgment of the three-dimensional spatial position, change rate and trend of TID, and the method relies on densely distributed ground-based GNSS receiving stations, and the spatial resolution and inversion accuracy are significantly limited in areas where GNSS is insufficient, and it is difficult to realize global or regional wide-range rapid monitoring, secondly, the method needs to complete STEC solving, voxel construction and multi-iteration inversion, and the calculation amount is large, the real-time performance is poor, and it is difficult to meet the dynamic identification demand of high time resolution. SUMMARY

[0007] The present application is to overcome the problems in the prior art that the ionospheric irregularity identification technology relies on densely distributed ground-based GNSS receiving stations, has large calculation amount and poor real-time performance, and is difficult to meet the dynamic identification demand of high time resolution, and provides a method and system for identifying ionospheric irregularities.

[0008] The primary object of the present application is to solve the above technical problems, and the technical solution of the present application is as follows:

[0009] The present application provides a method for identifying ionospheric irregularities, comprising the following steps:

[0010] The night disk observation data is preprocessed by using the data integrity check and invalid value removal method to obtain an effective radiation data set;

[0011] The fixed wavelength radiation data set of the southern hemisphere and the fixed wavelength radiation data set of the northern hemisphere are extracted from the effective radiation data set respectively; the fixed wavelength radiation data set of the southern hemisphere and the fixed wavelength radiation data set of the northern hemisphere are merged to obtain a merged radiation data set; and the radiation distribution map is drawn by using the two-dimensional grid interpolation method;

[0012] The ionospheric irregularity identification is performed on the radiation distribution map by using the radiation difference threshold identification method to obtain the preliminary identification result of the ionospheric irregularity;

[0013] The final ionospheric irregularity identification result is calculated and determined by matching the preliminary identification result with total electron content data.

[0014] Further, the fixed wavelength is 135.6 nm.

[0015] Further, the night disk observation data is L1C-NI1 data downloaded from a global-scale earth edge and disk observation satellite, which includes two channel data of a north hemisphere channel and a south hemisphere channel.

[0016] Further, the night disk observation data is preprocessed by using a data integrity check and invalid value removal method, including the following steps:

[0017] The complete data set of the north hemisphere channel and the south hemisphere channel at the same time point is obtained by using channel record number comparison combined with NetCDF file format verification;

[0018] Invalid values in the complete data set are removed, and observation data with non-empty radiation values are screened out;

[0019] The screened observation data is subjected to Gaussian denoising processing, and the cleaned effective radiation data set is output.

[0020] Further, the method for extracting south hemisphere fixed wavelength radiation data and north hemisphere fixed wavelength radiation data from the effective radiation data set, including the following steps:

[0021] The target wavelength parameter is combined with the wavelength list of the north hemisphere channel and the south hemisphere channel to screen out the actual wavelength value closest to the target wavelength in the two channels;

[0022] The determined actual wavelength value is input into the channel data calling function to extract the radiation value observation records of the corresponding wavelength in the south hemisphere data file and the north hemisphere data file, and output the preliminary extraction data set;

[0023] The preliminary extraction data set is spatially grouped according to the latitude direction and the longitude direction, and the radiation values in the group are summed to obtain the radiation total amount of each group;

[0024] The radiation total amount of each group and the latitude and longitude are stored as the south hemisphere fixed wavelength radiation data set and the north hemisphere fixed wavelength radiation data set.

[0025] Further, the method for drawing a radiation distribution map by using the merged radiation data set combined with a two-dimensional grid interpolation method, including the following steps:

[0026] The latitude and longitude indexes in the merged radiation data set are used to calculate and determine the node coordinates of the two-dimensional regular latitude and longitude grid by using a grid construction method;

[0027] Interpolating the corresponding radiation value into a two-dimensional regular latitude and longitude grid to obtain a radiation value grid;

[0028] Superimposing the radiation value grid with geographic elements to output a radiation distribution map containing geographic reference information.

[0029] Further, the method for identifying ionospheric irregularities using the radiation distribution map comprises the following steps:

[0030] Using the radiation distribution map, combined with the grid traversal method, the radiation value of each grid point and the difference of radiation values in its neighborhood are calculated and determined;

[0031] The radiation difference obtained by traversal is compared with the preset threshold value, and when the radiation value of a certain grid point decreases by more than the preset threshold value relative to the high-intensity background area where it is located, it is determined that this area is a dark band area corresponding to the ionospheric irregularity, and the identification result is output.

[0032] Further, the preliminary identification result is matched with the total electron content data to calculate and determine the final ionospheric irregularity identification result, comprising the following steps:

[0033] Using the preliminary ionospheric irregularity identification result to determine the candidate radiation dark band area set A;

[0034] Using the observation data of IGS sites combined with ephemeris files and double-frequency pseudorange difference method to calculate the TEC value of each ionospheric piercing point to obtain TEC grid dataset , the calculation formula is as follows:

[0035]

[0036] Where, TEC represents the total electron content, is the difference of double-frequency pseudorange observation values, , L1 and L2 are the frequencies of the two signals emitted from the same satellite at the same time, and the constant 1 / 40.3 is the ionospheric formula coefficient;

[0037] Spatial projection is performed on set A, combined with the assumed ionospheric height to obtain the projected image grid , where x represents longitude, y represents latitude, and t represents observation time;

[0038] The TEC grid is projected onto the same ionosphere to obtain an ionospheric electron content distribution map aligned in time and space with ;

[0039] The is paired with the corresponding pixels, and the radiation contrast feature matrix is calculated respectively TEC level gradient model feature matrix , the expression is as follows:

[0040]

[0041]

[0042] wherein, is the current point radiance, is the corresponding local neighborhood average radiance;

[0043] According to the feature matrix, a consistency score function is calculated, and the expression is as follows:

[0044]

[0045] wherein, is a weight balance coefficient;

[0046] The In a continuous time window, the mean and the standard deviation

[0047] An adaptive threshold is constructed, and the expression is as follows:

[0048]

[0049] Wherein, k is a confidence factor, used to control the sensitivity of the output;

[0050] The is compared with point by point, when ≥ , the area is output as the final ionospheric irregularity recognition result; when < , the candidate area is marked as pending or rejected, and the final recognition area set meeting the confidence condition is obtained as the final ionospheric irregularity recognition result.

[0051] The second aspect of the application provides a system for identifying ionospheric irregularities, comprising a memory and a processor, the memory comprising a method program for identifying ionospheric irregularities, and the method program for identifying ionospheric irregularities is executed by the processor to realize the steps of the method for identifying ionospheric irregularities.

[0052] The third aspect of the present application provides a computer readable storage medium, wherein a method for identifying ionospheric irregularities is included in the computer readable storage medium, and the method for identifying ionospheric irregularities comprises the following steps.

[0053] Compared with the prior art, the beneficial effects of the technical scheme of the present application are as follows:

[0054] The present application makes full use of the high temporal resolution and wide spatial coverage characteristics of global-scale earth edge and disc surface observation satellite data, extracts radiation features through image processing to achieve preliminary identification, and further combines the total electron content data obtained by IGS site inversion to verify spatial consistency and evaluate confidence, thereby effectively supplementing the blind area of ground-based observation and improving the accuracy and robustness of the identification result. Compared with the traditional scheme, the present application does not need complex voxel grid construction and iterative inversion algorithm, significantly reduces the computational complexity, and improves the processing efficiency. Based on the data fusion identification mechanism of the present application, the complete evolution process of ionospheric irregularities can be clearly depicted, and multi-region and multi-target parallel monitoring is supported, which has stronger practicability, scalability and deployment flexibility. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to make the purpose, technical scheme of the present application more clear, the present application provides the following drawings and makes the following description:

[0056] Figure 1 The method flowchart provided for the embodiment of the present application;

[0057] Figure 2 The data processing flowchart provided for the embodiment of the present application;

[0058] Figure 3 The 135.6nm wavelength radiation data image obtained on October 11, 2024, 00:10UT, provided for the embodiment of the present application;

[0059] Figure 4 The 135.6nm wavelength radiation data image obtained on October 12, 2024, 00:10UT, provided for the embodiment of the present application;

[0060] Figure 5 The 135.6nm radiation value data and TEC data fusion processing schematic diagram on October 11, 2024, 00:10UT, provided for the embodiment of the present application;

[0061] Figure 6 The 135.6nm radiation value data and TEC data fusion processing schematic diagram on October 12, 2024, 00:10UT, provided for the embodiment of the present application. DETAILED DESCRIPTION

[0062] In order to enable a more complete understanding of the above-mentioned objects, features and advantages of the present application, the present application will be described in further detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0063] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application. However, the present application can be practiced in other manners different from those described herein, and the scope of the present application is not limited to the specific embodiments disclosed below.

[0064] Embodiment 1

[0065] The present application provides a method for identifying ionospheric irregularities, as shown in Figure 1 The present application provides a method for identifying ionospheric irregularities, as shown in Figure 2 The present application provides a method for identifying ionospheric irregularities, as shown in

[0066] The specific steps are as follows:

[0067] S1: Preprocess the night disk observation data using the data integrity check and invalid value removal method to obtain a set of valid radiation data.

[0068] More specifically, the night disk observation data is L1C-NI1 data downloaded from the Global Scale Earth Limb and Disk Observing Satellite (GOLD), which includes data from the north hemisphere channel (CHA) and the south hemisphere channel (CHB).

[0069] The preprocessing of the night disk observation data using the data integrity check and invalid value removal method includes the following steps:

[0070] Using channel record number comparison combined with NetCDF file format verification, obtain a complete data set of the north hemisphere channel (CHA) and the south hemisphere channel (CHB) at the same time point;

[0071] Remove invalid values in the complete data set and select observation data with non-empty radiation values (RADIANCE);

[0072] Perform Gaussian denoising on the selected observation data to output a set of cleaned valid radiation data.

[0073] S2: Extracting the fixed-wavelength radiation data set of the southern hemisphere and the fixed-wavelength radiation data set of the northern hemisphere from the effective radiation data set respectively; merging the fixed-wavelength radiation data set of the southern hemisphere and the fixed-wavelength radiation data set of the northern hemisphere to obtain a merged radiation data set; and drawing a radiation distribution map by combining a two-dimensional grid interpolation method.

[0074] More specifically, the fixed wavelength is 135.6 nm, and the method for extracting the fixed-wavelength radiation data of the southern hemisphere and the fixed-wavelength radiation data of the northern hemisphere from the effective radiation data set comprises the following steps:

[0075] Using the target wavelength (CHOSEN_WAVELENGTH) parameter (target wavelength 135.6 nm) in combination with the wavelength list of the northern hemisphere channel (CHA) and the southern hemisphere channel (CHB), the actual wavelength value closest to the target wavelength in the two channels is screened out;

[0076] The determined actual wavelength value is input into a channel data calling function to extract the corresponding wavelength radiation value (RADIANCE) observation record in the southern hemisphere data file and the northern hemisphere data file respectively, and output a preliminary extraction data set;

[0077] The preliminary extraction data set is spatially grouped according to the n_ns index in the latitude direction and the n_ew index in the longitude direction, and the values (RADIANCE) in the group are summed to obtain the total radiation of each group;

[0078] The total radiation of each group and the latitude and longitude are converted into NumPy arrays and stored as the fixed-wavelength radiation data set of the southern hemisphere and the fixed-wavelength radiation data set of the northern hemisphere.

[0079] The method for drawing a radiation distribution map using the merged radiation data set in combination with a two-dimensional grid interpolation method comprises the following steps:

[0080] Using the latitude and longitude indexes in the merged radiation data set in combination with a grid construction method, the node coordinates of a two-dimensional regular latitude and longitude grid are calculated and determined;

[0081] The corresponding wavelength radiation value (RADIANCE) is interpolated into the two-dimensional regular latitude and longitude grid to obtain a radiation value grid;

[0082] The radiation value grid is superimposed with geographical elements such as a map base map and a coastline to output a radiation distribution map containing geographical reference information.

[0083] As Figure 3 and Figure 4The image shows 135.6 nm wavelength radiation data images acquired by the GOLD satellite during the geomagnetic storm at 00:10UT on October 11, 2024, and during the recovery phase of the geomagnetic storm at 00:10UT on October 12, 2024, corresponding to 19:10LT local time, which falls within the time window of the equatorial plasma bubble (EPB) (manifested as dark stripes in the GOLD image). The data in the image is based on the combined observation results from the GOLD satellite's CHA and CHB channels in the Northern and Southern Hemispheres, and was plotted after unified gridding, enabling wide-area, continuous observation of the global nightside region.

[0084] like Figure 3 As shown, a significant dark band extending north-south from the equator appears in western South America. This dark band represents a decrease in radiation brightness of more than 50% relative to the background brightness, consistent with typical EPB image characteristics, indicating the presence of a super EPB structure in this region. Its latitudinal extension reflects the spatial coverage capability of the EPB, and its morphological structure also demonstrates the dynamic process of EPB development. Simultaneously, regions of significantly enhanced brightness are visible on both sides of the equator in the image, corresponding to the bimodal band formed by the equatorial ionization anomaly (EIA), exhibiting a radiation enhancement effect in high electron density regions.

[0085] like Figure 4 As shown, EPB cluster activity was observed during the same time period after sunset, manifesting as multiple densely arranged dark stripes in the image. Such phenomena are difficult to observe comprehensively using traditional methods such as single-point GNSS, SWARM, or radar. However, by merging observations from the GOLD satellite's CHA and CHB channels in both the Northern and Southern Hemispheres, the entire study area can be covered at once, enabling complete image identification of ionospheric disturbances. Combined with the minute-level temporal resolution of the GOLD satellite, processing continuous image sequences allows for the tracking and analysis of the entire process of ionospheric irregularity generation, evolution, merging, and decay, significantly improving the efficiency of identifying ionospheric dynamics and enhancing spatiotemporal awareness.

[0086] S3: Ionospheric irregularities are identified by using the radiation difference threshold identification method on the radiation distribution map. The location of the region with significantly reduced radiation intensity is used as the preliminary identification result of the ionospheric irregularities.

[0087] The specific process is as follows:

[0088] Using a radiation distribution map and a grid traversal method, the difference between the radiation value of each grid point and the radiation values ​​in its neighborhood is calculated and determined.

[0089] The radiation difference obtained by traversing is compared with a preset threshold value. When the radiation value of a certain grid point decreases by more than the preset threshold value (in this embodiment, the value is 50%) relative to the highlight background area where the grid point is located, the area is determined as a dark band area corresponding to the ionospheric irregularity, and an identification result is output.

[0090] S4: The preliminary identification result is matched with total electron content (TEC) data calculated based on observation data of an IGS site and an ephemeris file, and a final ionospheric irregularity identification result is calculated and determined.

[0091] The specific process of calculating and determining the final ionospheric irregularity identification result is as follows:

[0092] A candidate radiation dark band area set A is determined using the preliminary ionospheric irregularity identification result.

[0093] TEC values of each ionospheric piercing point (IPP) are calculated using observation data of an IGS site in combination with an ephemeris file and a dual-frequency pseudorange difference method, and a TEC grid data set is obtained The meaning of the piercing point is that the signal emitted by a GPS satellite intersects with the signal path at a certain assumed height (usually 300 km) when penetrating the ionosphere. The TEC value calculation formula is as follows:

[0094]

[0095] In the formula, TEC represents total electron content, is a dual-frequency pseudorange observation value difference, , L1 and L2 are the frequencies of the two signals emitted from the same satellite at the same time, and 1 / 40.3 is an ionospheric formula coefficient.

[0096] The set A is spatially projected, and a projected image grid is obtained in combination with an assumed ionospheric height In the formula, x represents longitude, y represents latitude, and t represents observation time.

[0097] The TEC grid is projected at the same ionospheric height, and an ionospheric electron content distribution map aligned in time and space is obtained

[0098] The is paired with the , and radiation contrast feature matrices and TEC horizontal gradient modulus feature matrices are respectively calculated, and the expressions are as follows:

[0099] ​

[0100]

[0101] wherein, is the current point radiant brightness, is the corresponding local neighborhood average radiant brightness;

[0102] According to the feature matrix, a consistency score function is calculated , and the expression is as follows:

[0103]

[0104] wherein, is a weight balance coefficient, and in the embodiment, the value is 0.4;

[0105] The is statistically calculated in a continuous time window, and the mean value and the standard deviation

[0106] The adaptive threshold value is constructed, and the expression is as follows:

[0107]

[0108] wherein, k is a confidence factor, and in the embodiment, the value is 1.5, which is used to control the sensitivity of the output;

[0109] The is compared with point by point, when ≥ , the area is output as the final ionospheric irregularity recognition result; when < , the candidate area is marked as pending or rejected, and the final recognition area set E(t) meeting the confidence condition is obtained as the final ionospheric irregularity recognition result.

[0110] Optionally, the set E(t) can be Kalman filtered and updated with the output E(t−1) of the previous moment, the current frame recognition boundary is smoothed, and the final ionospheric irregularity recognition result is output.

[0111] As shown in Figure 5 , the GOLD satellite OI 135.6nm wavelength radiation data and TEC data fusion processing schematic diagram drawn by the application embodiment in the experiment at the selected time point October 11, 2024 00:10UT is shown, and as shown in Figure 6The fusion processing diagram corresponding to 2024-10-12 00:10 UT is shown. Specifically, the TEC distribution result is obtained by solving the observation data of the IGS station covering the area, and the result is matched in time and space with the wavelength radiation data extracted from the two channels of the GOLD satellite CHA and CHB, and is jointly plotted.

[0112] As shown in the prior art, Figure 5 As shown in the prior art, in the super-EPB region identified by the GOLD image, the TEC value is obviously depleted, indicating that the electron density in the region decreases significantly, which is consistent with the physical characteristics of the EPB event; at the same time, in the EIA peak region, the TEC data presents a high value, which is consistent with the distribution of the bright band in the GOLD wavelength radiation image, verifying the correlation between the light and dark structures in the GOLD image and the electron density distribution.

[0113] As shown in the prior art, Figure 6 As shown in the prior art, the RADIANCE and TEC data fusion image plotted at 2024-10-12 00:10 UT shows that the position of the EPB dark stripe identified in the GOLD image corresponds to a synchronous decrease in the TEC value, showing spatial consistency; in the EIA region with enhanced brightness, the TEC value is also at a high level. The above results show that the characteristic regions of EPB and EIA in the GOLD satellite image are effectively verified in the TEC spatial distribution, fully verifying the accuracy and reliability of the ionospheric irregularity identification method proposed in the present application in terms of spatial correspondence and physical consistency.

[0114] The present application makes full use of the high temporal resolution and wide spatial coverage characteristics of global-scale earth edge and disc observation satellite data, extracts radiation features through image processing to achieve preliminary identification, and further combines the total electron content data obtained by IGS site inversion to verify spatial consistency and confidence, thereby effectively supplementing the blind area of ground-based observation and improving the accuracy and robustness of the identification result. Compared with the traditional scheme, the present application does not need complex voxel grid construction and iterative inversion algorithm, significantly reduces the computational complexity, and improves the processing efficiency. Based on the data fusion identification mechanism of the present application, the complete evolution process of the ionospheric irregularity can be clearly depicted, and the parallel monitoring of multiple regions and multiple targets is supported, which has stronger practicality, scalability and deployment flexibility.

[0115] Embodiment 2:

[0116] The embodiment provides a system for identifying ionospheric irregularities, comprising a memory and a processor, wherein the memory comprises a method program for identifying ionospheric irregularities, and the method program for identifying ionospheric irregularities is executed by the processor to realize the steps of the method for identifying ionospheric irregularities as described in embodiment 1.

[0117] Example 3:

[0118] The embodiment provides a computer readable storage medium, wherein the computer readable storage medium comprises a method program for identifying ionospheric irregularities, and the method program for identifying ionospheric irregularities is executed by a processor to implement steps of a method for identifying ionospheric irregularities as described in the embodiment 1.

[0119] Obviously, the above embodiments of the present application are merely exemplary and are not intended to limit the embodiments of the present application. Based on the above description, any modification, equivalent replacement and improvement made by those skilled in the art within the spirit and principle of the present application shall be included in the protection scope of the claims of the present application.

Claims

1. A method of identifying ionospheric irregularities, characterized by, The method comprises the following steps: The night disk observation data is preprocessed by using a data integrity check and invalid value removal method to obtain an effective radiation data set; From the effective radiation data set, a south hemisphere fixed wavelength radiation data set and a north hemisphere fixed wavelength radiation data set are extracted respectively; The south hemisphere fixed wavelength radiation data set and the north hemisphere fixed wavelength radiation data set are merged to obtain a merged radiation data set; and a radiation distribution map is drawn by combining a two-dimensional grid interpolation method; The radiation distribution map is identified by using a radiation difference threshold identification method to obtain a preliminary ionospheric irregularity identification result, which comprises the following steps: The radiation value of each grid point and the radiation value in the neighborhood thereof are calculated and determined by using the radiation distribution map and combining a grid traversal method; The radiation difference obtained by traversal is compared with a preset threshold value; when the radiation value of a certain grid point is reduced by more than the preset threshold value relative to the high-intensity background region in which the grid point is located, it is determined that the region is a dark band region corresponding to the ionospheric irregularity, and the identification result is output; The preliminary ionospheric irregularity identification result is matched with total electron content data to calculate and determine a final ionospheric irregularity identification result, which comprises the following steps: A candidate radiation dark band region set A is determined by using the preliminary ionospheric irregularity identification result; The TEC value of each ionospheric piercing point is calculated by using the observation data of IGS stations, combining ephemeris file and double-frequency pseudorange difference method, and the TEC grid dataset is obtained The calculation formula is shown as follows: Wherein, TEC represents total electron content, is the difference between the two pseudo-range observations of the two frequencies, , respectively represent the frequencies of the L1 and L2 signals transmitted from the same satellite at the same time, and the constant 1 / 40.3 is an ionosphere formula coefficient; A spatial projection is performed on the set A, in combination with an assumed ionospheric height to obtain a projected image grid where x denotes longitude, y denotes latitude, and t denotes observation time. For TEC grid The same ionospheric projection is made to obtain the distribution of ionospheric electron content aligned in space-time with the ionospheric electron content distribution map corresponding pixel pairs and calculate the radiation contrast feature matrix corresponding pixel pairs and calculate the radiation contrast feature matrix corresponding pixel pairs and calculate the radiation contrast feature matrix corresponding pixel pairs and calculate the radiation contrast feature matrix corresponding pixel pairs and calculate the radiation contrast feature matrix wherein, is the current point radiance, is the corresponding local neighborhood average radiance; A consistency score function is calculated according to the feature matrix, and the expression is as follows: wherein is a weight balancing coefficient; Will Statistics are performed over a continuous time window, calculating the mean and standard deviation , Constructing adaptive thresholds The expression is as follows: Wherein, k is a confidence factor, used to control the sensitivity of the output; The method comprises the following steps: Point-by-point comparison is performed with When ≥ , the area is output as a final ionospheric irregularity identification result; when < , the candidate area is marked as pending or rejected, and a final identification area set meeting the confidence condition is obtained as the final ionospheric irregularity identification result.

2. The method of claim 1, wherein, The fixed wavelength is 135.6 nm.

3. The method of claim 1, wherein, The night disk observation data is L1C-NI1 data downloaded from a global-scale earth edge and disk observation satellite, which includes two channel data of a north hemisphere channel and a south hemisphere channel.

4. A method of identifying ionospheric irregularities as claimed in claim 3, wherein, The night disk observation data is preprocessed by using a data integrity check and invalid value removal method, which comprises the following steps: Complete data sets of the north hemisphere channel and the south hemisphere channel at the same time point are obtained by using channel record number comparison and NetCDF file format verification; Invalid values in the complete data set are removed, and observation data with non-empty radiation values are screened out; The screened observation data is subjected to Gaussian denoising processing, and an effective radiation data set after cleaning is output.

5. The method of claim 3, wherein, The method for extracting south hemisphere fixed wavelength radiation data and north hemisphere fixed wavelength radiation data from the effective radiation data set comprises the following steps: Actual wavelength values closest to the target wavelength in the two channels are screened out by using the target wavelength parameter and combining the wavelength list of the north hemisphere channel and the south hemisphere channel; The determined actual wavelength values are input into a channel data calling function to extract radiation value observation records of the corresponding wavelength in the south hemisphere data file and the north hemisphere data file respectively, and a preliminary extraction data set is output; The preliminary extraction data set is spatially grouped according to the latitude direction and the longitude direction, and the radiation values in the groups are summed to obtain the radiation total amount of each group; The radiation total amount of each group and the latitude and longitude are stored as the south hemisphere fixed wavelength radiation data set and the north hemisphere fixed wavelength radiation data set.

6. The method of claim 1, wherein, The method for drawing a radiation distribution map by using the merged radiation data set and combining a two-dimensional grid interpolation method comprises the following steps: The node coordinates of the two-dimensional regular latitude and longitude grid are calculated and determined by using the latitude and longitude index in the combined radiation data set and combining the grid construction method; The corresponding radiation value is interpolated into the two-dimensional regular latitude and longitude grid to obtain a radiation value grid; The radiation value grid is superimposed with geographic elements to output a radiation distribution map containing geographic reference information.

7. A system for identifying ionospheric irregularities, characterized by, The system comprises a memory and a processor, the memory comprising a method program for identifying ionospheric irregularities, and the processor executes the method program to implement the steps of the method for identifying ionospheric irregularities according to any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a method program for identifying ionospheric irregularities, and the processor executes the method program to implement the steps of the method for identifying ionospheric irregularities according to any one of claims 1 to 6.

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