Spatial differential disturbance extraction method based on satellite in-situ electron density data

Abstract

The invention discloses a space differential disturbance extraction method based on satellite in-situ electron density data, which comprises the following steps: collecting seismic data and a space weather index, obtaining observation data of the in-situ electron density data, and preprocessing the observation data; obtaining spatial differential data according to the observation data and the background data, and obtaining magnetic calm differential data according to the spatial weather index and the spatial differential data; clustering the magnetic calm differential data to obtain abnormal disturbance data, extracting seismic disturbance data in the magnetic calm differential data based on the seismic data, and extracting ionized layer abnormal data based on the abnormal disturbance data and the seismic disturbance data; and constructing a spatial difference disturbance extraction model according to the spatial difference data and the ionized layer abnormal data, inputting the to-be-analyzed data into the spatial difference disturbance extraction model, and outputting an extraction result. The method not only can improve the precision of spatial differential disturbance extraction, but also has good interpretability, and can be directly applied to a spatial differential disturbance extraction system.

Description

Technical Field

[0001] The invention relates to the field of earthquake precursor anomaly research, and in particular to a spatial differential disturbance extraction method based on satellite in-situ electron density data. Background Art

[0002] Earthquake disasters can cause huge casualties and property losses, so the research on earthquake precursor anomalies has attracted much attention from the scientific community. Scientists have found that the precursor phenomenon of the electromagnetic effect of an earthquake is quite significant, and this disturbance will propagate to the ionosphere through chemical electric fields, direct current electric fields, acoustic gravity waves, electromagnetic waves, etc., thus causing changes in the characteristic parameters of the ionosphere.

[0003] Ionospheric measurement technology can be roughly divided into two categories. One is ground-based observation, including ionospheric vertical sounders, oblique sounders, GNSS ground receiving station inversion TEC (total electron density content), incoherent scattering radar, VLF radio wave observation, HF radio wave observation, etc.; the other is space observation, including satellite top ionospheric detection, and in-situ observation of satellites and rockets. Ground-based ionospheric observation data has the characteristics of fixed location and continuous time. Its data analysis method can choose the common method of time series analysis. For example, the sliding average method for a single point is a common method for extracting pre-earthquake ionospheric disturbances. Satellite detection can achieve all-weather, all-time and global observations, filling the gaps in ground observations; global coverage data greatly increases the sample of strong earthquake research, which is conducive to the understanding of the statistical characteristics of precursors. However, satellite in-situ detection data varies both with time and space, and the commonly used time series analysis methods cannot be directly applied to satellite observation data. In view of this, through scientific research, a spatial differential disturbance extraction method for satellite in-situ detection data is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a method for extracting spatial differential disturbance based on satellite in-situ electron density data.

[0005] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0006] The present invention comprises the following steps:

[0007] Collecting earthquake data and space weather index, obtaining observation data of in-situ electron density data through polar orbit satellites, and preprocessing the observation data;

[0008] Obtaining spatial differential data according to the observation data and the background data, and obtaining magnetically quiet differential data according to the space weather index and the spatial differential data;

[0009] Clustering the magnetically quiet differential data to obtain abnormal disturbance data, performing seismic impact analysis on the magnetically quiet differential data based on the seismic data to obtain seismic disturbance data, and extracting ionospheric abnormality data based on the abnormal disturbance data and the seismic disturbance data;

[0010] A spatial differential disturbance extraction model is constructed according to the spatial differential data and the ionospheric anomaly data, the data to be analyzed is input into the spatial differential disturbance extraction model, and an extraction result is output.

[0011] Furthermore, the method of collecting seismic data, obtaining observation data of in-situ electron density data through a polar-orbiting satellite, and preprocessing the observation data includes:

[0012] Earthquake data include epicenter location, earthquake magnitude and earthquake time. In-situ electron density data within the range of 65°N to 65°S are obtained through the Langmuir probe of the polar-orbiting satellite. The time range of observation data and space weather index is before and at the epicenter of the earthquake.

[0013] The observation data are divided into dayside data and nightside data according to the observation time. The dayside data and nightside data of each day are a group of observation data. The global longitude and latitude coordinate system is gridded with a grid size of 5 longitude × 2.5 latitude. For each group of observation data, the median of the observation data in the grid is used as the observation data of this grid.

[0014] Furthermore, a method for obtaining spatial difference data according to the observation data and background data includes:

[0015] Take the observation date within the grid The median of the observation data of the day is used as the background data of the grid, and the calculation formula of the spatial difference data is:

[0016]

[0017] in, is the observation day in the grid The spatial difference data of is the observation day in the grid The observation data, is the observation day in the grid forward Days of background data, The date index of the observation data.

[0018] Furthermore, a method for obtaining magnetically quiet differential data according to the space weather index and the spatial differential data comprises:

[0019] Remove the spatial difference data of latitudes greater than 50 degrees north latitude and 50 degrees south latitude, and remove the spatial difference data of non-magnetic quiet days. The standard of magnetic quiet days is:

[0020] F10.7<160sfu, 8 Kp values ​​are all less than 3, and the absolute values ​​of 24 Dst indices are all less than 30nT.

[0021] Furthermore, the method of clustering the magnetic quiet differential data to obtain abnormal disturbance data includes:

[0022] Normalize the magnetic quiet differential data and the position coordinates of the grid where they are located, establish data points, preset two clusters, and cluster the data points:

[0023] Update formula for attraction and belonging:

[0024]

[0025] Among them, S(o,k) is the similarity between data point o and cluster center k, S(o,p) is the similarity between data point o and cluster center p, r t+1 (o,k) is the attraction between data point o and cluster center k in the t+1th iteration, A t (o,p) is the degree of belonging between data point o and cluster center p at the tth iteration, A(o,k) is the degree of belonging between data point o and cluster center k at the t+1th iteration, r t+1 (p, k) is the attraction between cluster center p and cluster center k in the t+1th iteration,

[0026] Similarity between data points:

[0027]

[0028] Where σ is the bandwidth parameter, x o，e and x p，e are the e-th dimension components of the data point o and the cluster center p, respectively. D is the number of dimensions of the data point, and w e is the weight of the e-th dimension component of the data point, S(o, o) is the similarity between the data point o and the cluster center o, indicating the tendency of the data point o to be selected as the cluster center, h(o) is the preference parameter of the data point o, h is the preference parameter, ∈ is the noise tolerance parameter,

[0029] Update range of attraction and belonging:

[0030]

[0031] Among them, γ is the attenuation coefficient, which is used to control the update amplitude, 0<γ<1, δ is the iterative adjustment parameter of the attenuation coefficient, r t(o, k) is the attraction between data point o and cluster center k in the tth iteration, A t (o, k) is the degree of belonging between data point o and cluster center k in the tth iteration,

[0032] Cluster stability index:

[0033]

[0034] Among them, H(C1, C2) is the matching degree of C1 and C2, WDX(p, λ) is the stability index of the m clustering results obtained by a set of candidate parameters h and γ, and H(C b , C b ′) is C b and C b ′, C1 and C2 are two different clustering results, C b and C b ′ are the bth and b′th clustering results obtained by the candidate parameters h and γ, respectively, c1 and c2 are the number of clusters in C1 and C2, respectively. is the first The clusters and the The number of data points that match the clusters. is the Ith cluster in C1 and the Ith cluster in C2 The number of data points that match the clusters. is the first The clusters and the The number of data points that match the clusters is determined by the number of candidate parameters with the largest stability index value.

[0035] The data points in the clusters with fewer data points are regarded as abnormal disturbance data.

[0036] Further, based on the seismic data, seismic impact analysis is performed on the magnetic quiet differential data to obtain seismic disturbance data, and a method for extracting ionospheric abnormality data based on the abnormal disturbance data and the seismic disturbance data includes:

[0037] The skewness, kurtosis and spatial variation rate of the magnetic quiet difference data of the revisit orbit closest to the epicenter are extracted to form the spatial difference features.

[0038] The spatial differential features are used as raw data to map to high-dimensional space through nonlinear transformation functions, and a hypersphere with the smallest volume is found in the high-dimensional space. The objective function is:

[0039]

[0040] in, is the radius of the hypersphere, n is the number of original data, Φ(·) is the nonlinear transformation function, is the i-th original data, a is the center of the hypersphere, ξ i is the ith relaxation factor, is the penalty parameter, β i is the local density weight, ||·|| represents the Euclidean distance, ρ i is the gradient threshold of the preset nonlinear transformation function, for The average value of , using the Lagrange multiplier method to obtain the dual form of the objective function:

[0041]

[0042]

[0043] Among them, G(·) is the kernel function, α i and α j They are and The corresponding Lagrange coefficient is used to solve the minimum value of the dual form of the objective function, and we get is the jth original data, v i is the gradient constraint The corresponding Lagrange coefficient is used to solve the minimum value of the dual form of the objective function, and the calculation formulas for the center and radius of the hypersphere are obtained as follows:

[0044]

[0045] in, SV is the original data set of support vectors, is the vth original data in SV,

[0046] The distance from the original data to the center of the hypersphere is

[0047]

[0048] in, for The distance to the center of the hypersphere, For the The original data, if but On or inside the hypersphere, it is normal data; otherwise The corresponding magnetic quiet differential data belong to seismic disturbance data;

[0049] The overlapping data of abnormal disturbance data and earthquake disturbance data are ionospheric anomaly data.

[0050] Furthermore, a method for constructing a spatial differential disturbance extraction model based on the spatial differential data and the ionospheric anomaly data comprises:

[0051] A spatial differential disturbance extraction model is constructed using a neural network algorithm, which includes an input layer, a function layer, and an output layer;

[0052] The input layer preprocesses the input data and converts it into spatial differential data, and obtains magnetically quiet differential data based on the space weather index; the function layer is a binary classification module that learns the correspondence between magnetically quiet differential data and ionospheric anomaly data; the output layer outputs the extraction results of ionospheric anomaly data.

[0053] In a second aspect, an embodiment of the present application further provides an electronic device, including:

[0054] A processor; and a memory arranged to store computer executable instructions, which when executed cause the processor to perform the method steps described in the first aspect.

[0055] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple applications, the electronic device executes the method steps described in the first aspect.

[0056] The beneficial effects of the present invention are:

[0057] The present invention is a spatial differential disturbance extraction method based on satellite in-situ electron density data. Compared with the prior art, the present invention has the following technical effects:

[0058] The method proposed in the present invention has been integrated into the "Earthquake Demonstration Application" platform developed based on the CSES-01 satellite data. After inputting the satellite in-situ electron density data, it can automatically generate daily spatial difference maps of the day side and night side for monitoring and tracking of earthquake key monitoring and defense areas.

[0059] The method proposed in this application is not only applicable to in-situ electron density data, but also can prepare data for in-situ detected temperature, single-band electromagnetic field and other data according to the input interface requirements, perform spatial difference processing, and extract disturbance phenomena in the ionosphere. Therefore, this method also has good application prospects in multi-parameter comparative analysis, which is conducive to the study of synchronous changes in space and time of different physical parameter disturbances in the ionosphere. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a flow chart of the steps of a method for extracting spatial differential disturbance based on satellite in-situ electron density data of the present invention;

[0061] Figure 2 It is the distribution diagram of observed data before and after preprocessing;

[0062] Figure 3 This is the spatial distribution of electron density on the day side;

[0063] Figure 4 is the electron density spatial difference distribution map;

[0064] Figure 5 This is a graph showing the electron density changing with latitude for different revisit orbits;

[0065] Figure 6 This is the statistical distribution diagram of epicenter distance and abnormal occurrence time;

[0066] Figure 7 It is a schematic diagram of the structure of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION

[0067] The present invention is further described below by means of specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0068] The present invention provides a spatial differential disturbance extraction method based on satellite in-situ electron density data, comprising the following steps:

[0069] like Figure 1 As shown, in this embodiment, the following steps are included:

[0070] Collecting earthquake data and space weather index, obtaining observation data of in-situ electron density data through polar orbit satellites, and preprocessing the observation data;

[0071] In the actual evaluation, the earthquake data is a Ms6.9 earthquake on August 5, 2018 at location A, with the epicenter coordinates (8.258°S, 116.438°E). The polar-orbiting satellite used is the China Electromagnetic Monitoring Experimental Satellite (CSES-01). The observation data before and after preprocessing are as follows: Figure 2 As shown, Figure 2 The observation day is July 28, 2018;

[0072] Obtaining spatial differential data according to the observation data and the background data, and obtaining magnetically quiet differential data according to the space weather index and the spatial differential data;

[0073] In the actual evaluation, the background data is obtained based on the observation data 30 days before the observation day. Figure 4The spatial difference distribution diagram of electron density, where the upper diagram is the spatial difference data distribution diagram of the dayside data on July 31, the middle diagram is the spatial difference data distribution diagram of the dayside data on August 4, and the lower diagram is the spatial difference data distribution diagram of the nightside data on July 31. The red and blue five-pointed stars are the epicenters and their conjugate points, and the blue line in the middle of each sub-diagram represents the magnetic equator;

[0074] Clustering the magnetically quiet differential data to obtain abnormal disturbance data, performing seismic impact analysis on the magnetically quiet differential data based on the seismic data to obtain seismic disturbance data, and extracting ionospheric abnormality data based on the abnormal disturbance data and the seismic disturbance data;

[0075] In the actual evaluation, Figure 5 The electron density of different revisit orbits varies with latitude. The upper figure is the dayside data of July 31, the middle figure is the dayside data of August 4, and the lower figure is the nightside data of July 31. The red line in the figure represents the orbit with ionospheric anomaly data extracted, and the other color curves represent their revisit orbits. The specific dates are shown in the legend. The red and blue five-pointed stars are the epicenter and its conjugate point, and the two blue vertical lines represent the latitude of 20° from the epicenter. Figure 6 This is a statistical distribution diagram of epicentral distance and abnormal occurrence time, showing that earthquake disturbances are statistically significant 11 days, 3 days, and 2 days before the earthquake;

[0076] A spatial differential disturbance extraction model is constructed according to the spatial differential data and the ionospheric anomaly data, the data to be analyzed is input into the spatial differential disturbance extraction model, and an extraction result is output.

[0077] In this embodiment, seismic data is collected, and observation data of in-situ electron density data is obtained through a polar-orbiting satellite. The method for preprocessing the observation data includes:

[0078] Earthquake data include epicenter location, earthquake magnitude and earthquake time. In-situ electron density data within the range of 65°N to 65°S are obtained through the Langmuir probe of the polar-orbiting satellite. The time range of observation data and space weather index is before and at the epicenter of the earthquake.

[0079] The observation data are divided into dayside data and nightside data according to the observation time. The dayside data and nightside data of each day are a group of observation data. The global longitude and latitude coordinate system is gridded with a grid size of 5 longitude × 2.5 latitude. For each group of observation data, the median of the observation data in the grid is used as the observation data of this grid.

[0080] In this embodiment, the distribution of observation data, background data and spatial difference data is as follows: Figure 3 As shown, Figure 3This is the spatial distribution map of the electron density on the sun side on May 8, 2021. The upper picture is the observation data, the middle picture is the background data, and the lower picture is the spatial difference data map of the observation data and the background data;

[0081] In this embodiment, the method for obtaining spatial difference data according to the observation data and background data includes:

[0082] Take the observation date within the grid The median of the observation data of the day is used as the background data of the grid, and the calculation formula of the spatial difference data is:

[0083]

[0084] in, is the observation day in the grid The spatial difference data of is the observation day in the grid The observation data, is the observation day in the grid forward Days of background data, The date index of the observation data.

[0085] In this embodiment, the method for obtaining magnetic quiet differential data according to the space weather index and the spatial differential data includes:

[0086] Remove the spatial difference data of latitudes greater than 50 degrees north latitude and 50 degrees south latitude, and remove the spatial difference data of non-magnetic quiet days. The standard of magnetic quiet days is:

[0087] F10.7<160sfu, 8 Kp values ​​are all less than 3, and the absolute values ​​of 24 Dst indices are all less than 30nT.

[0088] In this embodiment, the method of clustering the magnetic quiet difference data to obtain abnormal disturbance data includes:

[0089] Normalize the magnetic quiet differential data and the position coordinates of the grid where they are located, establish data points, preset two clusters, and cluster the data points:

[0090] Update formula for attraction and belonging:

[0091]

[0092]

[0093] Among them, S(o,k) is the similarity between data point o and cluster center k, S(o,p) is the similarity between data point o and cluster center p, r t+1(o,k) is the attraction between data point o and cluster center k in the t+1th iteration, A t (o,p) is the degree of belonging between data point o and cluster center p in the tth iteration, A(o,k) is the degree of belonging between data point o and cluster center k in the t+1th iteration, r t+1 (p,k) is the attraction between cluster center p and cluster center k in the t+1th iteration,

[0094] Similarity between data points:

[0095]

[0096] Where σ is the bandwidth parameter, x o,e and x p,e are the e-th dimension components of the data point o and the cluster center p, respectively. D is the number of dimensions of the data point, and w e is the weight of the e-th dimension component of the data point, S(o,o) is the similarity between the data point o and the cluster center o, indicating the tendency of the data point o to be selected as the cluster center, h(o) is the preference parameter of the data point o, h is the preference parameter, ∈ is the noise tolerance parameter,

[0097] Update range of attraction and belonging:

[0098]

[0099] Among them, γ is the attenuation coefficient, which is used to control the update amplitude, 0<γ<1, δ is the iterative adjustment parameter of the attenuation coefficient, r t (o,k) is the attraction between data point o and cluster center k in the tth iteration, A t (o,k) is the degree of belonging between data point o and cluster center k in the tth iteration,

[0100] Cluster stability index:

[0101]

[0102] Among them, H(C1,C2) is the matching degree of C1 and C2, WDX(p,λ) is the stability index of the m clustering results obtained by a set of candidate parameters h and γ, and H(C b ,C b′ ) is C b and C b′ The matching degree, C1 and C2 are two different clustering results, C b and C b′ The bth and bth candidates h and γ are obtained respectively. ′ clustering results, c1 and c2 are the number of clusters in C1 and C2 respectively. is the first The clusters and the The number of data points that match the clusters. is the Ith cluster in C1 and the Ith cluster in C2 The number of data points that match the clusters. is the first The clusters and the The number of data points that match the clusters is determined by the number of candidate parameters with the largest stability index value.

[0103] The data points in the clusters with fewer data points are regarded as abnormal disturbance data.

[0104] In this embodiment, the method of performing seismic impact analysis on the magnetic quiet differential data based on the seismic data to obtain seismic disturbance data and extracting ionospheric abnormality data based on the abnormal disturbance data and the seismic disturbance data includes:

[0105] The skewness, kurtosis and spatial variation rate of the magnetic quiet difference data of the revisit orbit closest to the epicenter are extracted to form the spatial difference features.

[0106] The spatial differential features are used as raw data to map to high-dimensional space through nonlinear transformation functions, and a hypersphere with the smallest volume is found in the high-dimensional space. The objective function is:

[0107]

[0108] in, is the radius of the hypersphere, n is the number of original data, Φ(·) is the nonlinear transformation function, is the i-th original data, a is the center of the hypersphere, ξ i is the ith relaxation factor, is the penalty parameter, β i is the local density weight, ∥·∥ represents the Euclidean distance, ρ i is the gradient threshold of the preset nonlinear transformation function, for The average value of , using the Lagrange multiplier method to obtain the dual form of the objective function:

[0109]

[0110] Among them, G(·) is the kernel function, α i and α j They are and The corresponding Lagrange coefficient is used to solve the minimum value of the dual form of the objective function, and we get is the jth original data, v i is the gradient constraint The corresponding Lagrange coefficient is used to solve the minimum value of the dual form of the objective function, and the calculation formulas for the center and radius of the hypersphere are obtained as follows:

[0111]

[0112] in, SV is the original data set of support vectors, is the vth original data in SV,

[0113] The distance from the original data to the center of the hypersphere is

[0114]

[0115] in, for The distance to the center of the hypersphere, For the The original data, if but On or inside the hypersphere, it is normal data; otherwise The corresponding magnetic quiet differential data belong to seismic disturbance data;

[0116] The overlapping data of abnormal disturbance data and earthquake disturbance data are ionospheric anomaly data.

[0117] In this embodiment, the method for constructing a spatial differential disturbance extraction model according to the spatial differential data and the ionospheric anomaly data includes:

[0118] A spatial differential disturbance extraction model is constructed using a neural network algorithm, which includes an input layer, a function layer, and an output layer;

[0119] The input layer preprocesses the input data and converts it into spatial differential data, and obtains magnetically quiet differential data based on the space weather index; the function layer is a binary classification module that learns the correspondence between magnetically quiet differential data and ionospheric anomaly data; the output layer outputs the extraction results of ionospheric anomaly data.

[0120] Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 2 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0121] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0122] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0123] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a spatial differential disturbance extraction device based on satellite in-situ electron density data at the logical level. The processor executes the program stored in the memory and is specifically used to execute any of the aforementioned spatial differential disturbance extraction methods based on satellite in-situ electron density data.

[0124] The above application Figure 1The spatial differential disturbance extraction method based on satellite in-situ electron density data disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or instructions in software form. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0125] The electronic device may also perform Figure 1 A spatial differential disturbance extraction method based on satellite in-situ electron density data is implemented Figure 1 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0126] An embodiment of the present application also proposes a computer-readable storage medium, which stores one or more programs, and the one or more programs include instructions. When the instructions are executed by an electronic device including multiple application programs, any one of the aforementioned spatial differential disturbance extraction methods based on satellite in-situ electron density data is executed.

[0127] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0128] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0129] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0130] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0131] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0132] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0133] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0134] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0135] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0136] 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 principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A spatial differential disturbance extraction method based on satellite in-situ electron density data, characterized in that: The following steps are involved: Collecting earthquake data and space weather index, obtaining observation data of in-situ electron density data through polar orbit satellites, and preprocessing the observation data; Obtaining spatial differential data according to the observation data and the background data, and obtaining magnetic quiet differential data according to the space weather index and the spatial differential data; Clustering the magnetically quiet differential data to obtain abnormal disturbance data, performing seismic impact analysis on the magnetically quiet differential data based on the seismic data to obtain seismic disturbance data, and extracting ionospheric abnormality data based on the abnormal disturbance data and the seismic disturbance data; A spatial differential disturbance extraction model is constructed according to the spatial differential data and the ionospheric anomaly data, the data to be analyzed is input into the spatial differential disturbance extraction model, and an extraction result is output.

2. According to claim 1, a spatial differential disturbance extraction method based on satellite in-situ electron density data is characterized in that: The method of collecting seismic data, obtaining observation data of in-situ electron density data through a polar-orbiting satellite, and preprocessing the observation data comprises: Earthquake data include epicenter location, earthquake magnitude and earthquake time. In-situ electron density data within the range of 65°N to 65°S are obtained through the Langmuir probe of the polar-orbiting satellite. The time range of observation data and space weather index is before and at the epicenter of the earthquake. The observation data are divided into dayside data and nightside data according to the observation time. The dayside data and nightside data of each day are a group of observation data. The global longitude and latitude coordinate system is gridded with a grid size of 5 longitude × 2.5 latitude. For each group of observation data, the median of the observation data in the grid is used as the observation data of this grid.

3. The spatial differential disturbance extraction method based on satellite in-situ electron density data according to claim 1 is characterized in that: The method for obtaining spatial difference data according to the observation data and background data comprises: Take the observation date within the grid The median of the observation data of the day is used as the background data of the grid, and the calculation formula of the spatial difference data is: in, is the observation day in the grid The spatial difference data of is the observation day in the grid The observation data, is the observation day in the grid forward Days of background data, The date index of the observation data.

4. The spatial differential disturbance extraction method based on satellite in-situ electron density data according to claim 1 is characterized in that: The method for obtaining magnetically quiet differential data according to the space weather index and the spatial differential data comprises: Remove the spatial difference data of latitudes greater than 50 degrees north latitude and 50 degrees south latitude, and remove the spatial difference data of non-magnetic quiet days. The standard of magnetic quiet days is: F10.7<160sfu, 8 Kp values ​​are all less than 3, and the absolute values ​​of 24 Dst indices are all less than 30nT.

5. The spatial differential disturbance extraction method based on satellite in-situ electron density data according to claim 1 is characterized in that: The method of clustering the magnetic quiet difference data to obtain abnormal disturbance data comprises: Normalize the magnetic quiet differential data and the position coordinates of the grid where they are located, establish data points, preset two clusters, and cluster the data points: Update formula for attraction and belonging: Among them, S(o,k) is the similarity between data point o and cluster center k, S(o,p) is the similarity between data point o and cluster center p, r t+1 (o,k) is the attraction between data point o and cluster center k in the t+1th iteration, A t (o,p) is the degree of belonging between data point o and cluster center p at the tth iteration, A(o,k) is the degree of belonging between data point o and cluster center k at the t+1th iteration, r t+1 (p,k) is the attraction between cluster center p and cluster center k in the t+1th iteration, Similarity between data points: Where σ is the bandwidth parameter, x o,e and x p,e are the e-th dimension components of the data point o and the cluster center p, respectively. D is the number of dimensions of the data point, and w e is the weight of the e-th dimension component of the data point, S(o,o) is the similarity between the data point o and the cluster center o, indicating the tendency of the data point o to be selected as the cluster center, h(o) is the preference parameter of the data point o, h is the preference parameter, ∈ is the noise tolerance parameter, Update range of attraction and belonging: Among them, γ is the attenuation coefficient, which is used to control the update amplitude, 0<γ<1, δ is the iterative adjustment parameter of the attenuation coefficient, r t (o,k) is the attraction between data point o and cluster center k in the tth iteration, A t (o,k) is the degree of belonging between data point o and cluster center k in the tth iteration, Cluster stability index: Among them, H(C1,C2) is the matching degree of C1 and C2, WDX(p,λ) is the stability index of the m clustering results obtained by a set of candidate parameters h and γ, and H(C b ,C b′ ) is C b and C b′ The matching degree, C1 and C2 are two different clustering results, C b and C b′ are the b-th and b'-th clustering results obtained by the candidate parameters h and γ, respectively. c1 and c2 are the number of clusters in C1 and C2, respectively. is the first The clusters and the The number of data points that match the clusters. is the Ith cluster in C1 and the Ith cluster in C2 The number of data points that match the clusters. is the first The clusters and the The number of data points that match the clusters is determined by the number of candidate parameters with the largest stability index value. The data points in the clusters with fewer data points are regarded as abnormal disturbance data.

6. The spatial differential disturbance extraction method based on satellite in-situ electron density data according to claim 1 is characterized in that: The method of performing seismic impact analysis on the magnetic quiet differential data based on the seismic data to obtain seismic disturbance data, and extracting ionospheric abnormality data based on the abnormal disturbance data and the seismic disturbance data comprises: The skewness, kurtosis and spatial variation rate of the magnetic quiet difference data of the revisit orbit closest to the epicenter are extracted to form the spatial difference features. The spatial differential features are used as raw data to map to high-dimensional space through nonlinear transformation functions, and a hypersphere with the smallest volume is found in the high-dimensional space. The objective function is: in, is the radius of the hypersphere, n is the number of original data, Φ(·) is the nonlinear transformation function, is the i-th original data, a is the center of the hypersphere, ξ i is the ith relaxation factor, is the penalty parameter, β i is the local density weight, ||·|| represents the Euclidean distance, ρ i is the gradient threshold of the preset nonlinear transformation function, for The average value of , using the Lagrange multiplier method to obtain the dual form of the objective function: Among them, G(·) is the kernel function, α i and α j They are and The corresponding Lagrange coefficient is used to solve the minimum value of the dual form of the objective function, and we get is the jth original data, v i is the gradient constraint The corresponding Lagrange coefficient is used to solve the minimum value of the dual form of the objective function, and the calculation formulas for the center and radius of the hypersphere are obtained as follows: in, SV is the original data set of support vectors, is the vth original data in SV, The distance from the original data to the center of the hypersphere is in, for The distance to the center of the hypersphere, For the The original data, if but On or inside the hypersphere, it is normal data; otherwise The corresponding magnetic quiet differential data belong to seismic disturbance data; The overlapping data of abnormal disturbance data and earthquake disturbance data are ionospheric anomaly data.

7. The spatial differential disturbance extraction method based on satellite in-situ electron density data according to claim 1 is characterized in that: The method for constructing a spatial differential disturbance extraction model according to the spatial differential data and the ionospheric anomaly data comprises: A spatial differential disturbance extraction model is constructed using a neural network algorithm, which includes an input layer, a function layer, and an output layer; The input layer preprocesses the input data and converts it into spatial differential data, and obtains magnetically quiet differential data based on the space weather index; the function layer is a binary classification module that learns the correspondence between magnetically quiet differential data and ionospheric anomaly data; the output layer outputs the extraction results of ionospheric anomaly data.

8. An electronic device, comprising: processor; as well as A memory arranged to store computer executable instructions, which when executed cause the processor to perform the method according to any one of claims 1 to 7.

9. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of application programs, enables the electronic device to execute the method according to any one of claims 1 to 7.

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