Ionosphere data analysis model construction method and terminal thereof

Abstract

The application discloses a method for constructing an ionosphere data analysis model, comprising the following steps: S1, accurately determining the satellite and receiver frequency interval deviation based on the IGGDCB method; S2, establishing a local ionosphere TEC model based on a generalized trigonometric series function; S3, establishing a global ionosphere TEC model based on a spherical harmonic function; S4, calculating the grid point ionosphere VTEC based on an inter-station partition method; and S5, realizing the prediction of the global ionosphere TEC by using a least square combination maximum posterior estimation method. The method realizes the fine modeling, monitoring and prediction of the global scale and key regional ionosphere changes, establishes a global / regional multi-scale ionosphere data analysis model, thereby greatly improving the fine monitoring of the space ionosphere effect and the ionosphere threat early warning capability, and providing sufficient protection for the site selection construction of an extra-high voltage line and the safe operation of a power grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of ionospheric data analysis, in particular to an ionospheric data analysis model construction method and a terminal thereof. BACKGROUND

[0002] The ionosphere is located 60 to 1000 kilometers above the earth's surface, and a large number of free electrons and ions exist therein. Influenced by the sun and geomagnetic activity, the electron density of the ionosphere changes regularly in time, season, geographical latitude and the like in the vertical and horizontal directions in space, including diurnal variation, seasonal variation and solar cycle variation. When the sun appears a large sunspot group, a coronal hole, a strong flare, a violent material ejection and the like, a large amount of charged particle flow and high-energy rays enter the near-earth space environment; the large increase of the charged particle flow and high-energy rays and the severe change of the electromagnetic field cause the density of the charged particles, the current system and the electric field distribution in the ionosphere to appear explosive fluctuations, which seriously threatens the safety of space and ground technical systems such as power transmission networks.

[0003] When a space ionospheric weather event occurs, the earth's magnetic field changes. The ultra-high voltage line has the characteristics of long power transmission distance, high voltage and strong magnetic field around the line. When the magnetic field changes occur, the magnetic flux passing through the power grid loop also changes, and a strong induced current is generated on the power grid loop. This will cause the following problems: (1) the voltage imbalance of the power transmission network in a short time, affecting the stable operation of the power grid; (2) the power transmission and transformation equipment is burned out due to core saturation, magnetic flux overflow and high heat generated at the high magnetic flux density; (3) if the power transmission line is constructed on a medium with low conductivity (such as igneous rock), the strong induced current generated by the magnetic storm will mainly act on the power grid, further causing a serious impact on the operation of the power grid. SUMMARY

[0004] The technical problem to be solved by the present application is to provide an ionospheric data analysis model construction method and a terminal thereof, to solve the key technical problems faced in the accurate post-event simulation, real-time fine detection and reliable medium and short-term prediction of space ionospheric changes, to form a characteristic global / regional multi-scale ionospheric modeling method, and to establish an ionospheric data analysis model for power grid safety applications.

[0005] To solve the above technical problems, one technical solution adopted by the present application is:

[0006] An ionospheric data analysis model construction method comprises the following steps:

[0007] S1, determining the satellite and receiver frequency bias based on the IGGDCB method;

[0008] S2, establishing a local ionospheric TEC model based on a generalized trigonometric series function;

[0009] S3, establishing a global ionospheric TEC model based on spherical harmonics;

[0010] S4, calculating grid point ionospheric VTEC based on inter-station partition method;

[0011] S5, realizing the prediction of global ionospheric TEC by using least square combination maximum posterior estimation method.

[0012] To solve the above technical problems, another technical solution adopted by the present application is:

[0013] An ionospheric data analysis model construction terminal comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps in an ionospheric data analysis model construction method when executing the computer program.

[0014] The present application has the advantages that: the multi-mode global satellite navigation (GNSS) technology and ground reference station observation resources are fully utilized to realize the fine modeling, monitoring and prediction of global-scale and key regional ionospheric changes, establish a global / regional multi-scale ionospheric data analysis model, improve the Beidou / GNSS navigation and positioning accuracy and service performance, and improve the spatial ionospheric environment monitoring, prediction and analysis capability, greatly improve the fine monitoring of spatial ionospheric effects and the ionospheric threat warning capability, and provide sufficient protection for the site selection and construction of ultra-high voltage lines and the safe operation of power grids. It can meet the analysis needs of ionospheric fine changes at different spatial scales, and meet the application needs of accurate post-analysis, real-time fine monitoring and reliable prediction of medium and short-term changes of ionospheric changes. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 A flowchart of an ionospheric data analysis model construction method according to an embodiment of the present application;

[0016] Figure 2 An overall technical solution of an ionospheric data analysis model construction method according to an embodiment of the present application;

[0017] Figure 3 An ionospheric thin layer assumption schematic diagram of an ionospheric data analysis model construction method according to an embodiment of the present application;

[0018] Figure 4 An architecture diagram of an ionospheric data analysis model construction terminal according to an embodiment of the present application. DETAILED DESCRIPTION

[0019] To explain the technical content, purposes and effects of the present application in detail, the following will be described in conjunction with the embodiments and the accompanying drawings.

[0020] A method for constructing an ionospheric data analysis model includes the following steps:

[0021] S1. Determine the inter-frequency offset between the satellite and the receiver based on the IGGDCB method;

[0022] S2. Establish a local ionospheric TEC model based on generalized trigonometric series functions;

[0023] S3. Establish a global ionospheric TEC model based on spherical harmonic functions;

[0024] S4. Calculate VTEC of the ionosphere at grid points based on the inter-station zoning method;

[0025] S5. The least squares combined maximum a posteriori estimation method is used to predict the global ionospheric TEC.

[0026] As described above, the beneficial effects of this invention are as follows: It fully utilizes multi-mode Global Navigation Satellite System (GNSS) technology and ground-based reference station observation resources to achieve refined modeling, monitoring, and forecasting of ionospheric changes at global and key regional scales. It establishes global / regional multi-scale ionospheric data analysis models, improves the accuracy and service performance of BeiDou / GNSS navigation and positioning, enhances the monitoring, forecasting, and analysis capabilities of the space ionospheric environment, and significantly improves the refined monitoring of space ionospheric effects and the early warning capabilities of ionospheric threats, providing sufficient guarantees for the site selection and construction of ultra-high voltage power lines and the safe operation of the power grid. It can meet the analytical needs for refined ionospheric changes at different spatial scales and can satisfy application needs such as accurate post-event re-analysis of ionospheric changes, real-time refined monitoring, and reliable prediction of short- and medium-term changes.

[0027] Further, step S1 specifically includes:

[0028] By designing a satellite inter-frequency deviation stability criterion, satellites whose inter-frequency deviations meet the preset stability criteria are adaptively selected to construct a pseudo-stability benchmark.

[0029] As described above, adaptively selecting some satellites with better inter-frequency deviation stability to construct a new "quasi-stable" benchmark for the reasonable separation of satellite and receiver inter-frequency deviation parameters can effectively avoid the impact of poor inter-frequency deviation stability of some satellites on the estimation of all other inter-frequency deviation parameters, compared with the "zero mean" benchmark applied to all satellites in the existing method, and further improve the reliability of parameter estimation.

[0030] Further, step S2 specifically includes:

[0031] An adaptive parameter selection strategy based on the F-test is adopted to select a trigonometric series function structure suitable for local regions by automatically adjusting the constituent terms in the generalized trigonometric series.

[0032] As can be seen from the above description, by combining the polynomial function with the trigonometric series function with periodic characteristics, the reasonable and accurate simulation of the local ionospheric TEC change can be effectively realized. The generalized trigonometric series function (GTSF) is used to establish the local ionospheric TEC model based on each reference station to obtain the fine change characteristics of the ionospheric TEC in the region.

[0033] Further, the step S3 is specifically:

[0034] For the continuity problem between adjacent time periods, the piecewise linear interpolation function method is used to process, and the calculation of the ionospheric VTEC at any time is set to the ionospheric TEC model in the two adjacent time periods. The ionospheric TEC model in the two adjacent time periods is seamlessly connected through a linear change function related to time, so that the ionospheric change in the adjacent time period is continuous.

[0035] The step S4 includes:

[0036] The selected weight fitting method is used to introduce a virtual ionospheric observation in the region where the ionospheric TEC is negative, to reconstruct the observation equation of the global spherical harmonic function ionospheric TEC model, and to adaptively adjust the weight of the virtual ionospheric observation according to the calculation result, to obtain the optimal solution of the global spherical harmonic function ionospheric TEC model coefficient.

[0037] As can be seen from the above description, the spherical harmonic function (SHF) is used to establish the global ionospheric TEC model to ensure the reasonable extrapolation of the ionospheric TEC in the unobserved region, and to realize the construction of the medium-short scale global ionospheric prediction data model.

[0038] Further, the step S5 is specifically:

[0039] S501, the trend item period of each order spherical harmonic coefficient is analyzed and extracted by the power spectrum estimation method;

[0040] S502, a plurality of spherical harmonic coefficient fitting residual errors are obtained based on the least square method fitting the spherical harmonic coefficients;

[0041] S503, the autocovariance value of the random term parameter is calculated according to the fitting residual error of the spherical harmonic coefficient, and the autocovariance function obtained by the polynomial function fitting is used to construct the variance and covariance matrix of the random term parameter according to the time interval;

[0042] S504, the trend item of the spherical harmonic coefficient is calculated based on the two-step solution method, and the random term of the spherical harmonic coefficient is obtained based on the maximum posterior estimation method. The trend item and the random term parameter are respectively time extrapolated to obtain the predicted trend item and the random term;

[0043] S505, merging the predicted trend term and the random term into predicted spherical harmonic coefficients, substituting the predicted spherical harmonic coefficients into a spherical harmonic function to calculate VTEC values of each grid point, and finally completing the prediction of global ionospheric TEC.

[0044] As can be seen from the above description, the global ionospheric TEC grid is obtained by comprehensively calculating the ionospheric TEC at the grid points by using the 'inter-station partition method', and reliable prediction of the global ionospheric information in the medium and short scales is realized.

[0045] An ionospheric data analysis model construction terminal, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the following steps when executing the computer program:

[0046] S1, determining satellite and receiver frequency interval deviation based on the IGGDCB method;

[0047] S2, establishing a local ionospheric TEC model based on a generalized trigonometric series function;

[0048] S3, establishing a global ionospheric TEC model based on a spherical harmonic function;

[0049] S4, calculating grid point ionospheric VTEC based on the inter-station partition method;

[0050] S5, realizing prediction of global ionospheric TEC by using a least square combination maximum a posteriori estimation method.

[0051] As can be seen from the above description, the present application has the beneficial effects that: the multi-mode global satellite navigation (GNSS) technology and ground reference station observation resources are fully utilized to realize fine modeling, monitoring and prediction of global scale and key regional ionospheric changes, a global / regional multi-scale ionospheric data analysis model is established, the Beidou / GNSS navigation and positioning accuracy and service performance are improved, the spatial ionospheric environment monitoring, prediction and analysis capability is improved, the fine monitoring of the spatial ionospheric effect and the ionospheric threat warning capability are greatly improved, and sufficient protection is provided for the ultra-high voltage line site selection construction and power grid safe operation. The application can meet the analysis requirements of fine changes of the ionosphere in different spatial scales, and can meet the application requirements of accurate post-analysis, real-time fine monitoring and reliable prediction of medium and short term changes of the ionosphere.

[0052] Further, the step S1 is specifically:

[0053] By designing a satellite frequency interval deviation stability discrimination standard, a part of the frequency interval deviation is selected to meet the preset stability standard to construct a pseudo-stable reference.

[0054] From the above description, the adaptive selection of the part of the satellite with good frequency deviation stability constructs a new "quasi-stable" reference, which is used for the reasonable separation of the satellite and receiver frequency deviation parameters. Compared with the "zero mean" reference applied to all satellites in the existing method, the influence of the poor stability of part of the satellite frequency deviation on the estimation of other all frequency deviation parameters can be effectively avoided, and the reliability of parameter estimation is further improved.

[0055] Further, the step S2 is specifically:

[0056] An adaptive parameter selection strategy based on F test is adopted to automatically adjust the constituent term in the generalized trigonometric series, and then select the trigonometric series function structure suitable for the local area.

[0057] From the above description, by combining the polynomial function with the trigonometric series function with periodic characteristics, the reasonable and accurate simulation of the local ionospheric TEC change can be effectively realized. The generalized trigonometric series function (GTSF) is used to establish a local ionospheric TEC model for each reference station to obtain the fine change characteristics of the ionospheric TEC in the region.

[0058] Further, the step S3 is specifically:

[0059] The piecewise linear interpolation function method is used to deal with the continuity problem between adjacent time periods. The calculation of the ionospheric VTEC at any time is set to the ionospheric TEC model in the two adjacent time periods, and the ionospheric TEC model in the two adjacent time periods is seamlessly connected through the linear change function related to time, so that the ionospheric change in the adjacent time period is continuous.

[0060] The step S4 includes:

[0061] The selected weight fitting method is adopted to introduce a virtual ionospheric observation in the region where the ionospheric TEC is negative, restructure the observation equation of the global spherical harmonic function ionospheric TEC model, and adaptively adjust the weight of the virtual ionospheric observation according to the calculation result to obtain the optimal solution of the global spherical harmonic function ionospheric TEC model coefficient.

[0062] From the above description, the spherical harmonic function (SHF) is used to establish a global ionospheric TEC model to ensure the reasonable extrapolation of the ionospheric TEC in the unobserved region, and realize the construction of the medium-short scale global ionospheric prediction data model.

[0063] Further, the step S5 is specifically:

[0064] S501, the trend item period of each order spherical harmonic coefficient is analyzed and extracted by the power spectrum estimation method;

[0065] S502, obtain a fitting residual of the plurality of spherical harmonic coefficients based on least square fitting of the spherical harmonic coefficients;

[0066] S503, calculate a self-covariance value of the random term parameter according to the fitting residual of the spherical harmonic coefficients, and obtain a self-covariance function fitted by using a polynomial function to construct a variance-covariance matrix of the random term parameter according to a time interval;

[0067] S504, calculate a trend term of the spherical harmonic coefficients based on a two-step solution, and obtain a random term of the spherical harmonic coefficients based on a maximum posterior estimation method, and respectively perform time extrapolation on the trend term and the random term parameter to obtain a predicted trend term and a predicted random term;

[0068] S505, combine the predicted trend term and the predicted random term into predicted spherical harmonic coefficients, and substitute the predicted spherical harmonic coefficients into a spherical harmonic function to calculate VTEC values of each grid point, and finally complete the prediction of the global ionospheric TEC.

[0069] As can be known from the above description, the global ionospheric TEC grid is obtained by comprehensively calculating the ionospheric TEC at the grid points by using the "inter-station partition method", and the reliable prediction of the global ionospheric information in the medium and short scales is realized.

[0070] The ionospheric data analysis model construction method and the terminal thereof can realize fine modeling, monitoring and prediction of global-scale and key-area ionospheric changes, greatly improve the fine monitoring of the spatial ionospheric effect and the ionospheric threat warning capability, and provide sufficient protection for the site selection and construction of the ultra-high voltage line and the safe operation of the power grid.

[0071] Nomenclature:

[0072] 1) GNSS: Global Navigation Satellite System, which is a global satellite navigation and positioning system based on civil use. It provides high-precision and high-reliability positioning services, realizes complete non-military control and management, and can realize global navigation and positioning functions.

[0073] 2) Ionospheric TEC: Ionospheric electron concentration total content (TEC), also known as ionospheric electron concentration column content, integrated content, etc., which is also an important parameter for ionospheric correction in precise positioning, navigation and wave science. Total electron content, i.e. the total number of electrons per square meter from the bottom of the ionosphere (about 90 kilometers high) to the top of the ionosphere (about 1000 kilometers high).

[0074] 3) Ionospheric VTEC: vertical total electron content, which is an important parameter reflecting the characteristics of the ionosphere.

[0075] 4) Frequency bias: Navigation satellites usually broadcast navigation signals at multiple frequencies. Due to the fact that the satellite navigation signal transmission links at different frequencies are not exactly the same, the time delay of the navigation signals reaching the satellite antenna electronic phase center via the satellite transmission link is also different, which is called the hardware delay difference or frequency bias parameter.

[0076] 5) Reference station: A reference station is a ground-based fixed observation station that continuously observes satellite navigation signals for a long time and transmits observation data to a data center in real time or at a fixed time through communication facilities.

[0077] 6) Spherical harmonics function SHF: Spherical harmonics function, as a function describing global changes in physical quantities, has excellent mathematical structure and has become one of the main function models for describing global ionospheric TEC, as shown in the following formula:

[0078]

[0079] where VTEC(φ,λ) represents the ionospheric VTEC at the ionospheric IPP point (φ,λ); φ and λ represent the latitude and longitude of the ionospheric IPP point, respectively; n dmax represents the maximum degree of the spherical harmonics function; represents the normalized Legendre function of n degrees and m order; MC(n,m) represents the normalization function; and represent the model parameters to be estimated, respectively.

[0080] 7) Polynomial function: The polynomial model describes the ionospheric VTEC as a polynomial function of the latitude difference and the hour angle difference, and its mathematical expression is as follows:

[0081]

[0082] where represents the ionospheric TEC in the line-of-sight and zenith directions; and λ represent the geographic latitude and longitude of the ionospheric piercing point, respectively; t represents the observation time; and λ0 represent the geographic latitude and longitude of the ionospheric TEC modeling center point, respectively; t0 represents the hour angle corresponding to the middle time of modeling; n max and m max represent the maximum order of the polynomial function, respectively; E nm represents the model parameters to be estimated of the polynomial function.

[0083] 8) Generalized trigonometric series function GTSF: The mathematical expression of the generalized trigonometric series function model is as follows:

[0084]

[0085] wherein: denotes the latitude of the center point of the local ionospheric TEC modeling; h denotes a function related to the local time t at the ionospheric intersection point; n max ,m max denote the maximum order of the polynomial function and the trigonometric series function, respectively; E max ,C nm ,S k denotes the model coefficients to be estimated. k

[0086] 9) Inter-station partition method: a method for constructing large-scale high-precision grid ionospheric model using GPS data.

[0087] 10) IGGDCB: The IGGDCB method is proposed for the conditions that the commonly used DCB determination methods in the world are not suitable for the regional layout of ground monitoring stations of Beidou system in China. This method avoids the dependence on external information and a large number of GNSS reference stations while ensuring the accuracy and reliability of DCB parameter estimation. The related theories and technical achievements have been published in the international journal of geodesy.

[0088] Please refer to Figure 3 , the ionospheric free electrons on the GNSS signal propagation path are concentrated on a specified infinitely thin spherical surface, and the horizontal distribution of ionospheric TEC on the spherical surface is modeled, which is called ionospheric thin layer assumption. In the ionospheric thin layer assumption, the thin layer height H ion is usually selected at the peak of ionospheric density in the height direction, H ion is the height of the F2 layer electron density peak, which is between 350-450 km, and it is slightly different in different places and different seasons around the world.

[0089] The intersection of the satellite-to-receiver line and the ionospheric thin layer is called the ionospheric intersection point (IPP, Intersect Pierce Point). In the ionospheric thin layer assumption, the ionospheric TEC in the line-of-sight direction is compressed on the IPP point, and the ionospheric VTEC in the vertical direction of the point is used. The TEC in the line-of-sight direction (STEC, Slant TEC) and the TEC in the vertical direction can be converted by the projection function, and the most simple and commonly used projection function is the trigonometric projection function

[0090]

[0091] ​Where F(ε) represents the projection function at the ionospheric intersection point; α represents the zenith distance of the satellite relative to the ionospheric intersection point; R earth represents the radius of the earth; H ion represents the height of the ionospheric sheet; and ε represents the elevation angle of the satellite relative to the receiver.

[0092] Embodiment One

[0093] Please refer to Figure 1 and Figure 2 A method for constructing an ionospheric data analysis model, comprising the following steps:

[0094] S1, determining the satellite and receiver frequency bias based on the IGGDCB method, specifically:

[0095] By designing a satellite frequency bias stability discrimination standard, adaptively selecting a part of the frequency bias that meets the preset stability standard to construct a quasi-stable reference.

[0096] Based on the phase-smoothed pseudorange technology, the obtained ionospheric TEC information (including hardware delay) of GPS, GLONASS, BeiDou and Galileo satellites, and based on the "zero baseline", the difference between the TECs of the adjacent two receivers is obtained, and the "two-step method" of satellite and receiver hardware delay estimation solves the problem of accurate determination of differential code bias of Beidou regional system and Beidou global system under the condition of fewer monitoring stations; at the same time, a "quasi-stable" reference suitable for separating satellite and receiver differential code bias is constructed.

[0097] Thus, for reasonable separation of satellite and receiver frequency bias parameters, compared with the "zero mean" reference applied to all satellites in the existing method, this method can accurately determine the satellite and receiver frequency bias, effectively avoid the influence of poor stability of part of the satellite frequency bias on the estimation of other all frequency bias parameters, and further improve the reliability of parameter estimation.

[0098] S2, establishing a local ionospheric TEC model based on a generalized trigonometric series function, specifically:

[0099] An adaptive parameter selection strategy based on F test is adopted to automatically adjust the components in the generalized trigonometric series, and then select a trigonometric series function structure suitable for the local area. Thus, the fine variation characteristics of the ionospheric TEC in the region are obtained.

[0100] The local ionospheric TEC has obvious diurnal variation characteristics. To describe the ionospheric TEC variation in a long (e.g., one day) measurement section and ensure its accuracy, a mathematical function capable of effectively reflecting the (quasi-) diurnal variation of the ionospheric TEC with local time must be used. The polynomial model describes the ionospheric vTEC as a polynomial function of the latitude difference and the solar hour angle difference. To ensure the accuracy of the ionospheric TEC variation within a day, it is usually necessary to divide it into 6-8 measurement sections, and there is no theoretical guarantee of the continuity of the ionospheric TEC between the sections. By combining the polynomial function with the trigonometric series function with periodic characteristics, the reasonable and accurate simulation of the local ionospheric TEC variation can be effectively realized.

[0101] In practical applications, the statistical test method should be used to select appropriate components of the generalized trigonometric series function according to the characteristics of the local ionospheric TEC variation, so that the fitting accuracy of the ionospheric TEC is optimal. Therefore, the generalized trigonometric series function can effectively describe the details of the local ionospheric VTEC variation due to its adjustable number of parameters and certain physical meaning, compared with the commonly used segmented polynomial function and low-order spherical harmonic function.

[0102] S3, establishing a global ionospheric TEC model based on spherical harmonic functions, specifically:

[0103] The segmented linear interpolation function method is used to handle the continuity between adjacent time periods. The calculation of the ionospheric VTEC at any time is set to the ionospheric TEC model in the two adjacent time periods, and the ionospheric TEC models in the two adjacent time periods are seamlessly connected through a linear variation function related to time, so that the ionospheric variation in adjacent time periods is continuous.

[0104] Global ionospheric prediction data products are the key to realizing space ionospheric weather forecasting. Spherical harmonic functions are commonly used fitting functions for global ionospheric modeling, and the fitted spherical harmonic coefficients still have periodic variation characteristics of TEC. Through power spectrum analysis of long-term data of spherical harmonic coefficients, the periods of each order of spherical harmonic coefficients can be extracted. Based on this, the Fourier trigonometric series is used to fit the periodic variation of the spherical harmonic coefficients, the maximum posterior estimation model is constructed using the fitting residuals, and then the predicted spherical harmonic model coefficients are obtained by time extrapolation of the periodic terms and residual terms of the spherical harmonic coefficients, respectively. Finally, the predicted spherical harmonic coefficients are substituted into the spherical harmonic function to realize the prediction of the global ionospheric TEC.

[0105] S4, calculating the grid point ionospheric VTEC based on the inter-station zoning method;

[0106] The station inter-partition method is a method for constructing a large-scale high-precision grid ionospheric model by using GPS data. In the selected weight fitting method, a virtual ionospheric observation is introduced in the region where the ionospheric TEC is negative, the observation equation of the global spherical harmonic function ionospheric TEC model is reconstructed, and the weight of the virtual ionospheric observation is adaptively adjusted according to the calculation result, so that the optimal solution of the global spherical harmonic function ionospheric TEC model coefficient is obtained.

[0107] Specifically, due to the larger ocean area in the southern hemisphere, the ground reference stations are less and unevenly distributed, so that the observation data in the high-latitude area of the southern hemisphere is significantly reduced, and the global ionospheric TEC grid often appears negative in the high-latitude area of the southern hemisphere. In order to effectively solve the problem of negative value in the modeling of global ionospheric TEC, based on the idea of selected weight fitting, a virtual ionospheric observation is introduced in the region where the ionospheric TEC is negative, the observation equation of the global spherical harmonic function ionospheric TEC model is reconstructed, and the weight of the virtual ionospheric observation is adaptively adjusted according to the calculation result, so that the optimal solution of the global spherical harmonic function ionospheric TEC model coefficient is obtained.

[0108] The multi-scale ionospheric real-time and prediction model established in the project can be directly applied to the accurate correction of ionospheric delay error in Beidou / GNSS high-precision position service, and the Beidou / GNSS navigation and positioning precision and service performance are improved.

[0109] S5, the least square combination maximum posterior estimation method is used to realize the prediction of global ionospheric TEC, specifically:

[0110] S501, the trend item period of each order spherical harmonic coefficient is analyzed and extracted by the power spectrum estimation method;

[0111] S502, the residual of the fitting of multiple sets of spherical harmonic coefficients is obtained based on the least square method fitting;

[0112] S503, the autocovariance value of the random term parameter is calculated according to the fitting residual of the spherical harmonic coefficient, and the autocovariance function obtained by fitting the polynomial function is used to construct the variance and covariance matrix of the random term parameter according to the time interval;

[0113] S504, the trend item of the spherical harmonic coefficient is calculated based on the two-step solution, the random term of the spherical harmonic coefficient is obtained based on the maximum posterior estimation method, and the trend item and the random term parameter are time extrapolated respectively to obtain the predicted trend item and the random term;

[0114] S505, the predicted trend item and the random term are combined into the predicted spherical harmonic coefficient, which is substituted into the spherical harmonic function to calculate the VTEC value of each grid point, and finally the prediction of global ionospheric TEC is completed.

[0115] From the service range, the global / regional ionospheric data analysis model established by the project can meet the analysis needs of fine changes of the ionosphere at different spatial scales; from the service type, the post-event / real-time / predictive ionospheric data analysis model established by the project can meet the application needs of accurate post-event reanalysis, real-time fine monitoring and reliable prediction of medium and short-term changes of the ionosphere.

[0116] The improvement of the spatial ionospheric environment monitoring, forecasting and analysis capability brought by the research will effectively guarantee the safe operation capability of the power transmission network and improve the spatial ionospheric threat response capability of the power grid operation; on the other hand, the high-precision global and regional high-precision ionospheric monitoring products generated by the project will help to further improve the positioning accuracy and user experience of Beidou / GNSS mass user terminals (such as smart phones), and drive the rapid development of fields such as smart city construction, intelligent / automatic driving and other fields with high-precision location services as the core, with significant economic benefits and significant industry prospects.

[0117] Embodiment two

[0118] Please refer to Figure 4 An ionospheric data analysis model construction terminal, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the following steps when executing the computer program.

[0119] In summary, the ionospheric data analysis model construction method and terminal provided by the application realize fine modeling, monitoring and forecasting of global-scale and key regional ionospheric changes by fully utilizing multi-mode global satellite navigation (GNSS) technology and ground reference station observation resources, establish global / regional multi-scale ionospheric data analysis models, thereby greatly improving the fine monitoring of spatial ionospheric effects and the ionospheric threat warning capability, and providing sufficient protection for the site selection and construction of ultra-high voltage lines and the safe operation of power grids.

[0120] It should be noted that, for the foregoing method embodiments, in order to facilitate description, they are all described as a series of action combinations, but those skilled in the art should know that the application is not limited by the described action sequence, because according to the application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily essential to the application.

[0121] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0122] The above merely illustrates the embodiments of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process transformation, or direct or indirect application in other related technical fields, which are made by using the content of the present application specification and drawings, are also included in the patent protection scope of the present application.

Claims

1. An ionospheric data analysis model construction method, characterized in that, The method comprises the following steps: S1, determining the satellite-receiver frequency bias based on the IGGDCB method; S2, establishing a local ionospheric TEC model based on a generalized trigonometric series function; S3, establishing a global ionospheric TEC model based on a spherical harmonic function; S4, calculating the ionospheric VTEC at grid points based on an inter-station partition method; S5, realizing the prediction of the global ionospheric TEC by using a least square combined maximum posterior estimation method. The step S1 specifically comprises: By designing a satellite frequency bias stability discrimination criterion, a part of the frequency biases meeting the preset stability criterion are adaptively selected to construct a quasi-stable reference. The step S2 specifically comprises: An adaptive parameter selection strategy based on F test is used to automatically adjust the components in the generalized trigonometric series, and then select a trigonometric series function structure suitable for a local area. The step S3 specifically comprises: A segmented linear interpolation function method is used to process the continuity between adjacent time periods, and the calculation of the ionospheric VTEC at any time is set to the ionospheric TEC models in two adjacent time periods, and the ionospheric TEC models in two adjacent time periods are seamlessly connected by a linear variation function related to time, so that the ionospheric variation in adjacent time periods is continuous. The step S4 comprises: A selected weight fitting method is used to introduce a virtual ionospheric observation in the region where the ionospheric TEC is negative, re-construct the observation equation of the global spherical harmonic function ionospheric TEC model, and adaptively adjust the weight of the virtual ionospheric observation according to the calculation result to obtain the optimal solution of the global spherical harmonic function ionospheric TEC model coefficient. The step S5 specifically comprises: S501, analyzing and extracting the trend item period of each order of spherical harmonic coefficient by using a power spectrum estimation method; S502, fitting the residual error of the spherical harmonic coefficient by using a least square method to obtain a plurality of spherical harmonic coefficient fitting residual errors; S503, calculating the autocovariance value of the random item parameter according to the fitting residual error of the spherical harmonic coefficient, and fitting the autocovariance function obtained by using a polynomial function to construct the variance and covariance matrix of the random item parameter according to the time interval; S504, calculating the trend item of the spherical harmonic coefficient based on a two-step solution method, and obtaining the random item of the spherical harmonic coefficient based on a maximum posterior estimation method, and respectively performing time extrapolation on the trend item and the random item parameter to obtain the predicted trend item and random item; S505, combining the predicted trend item and random item into the predicted spherical harmonic coefficient, and substituting the predicted spherical harmonic coefficient into the spherical harmonic function to calculate the VTEC value at each grid point, and finally completing the prediction of the global ionospheric TEC.

2. An ionospheric data analysis model construction terminal comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor realizes the following steps when executing the computer program: S1, determining the satellite-receiver frequency bias based on the IGGDCB method; S2, establishing a local ionospheric TEC model based on a generalized trigonometric series function; S3, establishing a global ionospheric TEC model based on a spherical harmonic function; S4, calculating the ionospheric VTEC at grid points based on an inter-station partition method; S5, realizing the prediction of the global ionospheric TEC by using a least square combined maximum posterior estimation method. The step S1 specifically comprises: By designing the satellite frequency deviation stability discrimination criterion, the quasi-stable reference is constructed by adaptively selecting part of the frequency deviation that meets the preset stability criterion; The step S2 is specifically: An adaptive parameter selection strategy based on F test is adopted to automatically adjust the component items in the generalized trigonometric series, and then select the trigonometric series function structure suitable for the local area; The step S3 is specifically: A piecewise linear interpolation function method is used to solve the continuity problem between adjacent time periods, and the calculation of ionospheric VTEC in any time is set to the ionospheric TEC model in two adjacent time periods, and the ionospheric TEC model in two adjacent time periods is seamlessly connected through a linear function related to time, so that the ionospheric change in adjacent time periods is continuous; The step S4 includes: A weight selection fitting method is used to introduce a virtual ionospheric observation in the ionospheric TEC negative value area, restructure the observation equation of the global spherical harmonic function ionospheric TEC model, and adaptively adjust the weight of the virtual ionospheric observation according to the calculation result to obtain the optimal solution of the global spherical harmonic function ionospheric TEC model coefficient; The step S5 is specifically: S501, the trend item period of each order spherical harmonic coefficient is analyzed and extracted by power spectrum estimation method; S502, a plurality of spherical harmonic coefficient fitting residual errors are obtained by fitting the spherical harmonic coefficient based on the least square method; S503, the autocovariance value of the random term parameter is calculated according to the fitting residual error of the spherical harmonic coefficient, and the autocovariance function obtained by polynomial function fitting is used to construct the variance and covariance matrix of the random term parameter according to the time interval; S504, the trend item of the spherical harmonic coefficient is calculated based on the two-step solution, and the random term of the spherical harmonic coefficient is obtained based on the maximum posterior estimation method, and the trend item and the random term parameter are time extrapolated respectively to obtain the predicted trend item and the random term; S505, the predicted trend item and the random term are combined into the predicted spherical harmonic coefficient, and the VTEC value of each grid point is calculated by substituting the spherical harmonic function to finally complete the prediction of the global ionospheric TEC.

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