CN115857058A - Ionosphere data analysis model construction method and terminal thereof - Google Patents
Ionosphere data analysis model construction method and terminal thereof Download PDFInfo
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Abstract
The invention discloses a method for building an ionosphere data analysis model, which comprises the following steps: s1, accurately determining the frequency deviation between a satellite and a receiver based on an IGGDCB method; s2, establishing a local ionized layer TEC model based on a generalized trigonometric series function; s3, establishing a global ionized layer TEC model based on a spherical harmonic function; s4, calculating a grid point ionosphere VTEC based on an interstation partition method; and S5, forecasting the global ionized layer TEC by adopting a least square combination maximum posterior estimation method. The method realizes the fine modeling, monitoring and forecasting of the ionospheric change of the global scale and key regions, and establishes a global/regional multi-scale ionospheric data analysis model, thereby greatly improving the fine monitoring of the spatial ionospheric effect and the ionospheric threat early warning capability, and providing sufficient guarantee for the site selection construction of extra-high voltage lines and the safe operation of a power grid.
Description
Technical Field
The invention relates to the field of ionospheric data analysis, in particular to a method and a terminal for building an ionospheric data analysis model.
Background
The ionosphere is located at the earth's surface for 60 to 1000 kilometers, where there are a large number of free electrons and ions. Under the influence of the sun and geomagnetic activity, the distribution of the electron density of the ionized layer in the vertical and horizontal directions of the space is changed steadily and regularly along with time, season, geographical latitude and the like, including diurnal variation, seasonal variation, solar periodic variation and the like. When the sun has the phenomena of large black particle groups, crown holes, strong flare spots, severe substance projection and the like, a large amount of charged particle flow and high-energy rays enter a near-earth space environment; the explosive fluctuation of charged particle density, current system, electric field distribution and the like in the ionized layer caused by the large increase of charged particle flow and high-energy rays and the drastic change of an electromagnetic field can generate serious threat to the safety of space and ground technical systems such as a power transmission network.
The occurrence of a spatial ionospheric weather event is accompanied by a change in the earth's magnetic field. The extra-high voltage line has the characteristics of long transmission distance, high voltage and strong magnetic field around the line. When the magnetic field changes, the magnetic flux passing through the power grid loop changes, and strong induced current is generated on the power grid loop. This will lead to the following problems: (1) The voltage unbalance of the power transmission network in a short time influences the stable operation of the power grid; (2) The electric transmission and transformation equipment is burnt out due to high heat generated at a high magnetic flux density because of iron core saturation and magnetic flux overflow; (3) If the power transmission line is constructed on a medium with low conductivity (such as igneous rocks), strong induced current generated by the magnetic storm is mainly applied to the power grid, and further serious impact is caused to the operation of the power grid.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method and the terminal solve the key technical problems in accurate simulation after the spatial ionospheric change, real-time fine detection and medium-short term reliable prediction, form a distinctive global/regional multi-scale ionospheric modeling method and establish an ionospheric data analysis model for power grid safety application.
In order to solve the technical problems, the invention adopts a technical scheme that:
an ionospheric data analysis model building method includes the following steps:
s1, determining frequency deviation between a satellite and a receiver based on an IGGDCB method;
s2, establishing a local ionized layer TEC model based on a generalized trigonometric series function;
s3, establishing a global ionized layer TEC model based on a spherical harmonic function;
s4, calculating a grid point ionosphere VTEC based on an interstation partition method;
and S5, forecasting the global ionized layer TEC by adopting a least square combination maximum posterior estimation method.
In order to solve the technical problem, the invention adopts another technical scheme as follows:
an ionospheric data analysis model building terminal includes a memory, a processor, and a computer program stored on the memory and operable on the processor, wherein the processor implements the steps in an ionospheric data analysis model building method when executing the computer program.
The invention has the beneficial effects that: the method has the advantages that multimode global satellite navigation (GNSS) technology and ground reference station observation resources are fully utilized, fine modeling, monitoring and forecasting of ionospheric changes of global scales and key regions are realized, a global/regional multi-scale ionospheric data analysis model is established, beidou/GNSS navigation positioning accuracy and service performance are improved, space ionospheric environment monitoring, forecasting and analyzing capabilities are improved, fine monitoring and ionospheric threat early warning capabilities of space ionospheric effects are greatly improved, and sufficient guarantee is provided for extra-high voltage line site selection construction and power grid safe operation. The method can meet the analysis requirements of fine ionospheric changes on different spatial scales, and can meet the application requirements of post-analysis precision of the ionospheric changes, real-time fine monitoring, reliable prediction of medium-short term changes and the like.
Drawings
Fig. 1 is a flowchart of a method for establishing an ionospheric data analysis model according to an embodiment of the present invention;
fig. 2 is a general technical solution of an ionospheric data analysis model of a method for establishing an ionospheric data analysis model according to an embodiment of the present invention;
fig. 3 is a schematic diagram illustrating an ionospheric thin layer assumption of a method for establishing an ionospheric data analysis model according to an embodiment of the present invention;
fig. 4 is an architecture diagram of an ionospheric data analysis model building terminal according to an embodiment of the present invention.
Detailed Description
In order to explain the technical contents, the objects and the effects of the present invention in detail, the following description is made with reference to the accompanying drawings in combination with the embodiments.
An ionospheric data analysis model building method includes the following steps:
s1, determining frequency deviation between a satellite and a receiver based on an IGGDCB method;
s2, establishing a local ionized layer TEC model based on a generalized trigonometric series function;
s3, establishing a global ionized layer TEC model based on a spherical harmonic function;
s4, calculating a grid point ionosphere VTEC based on an interstation partition method;
and S5, forecasting the global ionized layer TEC by adopting a least square combination maximum posterior estimation method.
From the above description, the beneficial effects of the present invention are: the method has the advantages that multimode global satellite navigation (GNSS) technology and ground reference station observation resources are fully utilized, fine modeling, monitoring and forecasting of ionospheric changes of global scales and key regions are realized, a global/regional multi-scale ionospheric data analysis model is established, beidou/GNSS navigation positioning accuracy and service performance are improved, space ionospheric environment monitoring, forecasting and analyzing capabilities are improved, fine monitoring and ionospheric threat early warning capabilities of space ionospheric effects are greatly improved, and sufficient guarantee is provided for extra-high voltage line site selection construction and power grid safe operation. The method can meet the analysis requirements of fine ionospheric changes on different spatial scales, and can meet the application requirements of post-analysis precision of the ionospheric changes, real-time fine monitoring, reliable prediction of medium-short term changes and the like.
Further, the step S1 specifically includes:
by designing a satellite inter-frequency deviation stability judgment standard, a satellite construction quasi-stability standard of which part of inter-frequency deviation accords with a preset stability standard is adaptively selected.
From the above description, it can be known that a satellite with better partial inter-frequency deviation stability is adaptively selected to construct a new 'quasi-stationary' reference for reasonable separation of the satellite and receiver inter-frequency deviation parameters, and compared with the 'zero mean' reference applied to all satellites in the existing method, the method can effectively avoid the influence on the estimation of all other inter-frequency deviation parameters caused by the poor inter-frequency deviation stability of the partial satellite, and further improve the reliability of parameter estimation.
Further, the step S2 specifically includes:
and selecting a trigonometric series function structure suitable for a local area by adopting an adaptive parameter selection strategy based on F test and automatically adjusting the composition items in the generalized trigonometric series.
From the above description, it can be known that by combining the polynomial function and the trigonometric series function with periodic characteristics, a reasonable and accurate simulation of the local ionosphere TEC variation can be effectively achieved. And establishing a local ionized layer TEC model by adopting a Generalized Trigonometric Series Function (GTSF) from reference station to obtain the fine variation characteristic of the ionized layer TEC in the region.
Further, the step S3 specifically includes:
aiming at the continuity problem between adjacent time periods, processing by adopting a piecewise linear interpolation function method, setting the calculation of the ionized layer VTEC in any one time to ionized layer TEC models in 2 adjacent time periods, and seamlessly connecting the ionized layer TEC models in two adjacent time periods through a time-related linear change function to ensure that the ionized layer change in the adjacent time periods is continuous;
the step S4 includes:
and adopting a weight selection fitting method, introducing the virtual ionized layer observed quantity into an ionized layer TEC (thermal electric field emission technology) area with a negative value, reconstructing an observation equation of the global spherical harmonic ionized layer TEC model, and adaptively adjusting the weight of the virtual ionized layer observed quantity according to a calculation result to obtain the optimal solution of the global spherical harmonic ionized layer TEC model coefficient.
According to the description, the global ionized layer TEC model is established by adopting the Spherical Harmonic Function (SHF) to ensure the reasonable extrapolation of the ionized layer TEC in the observation-free area, so that the construction of the medium and short scale global ionized layer prediction data model is realized.
Further, the step S5 specifically includes:
s501, analyzing and extracting trend term periods of spherical harmonic coefficients of all orders by a power spectrum estimation method;
s502, fitting spherical harmonic coefficients based on a least square method to obtain a plurality of groups of fitted residual errors of the spherical harmonic coefficients;
s503, calculating an autocovariance value of the random item parameters according to the fitting residual error of the spherical harmonic coefficient, and constructing a variance covariance matrix of the random item parameters according to time intervals by using an autocovariance function obtained by polynomial function fitting;
s504, calculating to obtain a trend item of the spherical harmonic coefficient based on a two-step solution method, obtaining a random item of the spherical harmonic coefficient based on a maximum posterior evaluation method, and respectively carrying out time extrapolation on parameters of the trend item and the random item to obtain a forecasted trend item and a forecasted random item;
and S505, combining the forecasted trend term and the random term into forecasted spherical harmonic coefficients, substituting the spherical harmonic coefficients into a spherical harmonic function to calculate VTEC values of all grid points, and finally completing forecasting of the global ionized layer TEC.
According to the description, the ionized layer TEC of the grid point is comprehensively calculated by using the station boundary partition method for reference, the global ionized layer TEC grid is obtained, and the reliable prediction of the medium and short scale global ionized layer information is realized.
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, the processor implementing the following steps when executing the computer program:
s1, determining frequency deviation between a satellite and a receiver based on an IGGDCB method;
s2, establishing a local ionized layer TEC model based on a generalized trigonometric series function;
s3, establishing a global ionized layer TEC model based on a spherical harmonic function;
s4, calculating a grid point ionosphere VTEC based on an interstation partition method;
and S5, forecasting the global ionized layer TEC by adopting a least square combination maximum posterior estimation method.
From the above description, the beneficial effects of the present invention are: the method has the advantages that multimode global satellite navigation (GNSS) technology and ground reference station observation resources are fully utilized, fine modeling, monitoring and forecasting of ionospheric changes of global scales and key regions are realized, a global/regional multi-scale ionospheric data analysis model is established, beidou/GNSS navigation positioning accuracy and service performance are improved, space ionospheric environment monitoring, forecasting and analyzing capabilities are improved, fine monitoring and ionospheric threat early warning capabilities of space ionospheric effects are greatly improved, and sufficient guarantee is provided for extra-high voltage line site selection construction and power grid safe operation. The method can meet the analysis requirements of fine ionospheric changes on different spatial scales, and can meet the application requirements of post-analysis precision of the ionospheric changes, real-time fine monitoring, reliable prediction of medium-short term changes and the like.
Further, the step S1 specifically includes:
by designing a satellite inter-frequency deviation stability judgment standard, a satellite construction quasi-stability standard of which part of inter-frequency deviation accords with a preset stability standard is adaptively selected.
From the above description, it can be known that a satellite with better partial inter-frequency deviation stability is adaptively selected to construct a new 'quasi-stationary' reference for reasonable separation of the satellite and receiver inter-frequency deviation parameters, and compared with the 'zero mean' reference applied to all satellites in the existing method, the method can effectively avoid the influence on the estimation of all other inter-frequency deviation parameters caused by the poor inter-frequency deviation stability of the partial satellite, and further improve the reliability of parameter estimation.
Further, the step S2 specifically includes:
and selecting a trigonometric series function structure suitable for a local area by adopting an adaptive parameter selection strategy based on F test and automatically adjusting the composition items in the generalized trigonometric series.
From the above description, it can be known that by combining the polynomial function and the trigonometric series function with periodic characteristics, a reasonable and accurate simulation of the local ionosphere TEC variation can be effectively achieved. And establishing a local ionized layer TEC model by adopting a Generalized Trigonometric Series Function (GTSF) from reference station to obtain the fine variation characteristic of the ionized layer TEC in the region.
Further, the step S3 specifically includes:
aiming at the continuity problem between adjacent time periods, a piecewise linear interpolation function method is adopted for processing, the calculation of the ionized layer VTEC in any one time is set to ionized layer TEC models in 2 adjacent time periods, and the ionized layer TEC models in two adjacent time periods are seamlessly connected through a linear change function related to time, so that the ionized layer change in the adjacent time periods is continuous;
the step S4 includes:
and adopting a weight selection fitting method, introducing virtual ionized layer observed quantity into an ionized layer TEC (thermoelectric cooler) area with a negative value, reconstructing an observation equation of the global spherical harmonic ionized layer TEC model, and adaptively adjusting the weight of the virtual ionized layer observed quantity according to a calculation result to obtain the optimal solution of the global spherical harmonic ionized layer TEC model coefficient.
According to the description, the global ionized layer TEC model is established by adopting the Spherical Harmonic Function (SHF) to ensure the reasonable extrapolation of the ionized layer TEC in the observation-free area, so that the construction of the medium and short scale global ionized layer prediction data model is realized.
Further, the step S5 specifically includes:
s501, analyzing and extracting trend term periods of spherical harmonic coefficients of all orders by a power spectrum estimation method;
s502, fitting the spherical harmonic coefficients based on a least square method to obtain a plurality of groups of fitted residual errors of the spherical harmonic coefficients;
s503, calculating an autocovariance value of the random item parameters according to the fitting residual error of the spherical harmonic coefficient, and constructing a variance covariance matrix of the random item parameters according to time intervals by using an autocovariance function obtained by polynomial function fitting;
s504, calculating to obtain a trend item of the spherical harmonic coefficient based on a two-step solution method, obtaining a random item of the spherical harmonic coefficient based on a maximum post-test estimation method, and respectively performing time extrapolation on the trend item and the random item parameters to obtain a forecasted trend item and a forecasted random item;
and S505, combining the forecasted trend term and the random term into forecasted spherical harmonic coefficients, substituting the spherical harmonic coefficients into a spherical harmonic function to calculate VTEC values of all grid points, and finally completing forecasting of the global ionized layer TEC.
According to the description, the ionized layer TEC of the grid point is comprehensively calculated by using the inter-station partition method for reference, the global ionized layer TEC grid is obtained, and the reliable prediction of the medium-short scale global ionized layer information is realized.
The ionosphere data analysis model construction method and the terminal thereof can realize the fine modeling, monitoring and forecasting of the ionosphere change of the global scale and key areas, greatly improve the fine monitoring of the space ionosphere effect and the ionosphere threat early warning capability, provide sufficient guarantee for the site selection construction of extra-high voltage lines and the safe operation of a power grid, and are explained by a specific implementation mode as follows:
the noun explains:
1) GNSS: the Global Navigation Satellite System is based on civil Global Navigation Satellite positioning System. The system provides high-precision and high-reliability positioning service, realizes control and management of completely non-military parties, and can perform global navigation and positioning functions.
2) Ionized layer TEC: the total ionized layer electron concentration (TEC), also called ionized layer electron concentration column content, integral content, etc., is also an important parameter for ionized layer correction in precision positioning, navigation and radio wave science. Total Electron Content, the sum of the number of electrons per square meter from the bottom of the ionosphere (approximately 90 kilometers in height) to the top of the ionosphere (approximately 1000 kilometers in height).
3) Ionosphere VTEC: vertical total electron content, the vertical total electron content, is an important parameter for reflecting the characteristics of the ionosphere.
4) Deviation between frequencies: the navigation satellite usually broadcasts navigation signals of multiple frequency points, and because the navigation signal transmitting links of the frequency points on the satellite are not completely the same, the time delay generated when the navigation signals reach the electronic phase center of the satellite antenna through the satellite transmitting links is also different, which is called as hardware delay difference or inter-frequency deviation parameter.
5) A reference station: the reference station is a ground fixed observation station which continuously observes satellite navigation signals for a long time and transmits observation data to a data center in real time or at regular time through a communication facility.
6) Spherical harmonic function SHF: the Spherical harmony function has an excellent mathematical structure as a function for describing global variation physical quantity, and has become one of the main function models for describing the global ionized layer TEC, as shown in the following formula:
wherein VTEC (Φ, λ) represents the ionosphere VTEC at the ionosphere IPP point (Φ, λ); phi and lambda respectively represent the latitude and longitude of the ionosphere IPP point; n is a radical of an alkyl radical dmax Representing the maximum degree of the spherical harmonic;
a normalized Legendre function representing an order of n degrees m; MC (n, m) represents a normalization function; />
And &>
Respectively representing the model parameters to be estimated.
7) Polynomial function: the polynomial model describes the ionospheric VTEC as a polynomial function that varies with latitude difference and solar time angle difference, with the mathematical expression:
in the formula (I), the compound is shown in the specification,
an ionosphere TEC representing a line of sight and zenith direction; />
And λ represents the geographical latitude and longitude, respectively, at the ionospheric puncture point; t represents an observation time; />
And λ 0 Respectively representing the geographical latitude and longitude of the ionized layer TEC modeling central point; t is t 0 Representing the solar time angle corresponding to the modeling intermediate time; n is max And m max Respectively representing the maximum order of the polynomial function; e nm The model parameters to be estimated representing the polynomial function.
8) Generalized trigonometric series function GTSF: the mathematical expression of the Generalized trigonometric series function model is as follows:
wherein:
representing the latitude of the modeling central point of the local ionized layer TEC; h represents a function related to t at the location of the ionospheric intersection; n is max ,m max And k is max Respectively representing the maximum order of a polynomial function and a trigonometric series function; e nm ,C k ,S k Representing the model coefficients to be estimated.
9) An inter-station partition method: a method for constructing a large-scale high-precision grid ionosphere model by utilizing GPS data.
10 IGGDCB: the IGGDCB method is provided aiming at the condition that a DCB determination method commonly used internationally is not suitable for being mainly used by Beidou system ground monitoring stations in China, and the method avoids the dependence of the commonly used method on external information and a large number of GNSS reference stations while ensuring the accuracy and reliability of DCB parameter estimation. The theory and technical result are published in the Journal of geodesic of the international geodesic publication.
Referring to fig. 3, the ionospheric free electrons in the GNSS signal propagation path are concentrated on a sphere of specified height infinity, on which the horizontal distribution of ionospheric TEC is modeled, referred to as the ionospheric thin layer hypothesis. In the ionospheric sheet hypothesis, the sheet height H ion Usually selected at the peak of the ionized layer density in the height direction, H ion Is the height of the peak of the electron density of the F2 layer, is between 350 and 450km, and is slightly different in different seasons in different parts of the world.
The intersection of the satellite-to-receiver connection and the ionospheric membrane is called the ionospheric Point (IPP). The ionospheric thin layer hypothesis is that the ionospheric TEC in the line-of-sight direction is all compressed at the IPP point and is represented by the ionospheric VTEC in the perpendicular direction to this point. The TEC (STEC, slant TEC) in the sight line direction and the TEC in the vertical direction can be converted through a projection function, and the simplest and most common projection function is a trigonometric projection function
Wherein F (epsilon) represents a projection function at an ionospheric intersection; α represents the zenith distance of the satellite relative to the ionosphere intersection; r earth Represents the radius of the earth; h ion Indicating the height of the ionospheric membrane; epsilon represents the altitude angle of the satellite relative to the receiver.
Example one
Referring to fig. 1 and fig. 2, a method for building an ionospheric data analysis model includes the following steps:
s1, determining the frequency deviation between a satellite and a receiver by an IGGDCB-based method, specifically:
by designing a satellite inter-frequency deviation stability judgment standard, a satellite construction quasi-stability standard of which part of inter-frequency deviation accords with a preset stability standard is adaptively selected.
Ionosphere TEC information (including hardware delay) corresponding to GPS, GLONASS, beiDou and Galileo satellites processed based on a phase smoothing pseudorange technology is processed, the difference between two adjacent receivers TEC obtained by processing is realized based on zero baseline, and a two-step method for estimating the hardware delay of the satellite and the receiver well solves the problem of accurately determining the differential code deviation of a Beidou regional system and a Beidou global system under the condition of few monitoring stations; meanwhile, a 'quasi-stable' reference suitable for differential code deviation separation of a satellite and a receiver is constructed.
Therefore, the method is used for reasonably separating the frequency deviation parameters of the satellite and the receiver, and compared with the zero mean value reference applied to all satellites in the existing method, the method can accurately determine the frequency deviation of the satellite and the receiver, can effectively avoid the influence on the estimation of all other frequency deviation parameters caused by poor stability of the frequency deviation of part of satellites, and further improves the reliability of parameter estimation.
S2, establishing a local ionized layer TEC model based on the generalized trigonometric series function, which specifically comprises the following steps:
and selecting a trigonometric series function structure suitable for a local area by adopting an adaptive parameter selection strategy based on F test and automatically adjusting the composition items in the generalized trigonometric series. Therefore, the fine variation characteristic of the ionized layer TEC in the area can be obtained.
The local ionized layer TEC has obvious day-of-week change characteristics, and a mathematical function capable of effectively reflecting the day-of-week change of the ionized layer TEC along with the local time is required to be utilized to describe the change of the ionized layer TEC in a longer (such as one day) measuring section and ensure the precision of the ionized layer TEC; the ionized layer vTEC is described as a polynomial function changing along with latitude difference and solar time angle difference by the polynomial model, the ionized layer vTEC changing in one day is described by the polynomial function, the ionized layer vTEC changing in one day generally needs to be divided into 6-8 measuring sections to ensure the precision, and the continuity of the ionized layer vTEC among the measuring sections is not theoretically ensured. By combining the polynomial function and the trigonometric series function with periodic characteristics, reasonable and accurate simulation of local ionized layer TEC change can be effectively realized.
In practical application, a proper composition item of the generalized trigonometric series function is selected by adopting a statistical test method according to the change characteristics of the local ionized layer TEC, so that the fitting precision of the ionized layer TEC is optimal. Therefore, the generalized trigonometric series function has certain physical meaning due to adjustable parameter number, and can more effectively describe the details of the change of the local ionospheric VTEC compared with the common piecewise polynomial function and the low-order spherical harmonic function.
S3, establishing a global ionized layer TEC model based on the spherical harmonic function, specifically:
the method is characterized in that a piecewise linear interpolation function method is adopted for processing continuity problems between adjacent time periods, calculation of the ionized layer VTEC in any one time is set to ionized layer TEC models in 2 adjacent time periods, and the ionized layer TEC models in two adjacent time periods are connected seamlessly through a linear change function related to time, so that ionized layer changes in the adjacent time periods are continuous.
The global ionosphere prediction data product is the key to realizing the spatial ionosphere weather forecast. The spherical harmonic function is a common fitting function for global ionosphere modeling, and the fitted spherical harmonic coefficient still has the periodic variation characteristic of TEC. According to the project, the period of each order of spherical harmonic coefficient can be extracted through power spectrum analysis of the spherical harmonic coefficient long-term data, a Fourier trigonometric series is adopted to fit the periodic change of the spherical harmonic coefficient based on the period change, a maximum post-test estimation model is constructed by using the fitting residual error, then the period term and the residual error term of the spherical harmonic coefficient are respectively subjected to time extrapolation to obtain the predicted spherical harmonic model coefficient, and finally the predicted spherical harmonic coefficient is substituted into the spherical harmonic function to realize the prediction of the global ionized layer TEC.
S4, calculating a grid point ionosphere VTEC based on an interstation partition method;
the inter-station partition method is a method for constructing a large-scale high-precision grid ionosphere model by using GPS data. And adopting a weight selection fitting method, introducing virtual ionized layer observed quantity into an ionized layer TEC (thermoelectric cooler) area with a negative value, reconstructing an observation equation of the global spherical harmonic ionized layer TEC model, and adaptively adjusting the weight of the virtual ionized layer observed quantity according to a calculation result to obtain the optimal solution of the global spherical harmonic ionized layer TEC model coefficient.
Specifically, the ocean area of the southern hemisphere is large, and the ground reference stations are distributed less and unevenly, so that observation data of a high-latitude area of the southern hemisphere are obviously reduced, and a global ionized layer TEC grid frequently has a negative value in the high-latitude area of the southern hemisphere. In order to effectively solve the problem that a negative value occurs during modeling of the global ionized layer TEC, based on the thought of 'weight selection fitting', virtual ionized layer observed quantity is introduced into a region where the ionized layer TEC is a negative value, an observation equation of the global spherical harmonic ionized layer TEC model is reconstructed, the weight of the virtual ionized layer observed quantity is adaptively adjusted according to a calculation result, and finally the optimal solution of the global spherical harmonic ionized layer TEC model coefficient is found.
The multi-scale ionosphere real-time and prediction model established by the project can be directly applied to ionosphere delay error accurate correction in Beidou/GNSS high-precision position service, and the Beidou/GNSS navigation positioning precision and service performance are improved.
S5, forecasting of the global ionized layer TEC is achieved by adopting a least square combination maximum posterior estimation method, and the method specifically comprises the following steps:
s501, analyzing and extracting trend term periods of spherical harmonic coefficients of all orders by a power spectrum estimation method;
s502, fitting spherical harmonic coefficients based on a least square method to obtain a plurality of groups of fitted residual errors of the spherical harmonic coefficients;
s503, calculating an autocovariance value of the random item parameters according to the fitting residual error of the spherical harmonic coefficients, and constructing a variance covariance matrix of the random item parameters according to time intervals by using an autocovariance function obtained by polynomial function fitting;
s504, calculating to obtain a trend item of the spherical harmonic coefficient based on a two-step solution method, obtaining a random item of the spherical harmonic coefficient based on a maximum posterior evaluation method, and respectively carrying out time extrapolation on parameters of the trend item and the random item to obtain a forecasted trend item and a forecasted random item;
and S505, combining the predicted trend term and the random term into a predicted spherical harmonic coefficient, substituting the spherical harmonic coefficient into the spherical harmonic function to calculate the VTEC value of each grid point, and finally completing prediction of the global ionized layer TEC.
From the service range, the global/regional ionospheric data analysis model established by the project can meet the analysis requirements of fine ionospheric changes on different spatial scales; from the aspect of service types, the post/real-time/prediction ionosphere data analysis model established by the project can meet the application requirements of post-accurate re-analysis of ionosphere changes, real-time fine monitoring, reliable prediction of medium and short term changes and the like.
The improvement of the monitoring, forecasting and analyzing capabilities of the spatial ionosphere environment brought by the research can effectively guarantee the safe operation capability of the power transmission network and improve the response capability of the spatial ionosphere threat of the power grid operation; on the other hand, high-precision global and regional high-precision ionosphere monitoring products produced by the project are beneficial to further improving the positioning precision and the use experience of the Beidou/GNSS mass user terminal (such as a smart phone), and drive rapid development in the fields such as smart city construction, intelligent/automatic driving and the like with high-precision position service as a core, and have remarkable economic benefit and industrial prospect.
Example two
Referring to fig. 4, an ionospheric data analysis model building terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program.
In summary, the ionosphere data analysis model construction method and the terminal thereof provided by the invention realize the fine modeling, monitoring and forecasting of global scale and key area ionosphere changes by fully utilizing the multimode Global Navigation Satellite System (GNSS) technology and the ground reference station observation resources, and establish a global/regional multi-scale ionosphere data analysis model, thereby greatly improving the fine monitoring of space ionosphere effect and the ionosphere threat early warning capability, and providing sufficient guarantee for extra-high voltage line site selection construction and power grid safe operation.
It should be noted that for simplicity and convenience of description, the above-described method embodiments are shown as a series of combinations of acts, but it should be understood by those skilled in the art that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no acts or modules are necessarily required of the invention.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes performed by the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A method for building an ionospheric data analysis model is characterized by comprising the following steps:
s1, determining frequency deviation between a satellite and a receiver based on an IGGDCB method;
s2, establishing a local ionized layer TEC model based on a generalized trigonometric series function;
s3, establishing a global ionized layer TEC model based on a spherical harmonic function;
s4, calculating a grid point ionosphere VTEC based on an interstation partition method;
and S5, forecasting the global ionized layer TEC by adopting a least square combination maximum test estimation method.
2. The method for establishing an ionospheric data analysis model according to claim 1, wherein the step S1 specifically comprises:
by designing a satellite inter-frequency deviation stability judgment standard, a satellite construction quasi-stability standard of which part of inter-frequency deviation accords with a preset stability standard is adaptively selected.
3. The ionospheric data analysis model construction method according to claim 1, wherein the step S2 specifically is:
and selecting a trigonometric series function structure suitable for a local area by adopting an adaptive parameter selection strategy based on F test and automatically adjusting the composition items in the generalized trigonometric series.
4. The ionospheric data analysis model construction method according to claim 1, wherein the step S3 specifically is:
aiming at the continuity problem between adjacent time periods, processing by adopting a piecewise linear interpolation function method, setting the calculation of the ionized layer VTEC in any one time to ionized layer TEC models in 2 adjacent time periods, and seamlessly connecting the ionized layer TEC models in two adjacent time periods through a time-related linear change function to ensure that the ionized layer change in the adjacent time periods is continuous;
the step S4 includes:
and adopting a weight selection fitting method, introducing virtual ionized layer observed quantity into an ionized layer TEC (thermoelectric cooler) area with a negative value, reconstructing an observation equation of the global spherical harmonic ionized layer TEC model, and adaptively adjusting the weight of the virtual ionized layer observed quantity according to a calculation result to obtain the optimal solution of the global spherical harmonic ionized layer TEC model coefficient.
5. The method for establishing an ionospheric data analysis model according to claim 1, wherein the step S5 specifically comprises:
s501, analyzing and extracting trend term periods of spherical harmonic coefficients of each order by a power spectrum estimation method;
s502, fitting the spherical harmonic coefficients based on a least square method to obtain a plurality of groups of fitted residual errors of the spherical harmonic coefficients;
s503, calculating an autocovariance value of the random item parameters according to the fitting residual error of the spherical harmonic coefficients, and constructing a variance covariance matrix of the random item parameters according to time intervals by using an autocovariance function obtained by polynomial function fitting;
s504, calculating to obtain a trend item of the spherical harmonic coefficient based on a two-step solution method, obtaining a random item of the spherical harmonic coefficient based on a maximum posterior evaluation method, and respectively carrying out time extrapolation on parameters of the trend item and the random item to obtain a forecasted trend item and a forecasted random item;
and S505, combining the forecasted trend term and the random term into forecasted spherical harmonic coefficients, substituting the spherical harmonic coefficients into a spherical harmonic function to calculate VTEC values of all grid points, and finally completing forecasting of the global ionized layer TEC.
6. 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, wherein the processor when executing the computer program implements the steps of:
s1, determining frequency deviation between a satellite and a receiver based on an IGGDCB method;
s2, establishing a local ionized layer TEC model based on a generalized trigonometric series function;
s3, establishing a global ionized layer TEC model based on a spherical harmonic function;
s4, calculating a grid point ionosphere VTEC based on an interstation partition method;
and S5, forecasting the global ionized layer TEC by adopting a least square combination maximum posterior estimation method.
7. The ionospheric data analysis model construction terminal according to claim 6, wherein the step S1 specifically is:
by designing a satellite inter-frequency deviation stability judgment standard, a satellite construction quasi-stability standard of which part of inter-frequency deviation accords with a preset stability standard is adaptively selected.
8. The ionospheric data analysis model construction terminal according to claim 6, wherein the step S2 specifically is:
and selecting a trigonometric series function structure suitable for a local area by adopting an adaptive parameter selection strategy based on F test and automatically adjusting the composition items in the generalized trigonometric series.
9. The ionospheric data analysis model construction terminal according to claim 6, wherein the step S3 specifically is:
aiming at the continuity problem between adjacent time periods, processing by adopting a piecewise linear interpolation function method, setting the calculation of the ionized layer VTEC in any one time to ionized layer TEC models in 2 adjacent time periods, and seamlessly connecting the ionized layer TEC models in two adjacent time periods through a time-related linear change function to ensure that the ionized layer change in the adjacent time periods is continuous;
the step S4 includes:
and adopting a weight selection fitting method, introducing virtual ionized layer observed quantity into an ionized layer TEC (thermoelectric cooler) area with a negative value, reconstructing an observation equation of the global spherical harmonic ionized layer TEC model, and adaptively adjusting the weight of the virtual ionized layer observed quantity according to a calculation result to obtain the optimal solution of the global spherical harmonic ionized layer TEC model coefficient.
10. The ionospheric data analysis model construction terminal of claim 6, wherein the step S5 specifically is:
s501, analyzing and extracting trend term periods of spherical harmonic coefficients of each order by a power spectrum estimation method;
s502, fitting spherical harmonic coefficients based on a least square method to obtain a plurality of groups of fitted residual errors of the spherical harmonic coefficients;
s503, calculating an autocovariance value of the random item parameters according to the fitting residual error of the spherical harmonic coefficient, and constructing a variance covariance matrix of the random item parameters according to time intervals by using an autocovariance function obtained by polynomial function fitting;
s504, calculating to obtain a trend item of the spherical harmonic coefficient based on a two-step solution method, obtaining a random item of the spherical harmonic coefficient based on a maximum posterior evaluation method, and respectively carrying out time extrapolation on parameters of the trend item and the random item to obtain a forecasted trend item and a forecasted random item;
and S505, combining the forecasted trend term and the random term into forecasted spherical harmonic coefficients, substituting the spherical harmonic coefficients into a spherical harmonic function to calculate VTEC values of all grid points, and finally completing forecasting of the global ionized layer TEC.
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