A GNSS correction method and system, and a geocentric motion inversion method and system

Abstract

The present invention belongs to the field of navigation and positioning technology, and discloses a GNSS correction method and system, and a geocentric motion inversion method and system. The present invention first obtains GNSS station coordinate sequence data, and then eliminates selected non-environmental load signals from the GNSS station coordinate sequence data, wherein the selected non-environmental load signals include one or more types of non-environmental load signals such as annual intersection errors, noise signals, and thermoelastic deformations, to obtain corrected GNSS data. On this basis, the present invention can also use the corrected GNSS data to perform geocentric motion inversion to obtain inversion information. The present invention not only proposes a new GNSS correction method, but also can improve the accuracy of geocentric motion inversion based on GNSS data.

Description

Technical Field

[0001] The present invention belongs to the field of navigation and positioning technology, and more specifically, relates to a GNSS correction method and system, and a geocentric motion inversion method and system. Background Art

[0002] The global large-scale redistribution of surface loads causes the center of the solid Earth's shape to move relative to its center of mass. This movement is called geocentric motion (GCM). Accurate observation of geocentric motion is not only important for analyzing the redistribution of surface loads, but also one of the requirements of millimeter-level geodesy. For example, GCM affects satellite orbit altimetry data, which in turn affects the accuracy of sea level observations.

[0003] The global large-scale redistribution of surface load will also cause surface deformation. With the continuous development and improvement of GNSS (Global Navigation Satellite System) technology, this deformation can be accurately observed. The surface load theory can be used to further convert this observed deformation into geocentric motion. This method of inverting the center of the earth is called the first-order deformation method. The first-order deformation method based on GNSS data has been proven to be one of the stable and effective methods for geocentric motion. However, in addition to this deformation, the GNSS station coordinate data currently available also contains many other signals, collectively referred to as non-environmental load signals. Although the accuracy of the GNSS station coordinate sequence has been continuously improved in recent years and the data source has become increasingly abundant, the presence of non-environmental load signals will be mixed into the geocentric motion inverted using this method, resulting in inaccurate results of the inverted geocentric motion. Summary of the invention

[0004] The present invention solves the problem in the prior art that the result of inverting the geocentric motion based on GNSS data is not accurate enough by providing a GNSS correction method and system and a geocentric motion inversion method and system.

[0005] In a first aspect, the present invention provides a GNSS correction method, comprising: acquiring GNSS station coordinate sequence data; eliminating selected non-environmental load signals from the GNSS station coordinate sequence data to obtain corrected GNSS data; the selected non-environmental load signals include one or more types of non-environmental load signals selected from annual intersection errors, noise signals, and thermoelastic deformations.

[0006] Preferably, after acquiring the GNSS station coordinate sequence data and before eliminating the selected non-environmental load signal, the method further includes: detecting and eliminating gross errors in the GNSS station coordinate sequence data, and eliminating one or more of trend items, jumps, and post-seismic deformations.

[0007] Preferably, the intersection annual error, the noise signal, and the thermoelastic deformation are respectively used as the first type of non-environmental load signal, the second type of non-environmental load signal, and the third type of non-environmental load signal, and are eliminated in sequence.

[0008] Preferably, a least square fitting function is constructed to fit the GNSS station coordinate sequence data, wherein the least square fitting function includes an anniversary term, a semi-anniversary term and an intersection year term; the intersection year term in the fitting formula is removed to eliminate the intersection year error.

[0009] Preferably, the least squares fitting function is expressed as follows:

[0010]

[0011]

[0012]

[0013] Where E0(t), N0(t), and U0(t) are the GNSS station coordinate sequences in the three directions of E, N, and U respectively, and t is the time corresponding to the GNSS station coordinate sequence. They are the annual terms in the three directions of E, N, and U, ω1 is the annual angular velocity, A 11 , A 21 , A 31 is the annual amplitude obtained by fitting, is the annual phase obtained by fitting, is the semiannual term, ω2 is the semiannual term angular velocity, A 12 , A 22 , A 32 is the half-annual amplitude obtained by fitting, is the semi-annual phase obtained by fitting, is the annual term of the first m intersection points, α i is the angular velocity of the ith intersection point, B 1i , B 2i , B 3i is the amplitude of the annual term of the i-th intersection obtained by fitting, β 1i , β 2i , β 3i is the phase of the annual term of the i-th intersection obtained by fitting.

[0014] Preferably, the noise signal includes a high-frequency noise signal and a low-frequency noise signal; the GNSS station coordinate sequence data is decomposed into N components using singular spectrum analysis, the N components are divided into the first s main components and the remaining components, and the remaining components in the decomposition formula are removed to eliminate the high-frequency noise signal; the main frequencies of the first s main components are analyzed, and the low-frequency signals with the main frequency of the set years are eliminated to eliminate the low-frequency noise signal.

[0015] Preferably, a spherical thermoelastic deformation model is used to calculate the thermoelastic deformation in three directions E, N and U, and the calculated thermoelastic deformation is eliminated from the GNSS station coordinate sequence data.

[0016] In a second aspect, the present invention provides a GNSS correction system, comprising:

[0017] A data acquisition unit, used to acquire GNSS station coordinate sequence data;

[0018] A data correction unit, used to eliminate selected non-environmental load signals from the GNSS station coordinate sequence data to obtain corrected GNSS data;

[0019] The GNSS correction system is used to execute the steps in the above-mentioned GNSS correction method.

[0020] In a third aspect, the present invention provides a method for geocentric motion inversion, comprising the following steps:

[0021] Obtaining corrected GNSS data using the above GNSS correction method;

[0022] The corrected GNSS data is used to perform geocentric motion inversion to obtain inversion information.

[0023] In a fourth aspect, the present invention provides a geocentric motion inversion system, comprising:

[0024] A GNSS correction system, used to execute the steps in the above GNSS correction method to obtain corrected GNSS data;

[0025] The inversion system is used to perform geocentric motion inversion using the corrected GNSS data to obtain inversion information.

[0026] One or more technical solutions provided in the present invention have at least the following technical effects or advantages:

[0027] On the one hand, the present invention takes into account that the presence of non-environmental load signals in the GNSS coordinate sequence will cause inaccurate results of the inversion of geocentric motion. At the same time, taking into account that the components of non-environmental load signals in the GNSS sequence are complex, and theoretically only large-scale global non-environmental load signals will have a greater impact on geocentric motion, the present invention first determines the type of non-environmental load signals that need to be removed, and then selects the most appropriate method to model them according to the characteristics of different signals to eliminate the non-environmental load signals in the GNSS geocentric motion, thereby effectively improving the accuracy of geocentric motion inversion based on GNSS data. Specifically, the present invention first obtains GNSS station coordinate sequence data, and then eliminates the selected non-environmental load signals (including one or more types of non-environmental load signals in the annual error of the intersection, noise signal, and thermoelastic deformation) from the GNSS station coordinate sequence data to obtain the corrected GNSS data. On this basis, the present invention can also use the corrected GNSS data to perform geocentric motion inversion to obtain inversion information. The present invention not only proposes a new GNSS correction method, but also can improve the accuracy of geocentric motion inversion based on GNSS data. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A schematic flow chart of a geocentric motion inversion method provided in Example 3 of the present invention;

[0029] Figure 2 The Z-direction geocentric comparison map is obtained by using the geocentric motion inversion method with environmental load, the geocentric motion inversion method before eliminating non-environmental load signals, and a geocentric motion inversion method provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0030] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0031] Embodiment 1:

[0032] Embodiment 1 provides a GNSS correction method, which mainly includes:

[0033] Get GNSS station coordinate sequence data;

[0034] Selected non-environmental load signals are eliminated from the GNSS station coordinate sequence data to obtain corrected GNSS data; the selected non-environmental load signals include one or more types of non-environmental load signals selected from annual intersection errors, noise signals, and thermoelastic deformations.

[0035] In addition, after acquiring the GNSS station coordinate sequence data and before eliminating the selected non-environmental load signal, the method may also include: detecting and eliminating gross errors in the GNSS station coordinate sequence data, and eliminating one or more of trend items, jumps, and post-seismic deformations.

[0036] Preferably, the intersection annual error, the noise signal, and the thermoelastic deformation can be respectively used as the first type of non-environmental load signal, the second type of non-environmental load signal, and the third type of non-environmental load signal, and eliminated in sequence.

[0037] The present invention selects the above three types of signals from the complex GNSS non-environmental load signals for analysis, and selects the appropriate modeling order and modeling method according to the characteristics of different types of signals, which can simultaneously eliminate the influence of the three types of non-environmental load signals on the GNSS inversion of the geocentric motion. Among them, the high-frequency noise, low-frequency noise modeled by singular spectrum analysis (SSA) and the thermoelastic deformation modeled by the sphere thermoelastic deformation model are all introduced into the GNSS geocentric motion inversion for the first time. That is, the present invention finds that the influence of the three types of non-environmental load signals on the GNSS inversion of the geocentric motion cannot be ignored, and fully considers the influence of the annual error of the intersection, high-frequency noise, low-frequency noise and thermoelastic deformation on the GNSS inversion of the geocentric motion, so that the geocentric motion obtained by the inversion of the GNSS station coordinate sequence is more accurate, and the non-environmental load error is greatly reduced. The present invention uses different methods to accurately model and remove the three types of non-environmental load signals before using the GNSS sequence to invert the geocentric motion, effectively avoiding the influence of multiple non-environmental load signals in the traditional method, and improving the accuracy of the GNSS inversion of the geocentric motion.

[0038] The processing of various types of non-environmental load signals is described below.

[0039] (1) Annual error of intersection point.

[0040] A least square fitting function is constructed to fit the GNSS station coordinate sequence data, wherein the least square fitting function includes an anniversary term, a semi-anniversary term and an intersection year term; and the intersection year term in the fitting formula is removed to eliminate the intersection year error.

[0041] The least squares fitting function is expressed as follows:

[0042]

[0043]

[0044]

[0045] Where E0(t), N0(t), and U0(t) are the GNSS station coordinate sequences in the three directions of E, N, and U respectively, and t is the time corresponding to the GNSS station coordinate sequence. They are the annual terms in the three directions of E, N, and U, ω1 is the annual angular velocity, A 11 , A 21 , A 31 is the annual amplitude obtained by fitting, is the annual phase obtained by fitting, is the semiannual term, ω2 is the semiannual term angular velocity, A 12 , A 22 , A 32 is the half-annual amplitude obtained by fitting, is the semi-annual phase obtained by fitting, is the first m intersection year term, m can be 4 to 8, α i is the angular velocity of the ith intersection point, B 1i , B 2i , B 3i is the amplitude of the annual term of the i-th intersection obtained by fitting, β 1i , β 2i , β 3i is the phase of the annual term of the i-th intersection obtained by fitting.

[0046] (2) Noise signal.

[0047] The noise signal includes a high-frequency noise signal and a low-frequency noise signal; the GNSS station coordinate sequence data is decomposed into N components using singular spectrum analysis, the N components are divided into the first s main components (s can be 8 to 12) and the remaining components, and the remaining components in the decomposition formula are removed to eliminate the high-frequency noise signal; the main frequencies of the first s main components are analyzed, and low-frequency signals with a main frequency of a set period (the set period can be 2 to 10 years) are eliminated to eliminate the low-frequency noise signal.

[0048] (3) Thermoelastic deformation.

[0049] The thermoelastic deformation model of a sphere is used to calculate the thermoelastic deformation in three directions, namely, E, N and U, and the calculated thermoelastic deformation is eliminated from the GNSS station coordinate sequence data.

[0050] Embodiment 2:

[0051] Embodiment 2 provides a GNSS correction system, including:

[0052] A data acquisition unit, used to acquire GNSS station coordinate sequence data;

[0053] A data correction unit, used to eliminate selected non-environmental load signals from the GNSS station coordinate sequence data to obtain corrected GNSS data;

[0054] The GNSS correction system is used to execute the steps in the GNSS correction method described in Example 1.

[0055] Since the functions of each unit in the GNSS correction system provided in Example 2 correspond to the steps in the GNSS correction method provided in Example 1, they can be understood by referring to the description of Example 1 and will not be repeated here.

[0056] On the basis of Example 1, the present invention further provides a geocentric motion inversion method and a geocentric motion inversion system, which are described below with reference to Example 3 and Example 4, respectively.

[0057] Embodiment 3:

[0058] Embodiment 3 provides a method for geocentric motion inversion, comprising the following steps:

[0059] Obtaining corrected GNSS data using the GNSS correction method described in Example 1;

[0060] The corrected GNSS data is used to perform geocentric motion inversion to obtain inversion information.

[0061] The following is an example of Example 3 with reference to specific parameters.

[0062] A method for inversion of geocentric motion, see Figure 1 , including the following steps:

[0063] Step 1: Obtain GNSS station coordinate sequence data.

[0064] That is, obtain high-precision GNSS station coordinate sequence data of globally evenly distributed stations. Specifically, the inversion of geocentric motion requires globally evenly distributed GNSS station network data, so it is necessary to screen the stations to be used. And the higher the accuracy of GNSS data, the higher the accuracy of geocentric motion. Obtain the GNSS station coordinate sequence of the global station network, perform gross error detection and elimination, eliminate trend items and post-seismic deformation, and use it as the input data for inversion of geocentric motion.

[0065] In this embodiment, the IGS third reprocessed data, which integrates the three observation systems of GPS, GLONASS and Galileo, is used. Before analysis, the trend item, jump, post-earthquake deformation, etc. of the data are eliminated. This data is the latest GNSS reprocessed data.

[0066] To invert the geocentric motion, it is necessary to select GNSS stations that are evenly distributed around the world. Here, the IGS20 frame station is used, with a total of 332 stations. The observation data of these stations are relatively complete. In order to further ensure the data quality during the geocentric motion inversion, these 332 stations are further screened, and only the stations with a time span greater than 8 years and less than 20% data missing are retained. A total of 281 stations are used for the final inversion.

[0067] Step 2: Eliminate the annual intersection error from the GNSS station coordinate sequence.

[0068] The present invention determines that the first non-environmental load signal to be eliminated is the intersection annual error. A least square fitting function is constructed to fit the station coordinate sequence in the E, N, and U directions. The constructed least square function includes an anniversary term, a half-anniversary term, and the 1st to 6th intersection annual terms. The 1st to 6th intersection annual terms obtained by fitting are removed to obtain a GNSS station coordinate sequence after eliminating the first type of non-environmental load signal.

[0069]

[0070] Formula (1) is the least square fitting function constructed when eliminating the annual error of the intersection point. is the first six intersection year terms. The first type of non-environmental load signal is eliminated from the station coordinate sequence to obtain the GNSS station coordinate sequence.

[0071] The average amplitudes of the annual term, the semi-anniversary term and the first six intersection term obtained by fitting the third reprocessing sequence of 281 stations are shown in Table 1. In the E and N directions, the average amplitude of the first intersection term can reach about 1 / 3 of the amplitude of the annual term, and about 1 / 4 in the U direction. The average amplitude of the second intersection term is close to that of the semi-anniversary term. In the E direction, the amplitude of the second intersection term is greater than that of the semi-anniversary term.

[0072] Table 1 Average amplitudes of the annual items, semi-annual items and the first six intersection items at 281 stations

[0073]

[0074]

[0075] Step 3: Eliminate the noise signal from the GNSS station coordinate sequence.

[0076] The present invention determines that the second non-environmental load signal that needs to be eliminated is a high-frequency noise signal and a low-frequency noise signal with a period of a set number of years (for example, two years). Singular spectrum analysis decomposes the sequence according to the eigenvalue analysis of the covariance matrix to obtain components of the sequence projection in different eigenvalue directions. Determining the components representing non-environmental load signals is a key step. In this example, when analyzing the components of high-frequency noise, it is directly truncated to the tenth component, and when analyzing other components, the main frequency is used to perform spectral analysis on the eigenvector. Singular spectrum analysis is used to decompose the station coordinate sequence in the three directions of E, N, and U to obtain the first ten main components and the remaining several components. The remaining several components are eliminated to achieve the effect of eliminating high-frequency noise. By analyzing the main frequencies of the first ten main components, the low-frequency signal with a main frequency of two years is eliminated to achieve the purpose of further denoising.

[0077]

[0078] Formula (2) is the result of decomposing the GNSS station coordinate sequence using singular spectrum analysis. E1(t), N1(t), and U1(t) are the station coordinate sequences after eliminating the first type of non-environmental load errors in the E, N, and U directions, respectively.

[0079] Singular spectrum analysis decomposes the station coordinate sequence into N components, in this case N is 730. Taking the coordinate sequence in the E direction as an example, are the first ten components obtained by singular spectrum decomposition. These are the components with main frequencies of two years in the first ten components. These components are filtered out as non-environmental load signal errors. The components other than the first ten components are filtered out as high-frequency noise.

[0080] The 281 station sequences after the annual intersection error has been eliminated are subjected to SSA decomposition and high and low frequency noise is filtered out to obtain new data. The ratios of the annual and half-anniversary amplitudes of the station coordinate sequence before and after filtering out the high and low frequency noise are shown in Table 2. The average values ​​of the ratios of the annual and half-anniversary items are all above 0.85, indicating that the present invention accurately retains the seasonal items of the station coordinate sequence while filtering out the high and low frequency noise.

[0081] Table 2 Ratio of the annual and semi-annual amplitudes of the coordinate series of 281 stations before and after filtering out high and low frequency noise

[0082]

[0083]

[0084] Step 4: Eliminate the thermoelastic deformation from the GNSS station coordinate sequence.

[0085] The present invention determines that the third type of non-environmental load signal to be eliminated is thermoelastic deformation. The annual amplitude of global temperature change can reach up to 30 degrees Celsius, and the resulting bedrock thermoelastic deformation is one of the main components of the non-environmental load signal. The thermoelastic deformation in the horizontal direction cannot be ignored. The uniform sphere thermoelastic deformation model is used to calculate the three-dimensional thermoelastic deformation in the E, N, and U directions, and eliminate it from the GNSS station coordinates.

[0086]

[0087] In formula (3), E2(t), N2(t), and U2(t) are the GNSS station coordinate sequences after eliminating the first and second types of non-environmental load signals, TEBE(t), TEBN(t), and TEBU(t) are the thermoelastic deformations in the E, N, and U directions respectively obtained by the unified spherical thermoelastic deformation model. E3(t), N3(t), and U3(t) are the station coordinate sequences after further eliminating the thermoelastic deformations.

[0088] The thermoelastic deformation of 281 stations was calculated to obtain the thermoelastic deformation. The average and maximum values ​​of the thermoelastic deformation of these stations are shown in Table 3. The thermoelastic deformation was further eliminated in the third reprocessing sequence after eliminating the annual error of the intersection point and high and low frequency noise.

[0089] Table 3 Thermoelastic deformation of 281 stations

[0090]

[0091] Step 5: Use the corrected GNSS data to perform geocentric motion inversion to obtain inversion information.

[0092] That is, when using GNSS data to invert geocentric motion, the GNSS station coordinate sequence after eliminating the above three types of non-environmental load signals is used. When performing geocentric motion inversion, the existing inversion method can be used.

[0093] Combining steps 2 to 4, the calculation formula for the GNSS station coordinate sequence used for geocentric motion inversion can be summarized as follows.

[0094]

[0095] The geocentric motion obtained by inverting the GNSS station coordinate sequence before eliminating the non-environmental load signal is ig3_gcm0, and the geocentric motion obtained by inverting the GNSS station coordinate sequence after eliminating the three types of non-environmental load signals is ig3_gcm3. In order to illustrate the impact of eliminating the three types of non-environmental load signals on the GNSS inverted geocentric motion, we calculated the RMS values ​​of ig3_gcm0 and ig3_gcm3 relative to the environmental load integrated geocentric (AOH_gcm) and the GRACE calculated geocentric (Grace_gcm), respectively, as shown in Table 4.

[0096] Table 4 RMS values ​​of ig3_gcm0 and ig3_gcm3 relative environmental load integrated geocentric and Grace observation geocentric

[0097]

[0098] In Table 4, GX, GY, and GZ represent the geocenter in the X, Y, and Z directions, respectively. It can be seen that after eliminating the three types of non-environmental load signals, the RMS values ​​of the GNSS inverted geocentric motion in the X, Y, and Z directions relative to the two reference geocentric motions are greatly reduced. Figure 2 After eliminating the non-environmental load signal, the GNSS inverted geocentric motion is more consistent with the environmental load geocentric motion, which illustrates the effectiveness of the present invention.

[0099] In summary, the present invention fully considers the influence of different non-environmental load signals, and realizes the modeling and elimination of several non-environmental load signals with greater influence, thereby improving the ability of GNSS to invert the geocentric motion.

[0100] Embodiment 4:

[0101] Embodiment 4 provides a geocentric motion inversion system, including:

[0102] A GNSS correction system, used to execute the steps in the GNSS correction method described in Example 1 to obtain corrected GNSS data;

[0103] The inversion system is used to perform geocentric motion inversion using the corrected GNSS data to obtain inversion information.

[0104] The functions of each system in the geocentric motion inversion system provided in Example 4 correspond to the steps in the geocentric motion inversion method provided in Example 3. Therefore, Example 4 can also be understood by referring to the description of Example 3, which will not be repeated here.

[0105] Finally, it should be noted that the above specific implementation methods are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A GNSS correction method, characterized in that: include: Get GNSS station coordinate sequence data; Eliminating selected non-environmental load signals from GNSS station coordinate sequence data to obtain corrected GNSS data; The selected non-environmental load signals include one or more types of non-environmental load signals selected from annual intersection error, noise signal, and thermoelastic deformation; Constructing a least square fitting function to fit the GNSS station coordinate sequence data, the least square fitting function includes an anniversary term, a semi-anniversary term and an intersection year term; removing the intersection year term in the fitting formula to eliminate the intersection year error; The noise signal includes a high-frequency noise signal and a low-frequency noise signal; the GNSS station coordinate sequence data is decomposed into N components using singular spectrum analysis, the N components are divided into the first s main components and the remaining components, and the remaining components in the decomposition formula are removed to eliminate the high-frequency noise signal; the main frequencies of the first s main components are analyzed, and the low-frequency signals whose main frequencies are set years are eliminated to eliminate the low-frequency noise signals.

2. The GNSS correction method according to claim 1, characterized in that: After acquiring the GNSS station coordinate sequence data and before eliminating the selected non-environmental load signal, the method further includes: detecting and eliminating gross errors in the GNSS station coordinate sequence data, and eliminating one or more of trend items, jumps, and post-seismic deformations.

3. The GNSS correction method according to claim 1, characterized in that: The intersection annual error, the noise signal, and the thermoelastic deformation are respectively used as the first type of non-environmental load signal, the second type of non-environmental load signal, and the third type of non-environmental load signal, and are eliminated in sequence.

4. The GNSS correction method according to claim 1, characterized in that: The least squares fitting function is expressed as follows: Where E0(t), N0(t), and U0(t) are the GNSS station coordinate sequences in the three directions of E, N, and U respectively, and t is the time corresponding to the GNSS station coordinate sequence. They are the annual terms in the three directions of E, N, and U, ω1 is the annual angular velocity, A 11 , A 21 , A 31 is the annual amplitude obtained by fitting, is the annual phase obtained by fitting, is the semiannual term, ω2 is the semiannual term angular velocity, A 12 , A 22 , A 32 is the half-annual amplitude obtained by fitting, is the semi-annual phase obtained by fitting, is the annual term of the first m intersection points, α i is the angular velocity of the ith intersection point, B 1i , B 2i , B 3i is the amplitude of the annual term of the i-th intersection obtained by fitting, β 1i , β 2i , β 3i is the phase of the annual term of the i-th intersection obtained by fitting.

5. The GNSS correction method according to claim 1, characterized in that: The spherical thermoelastic deformation model is used to calculate the thermoelastic deformation in the three directions of E, N and U, and the calculated thermoelastic deformation is eliminated from the GNSS station coordinate sequence data.

6. A GNSS correction system, characterized in that: include: A data acquisition unit, used to acquire GNSS station coordinate sequence data; A data correction unit, used to eliminate selected non-environmental load signals from the GNSS station coordinate sequence data to obtain corrected GNSS data; The GNSS correction system is used to execute the steps in the GNSS correction method according to any one of claims 1 to 5.

7. A method for inverting geocentric motion, characterized in that: The following steps are involved: Obtaining corrected GNSS data using the GNSS correction method described in any one of claims 1 to 5; The corrected GNSS data is used to perform geocentric motion inversion to obtain inversion information.

8. A geocentric motion inversion system, characterized in that: include: A GNSS correction system, configured to execute the steps of the GNSS correction method according to any one of claims 1 to 5 to obtain corrected GNSS data; The inversion system is used to perform geocentric motion inversion using the corrected GNSS data to obtain inversion information.

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