Real-time line galloping monitoring method and system

Abstract

The invention relates to the technical field of line monitoring information, and discloses a real-time line galloping monitoring method and system, and the method comprises the steps: constructing an LSTM model through the VMD decomposition intrinsic mode of the time sequence of the spherical harmonic coefficient of each ionosphere model, and analyzing the influence of a principal component according to the data of each intrinsic mode decomposed by the VMD, thereby obtaining an LSTM model; the ionosphere model spherical harmonic coefficient prediction value is obtained, the ionosphere delay amount is calculated through the ionosphere spherical harmonic coefficient prediction value, the real-time performance of calculation can be improved without waiting for the ionosphere spherical harmonic coefficient, calculation does not need to be conducted through the past ionosphere spherical harmonic coefficient, and therefore the calculation precision is improved. And the change of each intrinsic mode before and after reconstruction is measured through the sum of the Euclidean distances of each intrinsic mode at each time, so that the principal component influence of each intrinsic mode is determined.

Description

Technical Field

[0001] The present invention relates to the field of line monitoring information technology, and in particular to a real-time line dancing monitoring method and system. Background Art

[0002] The effect of the electron content (TEC) of the ionosphere on signal propagation is an important issue in global navigation satellite system (GNSS) applications, especially for positioning services that require high accuracy, such as line sway transmission monitoring. The ionosphere is an electrically charged layer of the Earth's atmosphere, located at an altitude of about 50 to 1000 kilometers. Uneven electron density in the ionosphere can cause changes in the speed and path of radio waves passing through the layer. This phenomenon is called ionospheric delay and is one of the main factors affecting the accuracy of GNSS signals.

[0003] In order to effectively predict and compensate for the impact of the ionosphere, a spherical harmonics model is usually used to describe the distribution of electron content in the global ionosphere. This model can provide an estimate of the ionospheric TEC on a global scale, which can then be used to correct GNSS signals propagating through the ionosphere. However, the traditional spherical harmonics model contains up to 256 coefficients, and it is often not feasible to transmit these coefficients in one go in the existing data transmission format. Especially in application scenarios that require real-time processing, the lack of instant and complete ionospheric data will directly affect positioning accuracy, which is extremely critical in applications with extremely strict requirements for positioning and time synchronization. Therefore, there is an urgent need for a method that can effectively transmit key spherical harmonics coefficients without increasing the transmission burden and ensure a balance between real-time performance and accuracy. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a real-time line dancing monitoring method and system to solve the problem that the coefficients of the spherical harmonic function model cannot be fully transmitted at one time under the current data transmission method, lack of real-time and complete ionospheric data, affecting positioning accuracy, and unable to achieve precise synchronization of positioning and time.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a real-time line galloping monitoring method, comprising:

[0008] Acquire the time series data of the ionospheric model spherical harmonic vector in real time, decompose the data components, and obtain the intrinsic mode components of each component;

[0009] Constructing LSTM models for the eigenmode components of each component respectively, and obtaining predicted values ​​of each eigenmode component of each component;

[0010] Taking values ​​of each eigenmode component through a sliding window to obtain window data, and constructing and reconstructing a corresponding first matrix for each eigenmode component of the window data;

[0011] Filling the rows of the first matrix of each eigenmode after reconstruction with zeros to form rows of a second matrix, reconstructing each eigenmode of the second matrix, and combining the predicted values ​​of the eigenmode components to calculate the predicted value of the spherical harmonic coefficient of each ionospheric model at a preset time;

[0012] Obtain the missing state of the spherical harmonic coefficients of the ionospheric model received at a preset time, and if missing, calculate the ionospheric delay of the CORS station in combination with the predicted value at the preset time;

[0013] According to the approximate coordinates and grid correction numbers of the line galloping monitoring point, the tropospheric delay, the ionospheric delay and the comprehensive error prediction value at the line galloping monitoring point are obtained to generate a virtual observation value of the line galloping monitoring point; wherein the grid correction number includes the ionospheric delay of the CORS station;

[0014] The virtual observation value and the actual observation value are combined into a short baseline double difference observation equation, and the equation is subjected to real-time Kalman filtering to obtain a time series of coordinates of the line sway monitoring points.

[0015] As a preferred solution of the real-time line dancing monitoring method of the present invention, wherein:

[0016] When the length N of each eigenmode component of each component is greater than a first threshold, a sliding window is set, and the length L of the sliding window is less than half of the length N of each eigenmode component.

[0017] As a preferred solution of the real-time line dancing monitoring method of the present invention, wherein:

[0018] Construct and reconstruct the corresponding first matrix for each eigenmode component of the window data, including: As the rows of the matrix form the first matrix F i (k) ;

[0019] The first matrix F i (k) Perform SVD decomposition to obtain:

[0020]

[0021] in, and Respectively represent F i (k) The left and right singular vectors of , Indicates F i (k) The singular value matrix of ;

[0022] The singular value matrix The singular value σ i (k) Sort and filter out the smallest a% of all singular values ​​σ corresponding to the time series of the kth spherical harmonic coefficient i (k) and the corresponding left singular vectors and the right singular vectors

[0023] Using the remaining singular values ​​σ i (k)* and the corresponding left singular vectors and the right singular vector V i (k)T* Reconstruct the first matrix of each eigenmode component of the time series of the kth spherical harmonic coefficient. The first matrix of each component after reconstruction is expressed as:

[0024]

[0025] As a preferred solution of the real-time line dancing monitoring method of the present invention, wherein:

[0026] Combining the predicted values ​​of the eigenmode components to calculate the predicted values ​​of the spherical harmonic coefficients of each ionospheric model at a preset time, including:

[0027] The L1 norm of the difference between the spherical harmonic vectors of each ionospheric model before and after reconstruction is calculated;

[0028] Calculating the weight of each eigenmode component based on the L1 norm;

[0029] The predicted value of each ionospheric model spherical harmonic coefficient at time t+1 is calculated based on the weights and the predicted values ​​of each eigenmode component.

[0030] As a preferred solution of the real-time line dance monitoring method described in the present invention, it also includes: obtaining the missing state of the ionospheric model spherical harmonic coefficients received at a preset time. If it is not missing, the received ionospheric model spherical harmonic coefficients are used to solve the ionospheric delay to obtain the ionospheric delay of the CORS station.

[0031] As a preferred solution of the real-time line galloping monitoring method of the present invention, the ionospheric delay of the CORS station is calculated in combination with the predicted value at the preset time, specifically including: arranging the ionospheric model spherical harmonic coefficients received at time t+1 into a 16*16 square matrix to obtain the missing measurement coefficient matrix

[0032] The ionospheric model spherical harmonic coefficients received before t time without missing values The received time j Arrange in order to form a 16*16 complete spherical harmonic coefficient matrix

[0033] The complete spherical harmonic coefficient matrix Each element of is accumulated one by one to obtain the merged matrix G, and the merged matrix G is decomposed by SVD;

[0034] The missing measurement coefficient matrix Each element of is added one by one to the elements in the merge matrix G to obtain the iteration matrix H. The measurement coefficient matrix is ​​missing during the addition. The missing values ​​in are used as variables;

[0035] Set the initial value of the missing value and update the elements in the matrix H with a fixed step size;

[0036] Perform SVD decomposition on the matrix H in each iteration;

[0037] Subtract each element in the singular value matrix after SVD decomposition of matrix H from the corresponding element in the singular value matrix of the merged matrix G and square them. Stop the iteration when the result is less than the set threshold.

[0038] The size of the missing value when the iteration stops is taken as the missing value;

[0039] The final missing value is obtained by averaging each missing value obtained by iterative calculation with the corresponding predicted value;

[0040] Fill the final missing values ​​into the missing measurement coefficient matrix middle;

[0041] The missing measurement coefficient matrix Each ionospheric model spherical harmonic coefficient is used to solve the ionospheric delay of the CORS station.

[0042] As a preferred solution of the real-time line dancing monitoring method of the present invention, wherein:

[0043] The generating of virtual observation values ​​of line sway monitoring points includes:

[0044] Using the coordinates and satellite ephemeris of the line galloping monitoring point and the CORS station, the observation value of the CORS station is converted to the line galloping monitoring point by calculating the carrier observation value of the line galloping monitoring point and the pseudo-range observation value of the line galloping monitoring point.

[0045] The virtual observation value of the line galloping monitoring point is calculated by combining the relative difference between the predicted tropospheric delay value and the predicted ionospheric delay value at the CORS station and the line galloping monitoring point.

[0046] In a second aspect, the present invention provides a real-time line dancing monitoring system, comprising:

[0047] An acquisition and decomposition module is used to acquire the time series data of the spherical harmonic vector of the ionosphere model in real time, decompose each component of the data, and obtain the intrinsic mode component of each component;

[0048] A first construction module is used to construct LSTM models for the eigenmode components of each component, respectively, to obtain the predicted values ​​of each eigenmode component of each component;

[0049] A second construction module is used to take values ​​of each eigenmode component through a sliding window to obtain window data, and to construct and reconstruct a corresponding first matrix for each eigenmode component of the window data;

[0050] A first calculation module is used to fill the reconstructed first matrix with zeros to form rows of a second matrix, reconstruct each eigenmode of the second matrix, and combine the weights of the eigenmode components to calculate the predicted value of the spherical harmonic coefficient of each ionospheric model at a preset time;

[0051] A calculation module is used to obtain the missing state of the spherical harmonic coefficients of the ionospheric model received at a preset time, and if missing, the ionospheric delay of the CORS station is calculated in combination with the predicted value at the preset time;

[0052] The second calculation module obtains the tropospheric delay, ionospheric delay and comprehensive error prediction value at the line galloping monitoring point according to the approximate coordinates and grid correction number of the line galloping monitoring point to generate a virtual observation value of the line galloping monitoring point; the grid correction number includes the ionospheric delay of the CORS station;

[0053] The third calculation module forms a short baseline double-difference observation equation with the virtual observation value and the actual observation value, performs real-time Kalman filtering on the equation, and obtains a time series of coordinates of the line sway monitoring points.

[0054] In a third aspect, the present invention provides a computing device, comprising:

[0055] Memory and processor;

[0056] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the real-time line dancing monitoring method are implemented.

[0057] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the real-time line dancing monitoring method.

[0058] Compared with the prior art, the present invention has the following beneficial effects: the present invention constructs an LSTM model by VMD decomposing the eigenmodes of the time series of the spherical harmonic coefficients of each ionospheric model, and analyzes the influence of the main component according to the data of each eigenmode decomposed by VMD, obtains the predicted value of the spherical harmonic coefficient of the ionospheric model, and solves the ionospheric delay amount by the predicted value of the ionospheric spherical harmonic coefficient, which can improve the real-time performance of the solution without waiting for the ionospheric spherical harmonic coefficient, and does not need to solve by the past ionospheric spherical harmonic coefficient, thereby improving the solution accuracy; the present invention gives a higher precision to the eigenmode with a greater influence on the main component High weight is given, otherwise a lower weight is given, which reduces the influence of noise on the ionospheric spherical harmonic coefficients, thereby improving the positioning accuracy of line galloping monitoring; the present invention measures the changes of each eigenmode before and after reconstruction by the sum of the Euclidean distances of each eigenmode at each time, thereby determining the influence of the main components of each eigenmode; in the present invention, the transmission and reception of a large amount of data are all completed through the CORS station, and only a small amount of correction numbers need to be transmitted and received at the line galloping monitoring point, which greatly reduces the data transmission and reception volume of a single line galloping monitoring point, which is of great significance for reducing the cost generated by the transmission and reception of line galloping monitoring data. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0060] Figure 1 This is a schematic diagram of the overall process logic of the real-time line dancing monitoring method described in the first embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0062] Example 1

[0063] Reference Figure 1, as an embodiment of the present invention, provides a real-time line dancing monitoring method, comprising:

[0064] S100: Real-time acquisition of time series data of ionospheric model spherical harmonic vectors;

[0065] Specifically, the components of the ionospheric model spherical harmonic vector are spherical harmonic coefficients.

[0066] S200: decomposing each component of the ionospheric model spherical harmonic vector time series data to obtain the intrinsic mode component of each component;

[0067] Specifically, each component is decomposed by VMD to obtain the eigenmode components of each component of the spherical harmonic vector of the ionospheric model: Where k is the number of the spherical harmonic coefficients, and i is the number of the eigenmode component.

[0068] In the embodiment of the present application, when the length N of each eigenmode component of each component is greater than the first threshold, a sliding window is set, and the length L of the sliding window is less than half of the length N of each eigenmode component.

[0069] S300: construct LSTM models for the eigenmode components of each component respectively, and obtain the predicted values ​​of each eigenmode component of each component

[0070] S400: Sliding values ​​of each eigenmode component are obtained through a sliding window to obtain window data, and the corresponding first matrix F is constructed and reconstructed for each eigenmode component of the window data. i (k) ;

[0071] For example, the sliding window can be from the beginning of the eigenmode component Start sliding to get the value, and get the window data each time you slide to get the value The sliding step size is 1;

[0072] In the embodiment of the present application, the first matrix F corresponding to each eigenmode component of the window data is constructed and reconstructed respectively. i (k) ,include:

[0073] All window data corresponding to eigenmode i As the rows of the matrix form the first matrix F i (k) ;

[0074] The first matrix F i (k) Perform SVD decomposition to obtain:

[0075]

[0076] in, and Respectively represent F i (k) The left and right singular vectors of , Indicates F i (k) The singular value matrix of ;

[0077] The singular value matrix The singular value σ i (k) Sort and filter out the smallest a% of all singular values ​​σ corresponding to the time series of the kth spherical harmonic coefficient i (k) and the corresponding left singular vectors and the right singular vectors

[0078] Using the remaining singular values ​​σ i (k)* and the corresponding left singular vectors and the right singular vectors Reconstruct the first matrix of each eigenmode component of the time series of the kth spherical harmonic coefficient. The first matrix of each component after reconstruction is expressed as:

[0079]

[0080] S500: The first matrix of each eigenmode after reconstruction Perform zero padding to form rows of the second matrix, average each column of the second matrix to reconstruct each eigenmode, and combine the weights of each eigenmode component to calculate the predicted value of the spherical harmonic coefficient of each ionospheric model at a preset time;

[0081] Exemplarily, the zero-padding step is: take out each row of the first matrix of each eigenmode after reconstruction from top to bottom, and pad all the taken rows with zeros at the beginning and end. The zero-padding rule is: pad m-1 zeros before the first element of the mth row, and pad NL zeros after the Lth element of the mth row. All the rows after zero-padding are sequentially composed into a second matrix row;

[0082] Second Matrix Each column of is averaged to reconstruct each eigenmode and is expressed as:

[0083]

[0084] In the embodiment of the present application, the weights of the eigenmode components are combined to calculate the predicted value of the spherical harmonic coefficient of each ionospheric model at a preset time, including:

[0085] The L1 norm of the difference between the spherical harmonic vectors of each ionospheric model before and after reconstruction is calculated;

[0086] Specifically, the L1 norm It is expressed as:

[0087]

[0088] Where t is the time point;

[0089] The weight of each eigenmode component is calculated based on the L1 norm;

[0090] Specifically, the weight of each eigenmode component is calculated as follows:

[0091]

[0092] The predicted value of the spherical harmonic coefficient of each ionospheric model at time t+1 is calculated based on the weights and the predicted values ​​of each intrinsic mode component;

[0093] Specifically, the predicted value y at time t+1 is (k) The calculation method of (t+1) is:

[0094]

[0095] S600: Obtain the missing state of the spherical harmonic coefficients of the ionospheric model received at a preset time, and if missing, calculate the ionospheric delay of the CORS station in combination with the predicted value at the preset time;

[0096] It should be noted that the preset time is the above-mentioned time t+1.

[0097] In the embodiment of the present application, the ionospheric delay of the CORS station is calculated in combination with the predicted value at the preset time, specifically including: arranging the ionospheric model spherical harmonic coefficients received at time t+1 into a 16*16 square matrix to obtain the missing measurement coefficient matrix

[0098] The ionospheric model spherical harmonic coefficients received before t time without missing values The received time j Arrange in order to form a 16*16 complete spherical harmonic coefficient matrix

[0099] The complete spherical harmonic coefficient matrix Each element of is accumulated one by one to obtain the merged matrix G, and the merged matrix G is decomposed by SVD;

[0100] Specifically, the merge matrix G is expressed as:

[0101]

[0102] Perform SVD decomposition on the merged matrix G, expressed as:

[0103]

[0104] in, and denote the left and right singular vectors of G respectively, represents the singular value matrix of G;

[0105] The missing measurement coefficient matrix Each element of is added one by one to the elements in the merge matrix G to obtain the iteration matrix H. The measurement coefficient matrix is ​​missing during the addition. The missing values ​​in are used as variables;

[0106] Set the initial value of the missing value and update the elements in the matrix H with a fixed step size;

[0107] Perform SVD decomposition on the matrix H in each iteration;

[0108] Subtract each element in the singular value matrix after SVD decomposition of matrix H from the corresponding element in the singular value matrix of the merged matrix G and square them. Stop the iteration when the result is less than the set threshold.

[0109] Specifically, the calculation process and the results are expressed as follows:

[0110]

[0111] The iteration stops when Ω is less than the set threshold.

[0112] The size of the missing value when the iteration stops is taken as the missing value;

[0113] The final missing value is obtained by averaging each missing value obtained by iterative calculation with the corresponding predicted value;

[0114] Fill the final missing values ​​into the missing measurement coefficient matrix middle;

[0115] The missing measurement coefficient matrix Each ionospheric model spherical harmonic coefficient is used to solve the ionospheric delay of the CORS station.

[0116] In an embodiment of the present application, it also includes obtaining the missing state of the ionospheric model spherical harmonic coefficients received at a preset time. If not missing, the received ionospheric model spherical harmonic coefficients are used to solve the ionospheric delay to obtain the ionospheric delay of the CORS station.

[0117] It should be noted that all the above steps S100-S600 are performed through the CORS station, which can greatly reduce the amount of data sent and received at a single line sway monitoring point and reduce costs.

[0118] S700: according to the approximate coordinates and grid correction numbers of the line galloping monitoring point, the tropospheric delay, the ionospheric delay and the comprehensive error prediction value at the line galloping monitoring point are obtained to generate a virtual observation value of the line galloping monitoring point; wherein the grid correction number includes the ionospheric delay of the CORS station;

[0119] Specifically, the method for obtaining the predicted value of the tropospheric delay at the line galloping monitoring point is:

[0120] The tropospheric related errors are divided into dry delay part and wet delay part. The dry delay part is corrected by GPT2w or ITG model. The wet delay part uses the Kriging interpolation algorithm based on height correction according to the tropospheric wet delay results solved by each CORS station to calculate the tropospheric correction number of the line galloping monitoring point. The tropospheric delay is calculated according to the tropospheric correction number at the line galloping monitoring point.

[0121] The Kriging interpolation algorithm based on height correction specifically includes:

[0122] The elevation of each CORS station is averaged to obtain the elevation datum for tropospheric wet delay correction in the interpolation area;

[0123] The tropospheric wet delay correction number from each CORS station to the tropospheric error correction reference plane is calculated based on the tropospheric wet delay height difference correction model;

[0124] The spatial structure and variability analysis of the tropospheric wet delay at each CORS station is conducted on the elevation datum plane;

[0125] Based on the approximate coordinates of the line galloping monitoring point and the results of the spatial structure analysis and variability analysis of the tropospheric wet delay, the CORS station uses the Kriging algorithm to interpolate the tropospheric wet delay correction value of the tropospheric wet delay correction reference plane at that location;

[0126] According to the approximate coordinate elevation of the line galloping monitoring point, calculate the height difference between the line galloping monitoring point and the tropospheric error correction reference surface;

[0127] The predicted value of tropospheric wet delay at the approximate coordinate elevation of the line galloping monitoring point is calculated based on the tropospheric wet delay height difference correction model.

[0128] Specifically, the method for obtaining the predicted value of the ionospheric delay at the line galloping monitoring point is:

[0129] Perform spatial structure analysis and spatial variability analysis at the central ionosphere altitude.

[0130] Use the preset altitude as the reference plane for ionospheric error correction;

[0131] Perform non-differential ionospheric delay error interpolation calculation to the line galloping monitoring point on the ionospheric error correction reference plane to obtain the ionospheric correction number at the line galloping monitoring point, and calculate the ionospheric delay amount based on the ionospheric correction number at the line galloping monitoring point;

[0132] The non-differenced ionospheric delay error interpolation calculation includes:

[0133] Under the conditions of linear unbiased and optimal estimation, the Kriging equations for interpolation of the total electron content in the ionosphere are obtained. The Kriging equations are:

[0134]

[0135] Where μ represents the Lagrange multiplier factor, d ij Indicates the distance between the CORS site and the line galloping monitoring point, d i0 represents the distance between the interpolation point and the observed value, λ j represents the weighting coefficient;

[0136] Solve the Kriging equations to obtain weighted coefficients;

[0137] The estimated value of the total electron content in the ionosphere is calculated based on the weighting coefficients;

[0138] The predicted value of ionospheric delay at the line galloping monitoring point is calculated by estimating the total electron content of the ionosphere.

[0139] Specifically, the method for obtaining the comprehensive error prediction value at the line galloping monitoring point may be to interpolate the comprehensive error prediction value through the inverse distance weighting method.

[0140] In the embodiment of the present application, generating a virtual observation value of a line sway monitoring point includes:

[0141] Using the coordinates and satellite ephemeris of the line galloping monitoring point and the CORS station, the observation value of the CORS station is converted to the line galloping monitoring point by calculating the carrier observation value of the line galloping monitoring point and the pseudo-range observation value of the line galloping monitoring point.

[0142] Specifically, the method of converting the observation value of the CORS station to the line galloping monitoring point is:

[0143] Φ′ V =Φ A -ρ A +ρ V

[0144] P′ V =P A -ρ A +ρ V

[0145] Among them, Φ′ V represents the carrier observation value of the line galloping monitoring point, P′ V represents the pseudo-range observation value of the line galloping monitoring point, Φ A represents the carrier observation value of the CORS site, P A represents the pseudorange observation value of the CORS station, ρ A represents the geometric distance from the CORS site to the satellite, ρ V Indicates the geometric distance from the line dancing monitoring point to the satellite.

[0146] Combined with the relative difference between the predicted tropospheric delay and the predicted ionospheric delay at the CORS station and the line galloping monitoring point, the virtual observation value of the line galloping monitoring point is calculated;

[0147] Specifically, the difference between the tropospheric delay errors of the CORS stations after double difference calculation is The calculation method is:

[0148]

[0149] Calculate the difference after double difference of ionospheric delay error at CORS station The calculation method is as follows:

[0150]

[0151] Where f1 and f2 are the frequencies of L1 and L2 observations, respectively. and are L1 and L2 double difference carrier observation values, and is the L1 and L2 double difference integer ambiguity, ρ represents the geometric distance between the satellite and the receiver;

[0152] Specifically, the difference of the tropospheric delay error at the line galloping monitoring point after double difference is calculated by spatial interpolation algorithm The difference between the double difference of the ionospheric delay error and

[0153] Calculate the observation value of the virtual reference station, that is, the virtual observation value of the line galloping monitoring point;

[0154] Specifically, the observation values ​​of the virtual reference station of the virtual reference station include the virtual reference station carrier observation value and the virtual reference station pseudorange observation value;

[0155] Carrier observation value Φ of the virtual reference station V The calculation method is as follows:

[0156]

[0157] in, Indicates the double difference error correction of the carrier observation value of the virtual reference station, Including the double difference error correction of the carrier observation values ​​corresponding to frequency point 1 and frequency point 2 respectively and The calculation method is as follows:

[0158]

[0159] Pseudorange observation value P of the virtual reference station V The calculation method is as follows:

[0160]

[0161] in, Indicates the pseudo-range double difference error correction of the virtual reference station. Including the pseudo-range double difference error corrections of the line galloping monitoring points corresponding to frequency point 1 and frequency point 2 respectively and The calculation method is as follows:

[0162]

[0163] S800: The virtual observation values ​​and the actual observation values ​​are combined into a short baseline double-difference observation equation, and the equation is subjected to real-time Kalman filtering to obtain a time series of coordinates of the line sway monitoring points.

[0164] The above is a schematic scheme of a real-time line dancing monitoring method of this embodiment. It should be noted that the technical scheme of the real-time line dancing monitoring system and the technical scheme of the real-time line dancing monitoring method described above belong to the same concept. For details not described in detail in the technical scheme of the real-time line dancing monitoring system in this embodiment, please refer to the description of the technical scheme of the real-time line dancing monitoring method described above.

[0165] The real-time line dancing monitoring system in this embodiment includes:

[0166] An acquisition and decomposition module is used to acquire the time series data of the spherical harmonic vector of the ionosphere model in real time, decompose each component of the data, and obtain the intrinsic mode component of each component;

[0167] A first construction module is used to construct LSTM models for the eigenmode components of each component, respectively, to obtain the predicted values ​​of each eigenmode component of each component;

[0168] A second construction module is used to take values ​​of each eigenmode component through a sliding window to obtain window data, and to construct and reconstruct a corresponding first matrix for each eigenmode component of the window data;

[0169] A first calculation module is used to fill the reconstructed first matrix with zeros to form rows of a second matrix, reconstruct each eigenmode of the second matrix, and combine the weights of the eigenmode components to calculate the predicted value of the spherical harmonic coefficient of each ionospheric model at a preset time;

[0170] A calculation module is used to obtain the missing state of the spherical harmonic coefficients of the ionospheric model received at a preset time, and if missing, the ionospheric delay of the CORS station is calculated in combination with the predicted value at the preset time;

[0171] The second calculation module obtains the tropospheric delay, ionospheric delay and comprehensive error prediction value at the line galloping monitoring point according to the approximate coordinates and grid correction number of the line galloping monitoring point to generate a virtual observation value of the line galloping monitoring point; the grid correction number includes the ionospheric delay of the CORS station;

[0172] The third calculation module forms a short baseline double-difference observation equation with the virtual observation value and the actual observation value, performs real-time Kalman filtering on the equation, and obtains a time series of coordinates of the line sway monitoring points.

[0173] This embodiment further provides a computing device suitable for real-time line galloping monitoring, including:

[0174] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the real-time line dance monitoring method proposed in the above embodiment.

[0175] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the real-time line dancing monitoring method proposed in the above embodiment is implemented.

[0176] The storage medium proposed in this embodiment and the method for implementing real-time line dancing monitoring proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0177] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (Random Access Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0178] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A real-time line galloping monitoring method, characterized in that: include: Acquire the time series data of the ionospheric model spherical harmonic vector in real time, decompose the data components, and obtain the intrinsic mode components of each component; Constructing LSTM models for the eigenmode components of each component respectively, and obtaining predicted values ​​of each eigenmode component of each component; Taking values ​​of each eigenmode component through a sliding window to obtain window data, and constructing and reconstructing a corresponding first matrix for each eigenmode component of the window data; Filling the rows of the first matrix of each eigenmode after reconstruction with zeros to form rows of a second matrix, reconstructing each eigenmode of the second matrix, and combining the predicted values ​​of the eigenmode components to calculate the predicted value of the spherical harmonic coefficient of each ionospheric model at a preset time; Obtain the missing state of the spherical harmonic coefficients of the ionospheric model received at a preset time, and if missing, calculate the ionospheric delay of the CORS station in combination with the predicted value at the preset time; According to the approximate coordinates and grid correction numbers of the line galloping monitoring point, the tropospheric delay, the ionospheric delay and the comprehensive error prediction value at the line galloping monitoring point are obtained to generate a virtual observation value of the line galloping monitoring point; wherein the grid correction number includes the ionospheric delay of the CORS station; The virtual observation value and the actual observation value are combined into a short baseline double difference observation equation, and the equation is subjected to real-time Kalman filtering to obtain a time series of coordinates of the line sway monitoring points.

2. The real-time line dancing monitoring method according to claim 1, characterized in that: When the length N of each eigenmode component of each component is greater than a first threshold, a sliding window is set, and the length L of the sliding window is less than half of the length N of each eigenmode component.

3. The real-time line dancing monitoring method according to claim 2, characterized in that: Construct and reconstruct the corresponding first matrix for each eigenmode component of the window data, including: As the rows of the matrix form the first matrix The first matrix Perform SVD decomposition to obtain: in, and Respectively The left and right singular vectors of , express The singular value matrix of ; The singular value matrix The singular value σ i (k) Sort and filter out the smallest a% of all singular values ​​σ corresponding to the time series of the kth spherical harmonic coefficient i (k) and the corresponding left singular vectors and the right singular vectors Using the remaining singular values and the corresponding left singular vectors and the right singular vectors Reconstruct the first matrix of each eigenmode component of the time series of the kth spherical harmonic coefficient. The first matrix of each component after reconstruction is expressed as:

4. The real-time line dancing monitoring method according to claim 3, characterized in that: Combining the predicted values ​​of the eigenmode components to calculate the predicted values ​​of the spherical harmonic coefficients of each ionospheric model at a preset time, including: The L1 norm of the difference between the spherical harmonic vectors of each ionospheric model before and after reconstruction is calculated; Calculating the weight of each eigenmode component based on the L1 norm; The predicted value of each ionospheric model spherical harmonic coefficient at time t+1 is calculated based on the weights and the predicted values ​​of each eigenmode component.

5. The real-time line dancing monitoring method according to claim 4, characterized in that: The method also includes obtaining the missing state of the ionospheric model spherical harmonic coefficients received at a preset time. If not missing, the received ionospheric model spherical harmonic coefficients are used to solve the ionospheric delay to obtain the ionospheric delay of the CORS station.

6. The real-time line dancing monitoring method according to claim 4, characterized in that: Combined with the predicted value at the preset time, the ionospheric delay of the CORS station is calculated, specifically including: arranging the ionospheric model spherical harmonic coefficients received at time t+1 into a 16*16 square matrix to obtain the missing measurement coefficient matrix The ionospheric model spherical harmonic coefficients received before t time without missing values The received time j Arrange in order to form a 16*16 complete spherical harmonic coefficient matrix The complete spherical harmonic coefficient matrix Each element of is accumulated one by one to obtain the merged matrix G, and the merged matrix G is decomposed by SVD; The missing measurement coefficient matrix Each element of is added one by one to the elements in the merge matrix G to obtain the iteration matrix H. The measurement coefficient matrix is ​​missing during the addition. The missing values ​​in are used as variables; Set the initial value of the missing value and update the elements in the matrix H with a fixed step size; Perform SVD decomposition on the matrix H in each iteration; Subtract each element in the singular value matrix after SVD decomposition of matrix H from the corresponding element in the singular value matrix of the merged matrix G and square them. Stop the iteration when the result is less than the set threshold. The size of the missing value when the iteration stops is taken as the missing value; The final missing value is obtained by averaging each missing value obtained by iterative calculation with the corresponding predicted value; Fill the final missing values ​​into the missing measurement coefficient matrix middle; The missing measurement coefficient matrix Each ionospheric model spherical harmonic coefficient is used to solve the ionospheric delay of the CORS station.

7. The real-time line dancing monitoring method according to claim 5 or 6, characterized in that: The generating of virtual observation values ​​of line sway monitoring points includes: Using the coordinates and satellite ephemeris of the line galloping monitoring point and the CORS station, the observation value of the CORS station is converted to the line galloping monitoring point by calculating the carrier observation value of the line galloping monitoring point and the pseudo-range observation value of the line galloping monitoring point. The virtual observation value of the line galloping monitoring point is calculated by combining the relative difference between the predicted tropospheric delay value and the predicted ionospheric delay value at the CORS station and the line galloping monitoring point.

8. A system using the real-time line galloping monitoring method as claimed in claim 1, characterized in that: include: An acquisition and decomposition module is used to acquire the time series data of the spherical harmonic vector of the ionosphere model in real time, decompose each component of the data, and obtain the intrinsic mode component of each component; A first construction module is used to construct LSTM models for the eigenmode components of each component, respectively, to obtain the predicted values ​​of each eigenmode component of each component; A second construction module is used to take values ​​of each eigenmode component through a sliding window to obtain window data, and to construct and reconstruct a corresponding first matrix for each eigenmode component of the window data; A first calculation module is used to fill the reconstructed first matrix with zeros to form rows of a second matrix, reconstruct each eigenmode of the second matrix, and combine the weights of the eigenmode components to calculate the predicted value of the spherical harmonic coefficient of each ionospheric model at a preset time; A calculation module is used to obtain the missing state of the spherical harmonic coefficients of the ionospheric model received at a preset time, and if missing, the ionospheric delay of the CORS station is calculated in combination with the predicted value at the preset time; The second calculation module obtains the tropospheric delay, ionospheric delay and comprehensive error prediction value at the line galloping monitoring point according to the approximate coordinates and grid correction number of the line galloping monitoring point to generate a virtual observation value of the line galloping monitoring point; The grid correction number includes the ionospheric delay of the CORS station; The third calculation module forms a short baseline double-difference observation equation with the virtual observation value and the actual observation value, performs real-time Kalman filtering on the equation, and obtains a time series of coordinates of the line sway monitoring points.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the real-time line dancing monitoring method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, implement the steps of the real-time line galloping monitoring method according to any one of claims 1 to 7.

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