Line galloping three-dimensional state monitoring method for signal unstable region

Abstract

The invention discloses a line galloping three-dimensional state monitoring method for a signal unstable region, which relates to the technical field of information, and comprises the following steps: through real-time data acquisition and VMD decomposition, constructing an IMF component predicted value of an LSTM model, and forming a first matrix for ultra-long component sliding values; and removing invalid singular values through SVD decomposition, reconstructing and supplementing 0 to obtain a second matrix, averagely reconstructing the IMF, and calculating a grid correction predicted value. Generating a virtual observation value by combining the general coordinates of the line galloping monitoring point and the predicted value of the grid correction, constructing a short baseline double-difference equation, carrying out real-time Kalman filtering, and obtaining a coordinate time sequence of the line galloping monitoring point; according to the method, VMD decomposition is combined with an LSTM model, principal components of IMF data are subjected to weighted analysis, a grid correction predicted value is obtained, and the line galloping monitoring precision is improved; a large amount of data is processed through the CORS site, and the monitoring point only needs a small amount of correction for communication, so that the data transmission burden and cost are effectively reduced.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a line galloping three-dimensional state monitoring method for areas with unstable signals. Background Art

[0002] With the continuous improvement of living standards, people's demand for electricity is also increasing, and it is crucial to ensure that transmission lines continue to supply power to people normally. In the field of power transmission, line dance monitoring plays a vital role in ensuring the stability and safety of transmission lines. Line dance is the vibration or swing of transmission lines, which may be caused by wind, rain, snow or other environmental factors. This dance may cause the lines to contact each other or collide with other structures, thereby causing power outages or power system failures.

[0003] Traditional line movement monitoring methods mostly rely on ground observation equipment or simple satellite-based positioning technology. Although these methods can provide certain monitoring capabilities, they have problems such as low accuracy, slow data update and poor adaptability to environmental factors. Especially in severe weather conditions, traditional methods are often unable to accurately capture the three-dimensional dynamic information of the line, thus affecting the accuracy and real-time nature of monitoring.

[0004] In the existing technology, line dance monitoring can be carried out by using network RTK, but the high precision of network RTK depends on the timely broadcast of grid correction numbers. If the broadcast is not timely enough, the positioning accuracy will be reduced over time due to the untimely update of grid correction numbers. This is a huge challenge for line dance monitoring usually located in areas with unstable signals. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problems solved by the present invention are: the traditional line dancing monitoring method has low accuracy, slow data update, poor adaptability to environmental factors, and how to timely broadcast grid correction numbers to improve the accuracy of line dancing monitoring in areas with unstable signals.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a three-dimensional state monitoring method for line galloping in areas with unstable signals, comprising real-time collection of grid correction number time series data, calculation of the predicted value of the intrinsic mode component of the grid correction number based on VMD decomposition, setting a sliding window to create a first matrix; performing SVD decomposition on the first matrix, reconstructing the first matrix, forming a second matrix after filling the matrix with 0, taking the average to reconstruct the intrinsic mode, and calculating the predicted value of the grid correction number; generating virtual observation values ​​based on the approximate coordinates of the line galloping monitoring point and the predicted value of the grid correction number, forming a short baseline double difference equation with the virtual observation value and the actual observation value, and obtaining the coordinate time series of the line galloping monitoring point through real-time Kalman filtering.

[0008] As a preferred solution of the line galloping three-dimensional state monitoring method for signal unstable areas described in the present invention, wherein: the setting of the sliding window to create the first matrix includes real-time acquisition of grid correction number time series data released by the line CORS station, and the data is decomposed by VMD to obtain the intrinsic mode component of the grid correction number, {X(0) i ,X(1) i ,...,X(N) i}, the intrinsic mode component constructs the LSTM model and obtains the predicted value pd of the intrinsic mode component i ;

[0009] When the length N of the eigenmode component is greater than the first set threshold, the sliding window L is set, and the length of the sliding window L is less than half of N, then the sliding window starts from the beginning of the eigenmode component X(0) i Slide to get the window data {X(t) i ,X(t+1) i ,...,X(t+L) i}, the sliding step is 1;

[0010] The intrinsic mode components of the windowed data {X(t) i ,X(t+1) i ,...,X(t+L) i}Create the corresponding first matrix F i , the first matrix F i The creation method is to convert the window data {X(t) corresponding to the eigenmode i i ,X(t+1) i ,...,X(t+L) i} as the rows of the matrix to form the first matrix F i .

[0011] As a preferred solution of the line galloping three-dimensional state monitoring method for signal unstable areas described in the present invention, wherein: the matrix is ​​supplemented with 0 to form a second matrix, including the first matrix F i Perform SVD decomposition, expressed as:

[0012]

[0013] Among them, U i and are the left and right singular vectors of the i-th component, Σ i represents the singular value matrix of the i-th component;

[0014] Σ i The singular value ρ i Sort and remove the smallest a% of all singular values ​​ρ i and the corresponding left singular vector U i and the right singular vectors

[0015] Using the remaining singular values ​​ρ i * and the corresponding left singular vector U i * and the right singular vectors The first matrix of the reconstructed component, the first matrix of the reconstructed component, is expressed as:

[0016]

[0017] Among them, F i * is the first matrix of the reconstructed components, U i * The remaining singular value ρ i * The corresponding left singular vectors are, The remaining singular value ρ i * The corresponding right singular vectors;

[0018] Take out each row of the first matrix of the reconstructed eigenmode from top to bottom, and add 0 to the beginning and end of all the taken rows. The rule of adding 0 is to add m-1 0s before the first element of the mth row, and to add NL 0s after the Lth element of the mth row. All the rows after adding 0 are sequentially composed into the second matrix H i * OK.

[0019] As a preferred solution of the line galloping three-dimensional state monitoring method for signal unstable areas described in the present invention, wherein: the calculation of the grid correction number prediction value includes converting the second matrix H i* Each column of is averaged to reconstruct the eigenmode

[0020] Calculate the Euclidean distance between the eigenmodes before and after reconstruction, expressed as:

[0021]

[0022] Among them, R i is the Euclidean distance of the i-th eigenmode component, is the i-th reconstructed eigenmode component, X(t) i is the eigenmode component before the i-th reconstruction, t is the time point, t≤N;

[0023] Calculate the weight of the eigenmode component, expressed as:

[0024]

[0025] Among them, ρ i is the weight of the i-th eigenmode component;

[0026] Calculate the predicted value of the grid correction, expressed as:

[0027]

[0028] Among them, y is the predicted value of the grid correction number, and M is the number of eigenmodes after VMD decomposition.

[0029] As a preferred solution of the line galloping three-dimensional state monitoring method for signal unstable areas described in the present invention, wherein: the approximate coordinates of the line galloping monitoring point and the predicted grid correction number include interpolating the tropospheric delay, ionospheric delay and comprehensive error prediction value at the line galloping monitoring point according to the approximate coordinates of the line galloping monitoring point and the predicted grid correction number y;

[0030] Calculate the predicted value of ionospheric delay at the line galloping monitoring point, perform spatial structural analysis and spatial variability analysis at the central ionosphere altitude, use the preset altitude as the ionosphere error correction reference plane, perform non-difference ionospheric delay error interpolation calculation at the line galloping monitoring point on the ionosphere error correction reference plane, and obtain the ionospheric correction number at the line galloping monitoring point. The ionospheric delay is calculated based on the ionospheric correction number at the line galloping monitoring point. The non-difference ionospheric delay error interpolation calculation includes the Kriging equation group of ionospheric total electron content interpolation under linear unbiased and optimal estimation conditions, which is expressed as:

[0031]

[0032] Where μ is the Lagrange multiplier factor, dij is the distance between the CORS site and the line galloping monitoring point, d i0 is the distance between the interpolation point and the observed value, λ j is the weighting coefficient;

[0033] The Kriging equations are solved to obtain weighting coefficients, and the estimated value of the total electron content of the ionosphere is calculated based on the weighting coefficients. The predicted value of the ionospheric delay at the line galloping monitoring point is calculated by the estimated value of the total electron content of the ionosphere.

[0034] Calculate the predicted value of tropospheric delay at the line galloping monitoring point, divide the tropospheric related errors into dry delay part and wet delay part, the dry delay part is corrected by GPT2w or ITG model, and the wet delay part is calculated according to the tropospheric wet delay results solved by each CORS station using the Kriging interpolation algorithm based on height correction, calculate the tropospheric correction number of the line galloping monitoring point, and calculate the tropospheric delay according to the tropospheric correction number at the line galloping monitoring point. The Kriging interpolation algorithm based on height correction specifically includes taking the average of the elevations of each CORS station to obtain the elevation reference plane of the tropospheric wet delay correction in the interpolation area, and calculating according to the tropospheric wet delay height difference correction model. The tropospheric wet delay correction number of each CORS station to the tropospheric error correction datum is obtained, and the spatial structure analysis and variability analysis of the tropospheric wet delay of each CORS station are performed on the elevation datum. According to the approximate coordinates of the line galloping monitoring point, the CORS station uses the Kriging algorithm to interpolate the tropospheric wet delay correction number of the tropospheric wet delay correction datum according to the spatial structure analysis and variability analysis results of the tropospheric wet delay. According to the approximate coordinate elevation of the line galloping monitoring point, the height difference between the line galloping monitoring point and the tropospheric error correction datum is calculated. According to the tropospheric wet delay height difference correction model, the predicted value of the tropospheric wet delay at the approximate coordinate elevation of the line galloping monitoring point is calculated.

[0035] The comprehensive error prediction value at the line galloping monitoring point is calculated and interpolated by the inverse distance weighted method to obtain the comprehensive error prediction value.

[0036] As a preferred solution of the line galloping three-dimensional state monitoring method for signal unstable areas described in the present invention, wherein: the virtual observation value includes generating a line galloping monitoring point virtual observation value according to the tropospheric delay, ionospheric delay and comprehensive error prediction value of the line galloping monitoring point;

[0037] The calculation method of the virtual observation value of the line galloping monitoring point is to use the coordinates and satellite ephemeris of the line galloping monitoring point and the CORS station, convert the observation value of the CORS station to the line galloping monitoring point, and then take into account the relative difference between the predicted value of the tropospheric delay and the predicted value of the ionospheric delay to obtain the observation value of the line galloping monitoring point;

[0038] The observation value of the CORS station is converted to the line galloping monitoring point, which is expressed as:

[0039] Φ' V =Φ A -ρ A +ρ V

[0040] P' V =P A -ρ A +ρ V

[0041] Among them, Φ' V is the carrier observation value of the line galloping monitoring point, P' V is the pseudo-range observation value of the line galloping monitoring point, Φ A is the carrier observation value of the CORS site, P A is the pseudorange observation value of the CORS station, ρ A is the geometric distance from the CORS site to the satellite, ρ V The geometric distance from the line dancing monitoring point to the satellite;

[0042] Calculate the difference between the tropospheric delay errors of the CORS site and the double difference It is expressed as:

[0043]

[0044] Calculate the difference after double difference of ionospheric delay error at CORS station It is expressed as:

[0045]

[0046] 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 is the geometric distance between the satellite and the receiver;

[0047] 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 Calculate the observation values ​​of the line galloping monitoring points.

[0048] As a preferred solution of the line galloping three-dimensional state monitoring method for signal unstable areas described in the present invention, wherein: the short baseline double difference equation is formed, including the line galloping monitoring point using the line galloping monitoring point virtual observation value and the line galloping monitoring point actual observation value to form a short baseline double difference observation equation, the line galloping monitoring point observation value includes the line galloping monitoring point carrier observation value and the line galloping monitoring point pseudorange observation value;

[0049] Calculate the carrier observation value Φ of the line galloping monitoring point V , expressed as:

[0050]

[0051] in, is the double difference error correction number of the carrier observation value at the line galloping monitoring point, Including the double difference error correction of the carrier observation values ​​corresponding to frequency point 1 and frequency point 2 respectively and It is expressed as:

[0052]

[0053] Calculate the pseudo-range observation value P of the line sway monitoring point V , expressed as:

[0054]

[0055] in, It represents the double difference error correction of pseudo-range of line galloping monitoring point, 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 It is expressed as:

[0056]

[0057] in, and They are the positive numbers of pseudo-range double difference errors of the line galloping monitoring points at frequency points 1 and 2, is the difference between the ionospheric delay errors and the double difference.

[0058] Another object of the present invention is to provide a three-dimensional line galloping status monitoring system for areas with unstable signals, which can analyze the influence of the principal components of each intrinsic mode data and give corresponding weights, thereby solving the problem of low line galloping monitoring accuracy caused by untimely updates due to unstable signals in the prior art.

[0059] As a preferred solution of the line galloping three-dimensional state monitoring system for signal unstable areas described in the present invention, it includes: a VMD decomposition module, a prediction value calculation module, and a double difference observation module; the VMD decomposition module is used to collect grid correction number time series data in real time, calculate the prediction value of the intrinsic mode component of the grid correction number based on VMD decomposition, and set a sliding window to create a first matrix; the prediction value calculation module is used to perform SVD decomposition on the first matrix, reconstruct the first matrix, form a second matrix after filling the matrix with 0, take the average to reconstruct the intrinsic mode, and calculate the grid correction number prediction value; the double difference observation module is used to generate virtual observation values ​​based on the approximate coordinates of the line galloping monitoring point and the grid correction number prediction value, and the virtual observation values ​​and the actual observation values ​​form a short baseline double difference equation, and through real-time Kalman filtering, obtain the line galloping monitoring point coordinate time series.

[0060] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a method for three-dimensional state monitoring of line galloping in areas with unstable signals.

[0061] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a three-dimensional state monitoring method for line galloping in signal unstable areas.

[0062] Beneficial effects of the present invention: The three-dimensional state monitoring method for line galloping in areas with unstable signals provided by the present invention uses VMD decomposition and constructs an LSTM model to analyze the influence of the main components of each intrinsic mode data and give corresponding weights, and the weighted sum is used to obtain the predicted value of the grid correction number, thereby effectively solving the problem of decreased line galloping monitoring accuracy caused by insufficient signal stability when the prior art network RTK is solving; in the process of data transmission and reception, a large amount of data is completed through the CORS station, and the line galloping monitoring point only needs to send and receive a small amount of correction numbers to monitor the line galloping situation, which not only greatly reduces the data transmission and reception pressure of a single line galloping monitoring point, but also greatly reduces the cost incurred by the monitoring point due to data transmission and reception. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] 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.

[0064] Figure 1 The first embodiment of the present invention provides a flow chart of a method for monitoring the three-dimensional state of line galloping in areas with unstable signals.

[0065] Figure 2 The third embodiment of the present invention provides an overall flow chart of a line galloping three-dimensional state monitoring system for areas with unstable signals. DETAILED DESCRIPTION

[0066] 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.

[0067] Example 1, reference Figure 1 , as an embodiment of the present invention, provides a three-dimensional state monitoring method for line galloping in signal unstable areas, comprising:

[0068] S1: Collect the time series data of grid correction numbers in real time, calculate the predicted value of the intrinsic mode component of the grid correction numbers based on VMD decomposition, and set the sliding window to create the first matrix.

[0069] Furthermore, a sliding window is set to create the first matrix including the time series data of grid corrections released by the real-time acquisition line CORS station. The data is decomposed by VMD to obtain the intrinsic mode components of the grid corrections, {X(0) i ,X(1) i ,...,X(N) i}, the intrinsic mode component constructs the LSTM model and obtains the predicted value pd of the intrinsic mode component i ; When the length N of the eigenmode component is greater than the first set threshold, set the sliding window L, and the length of the sliding window L is less than half of N, then the sliding window starts from the beginning of the eigenmode component X(0) i Slide to get the window data {X(t) i ,X(t+1) i ,...,X(t+L) i}, the sliding step is 1; for the intrinsic mode component of the window data {X(t) i ,X(t+1) i ,...,X(t+L) i}Create the corresponding first matrix F i , the first matrix F i The creation method is to convert the window data X(t) corresponding to the eigenmode i i ,X(t+1) i ,...,X(t+L) i} as the rows of the matrix to form the first matrix F i .

[0070] It should be said that VMD decomposition can decompose complex grid correction number time series data into multiple intrinsic mode components. Each intrinsic mode component contains components of different frequencies and amplitudes in the original signal. This decomposition can effectively separate the noise and interference components in the signal from the useful signal, thereby improving the accuracy of subsequent processing and prediction; the LSTM model can learn the long-term dependencies in the grid correction number time series data and perform time series prediction. By performing LSTM model prediction on the intrinsic mode components, the predicted value of the grid correction number at future times can be obtained, thereby realizing real-time monitoring of line dancing.

[0071] S2: Perform SVD decomposition on the first matrix, reconstruct the first matrix, fill the matrix with 0 to form the second matrix, take the average reconstructed eigenmode, and calculate the predicted value of the grid correction number.

[0072] Furthermore, the second matrix is ​​constructed by filling the matrix with zeros, including the first matrix F i Perform SVD decomposition, expressed as:

[0073]

[0074] Among them, U i and are the left and right singular vectors of the i-th component, Σ i represents the singular value matrix of the i-th component; Σ i The singular value ρ i Sort and remove the smallest a% of all singular values ​​ρ i and the corresponding left singular vector U i and the right singular vectors Using the remaining singular values ​​ρ i * and the corresponding left singular vector U i * and the right singular vectors The first matrix of the reconstructed component, the first matrix of the reconstructed component, is expressed as:

[0075]

[0076] Among them, F i * is the first matrix of the reconstructed components, U i * The remaining singular value ρ i * The corresponding left singular vectors are, The remaining singular value ρ i* The corresponding right singular vector; each row of the first matrix of the reconstructed eigenmode is taken out from top to bottom, and all the rows are padded with 0 at the beginning and end. The rule of padded 0 is to padded m-1 0s before the first element of the mth row, and padded NL 0s after the Lth element of the mth row. All the rows after padded 0 are sequentially composed into the second matrix H i * OK.

[0077] It should be noted that the calculation of the predicted grid correction value includes converting the second matrix H i * Each column of is averaged to reconstruct the eigenmode Calculate the Euclidean distance between the eigenmodes before and after reconstruction, expressed as:

[0078]

[0079] Among them, R i is the Euclidean distance of the i-th eigenmode component, is the i-th reconstructed eigenmode component, X(t) i is the eigenmode component before the i-th reconstruction, t is the time point, t≤N; the weight of the eigenmode component is calculated, expressed as:

[0080]

[0081] Among them, ρ i is the weight of the i-th eigenmode component; the predicted value of the grid correction is calculated, expressed as:

[0082]

[0083] Among them, y is the predicted value of the grid correction number, and M is the number of eigenmodes after VMD decomposition.

[0084] It should also be noted that the singular values ​​represent the energy distribution of the matrix. Smaller singular values ​​usually correspond to noise and redundant information. By removing the smallest a% of the singular values, these noise and redundant information can be effectively removed, thereby improving the accuracy of data processing; the matrix is ​​padded with 0 because after removing the singular values, the dimension of the reconstructed first matrix will change, and a 0-padded operation is required to maintain the original dimension of the matrix, which is very important for subsequent data processing and model building because the model usually requires input data of fixed dimension.

[0085] S3: Based on the approximate coordinates of the line sway monitoring points and the predicted values ​​of the grid correction numbers, virtual observation values ​​are generated. The virtual observation values ​​and the actual observation values ​​form a short baseline double difference equation, and the real-time Kalman filter is used to obtain the time series of the line sway monitoring point coordinates.

[0086] Furthermore, based on the approximate coordinates and grid correction number prediction values ​​of the line galloping monitoring point, the tropospheric delay, ionospheric delay and comprehensive error prediction values ​​at the line galloping monitoring point are interpolated according to the approximate coordinates and grid correction number prediction values ​​y of the line galloping monitoring point; the ionospheric delay prediction value at the line galloping monitoring point is calculated, spatial structural analysis and spatial variability analysis are performed at the central ionosphere altitude, a preset altitude is used as an ionospheric error correction reference plane, and non-difference ionospheric delay error interpolation calculation of the line galloping monitoring point is performed on the ionospheric error correction reference plane to obtain the ionospheric correction number at the line galloping monitoring point, and the ionospheric delay is calculated according to the ionospheric correction number at the line galloping monitoring point. The non-difference ionospheric delay error interpolation calculation includes obtaining the Kriging equation group of ionospheric total electron content interpolation under linear unbiased and optimal estimation conditions, which is expressed as:

[0087]

[0088] Where μ is the Lagrange multiplier factor, d ij is the distance between the CORS site and the line galloping monitoring point, d i0 is the distance between the interpolation point and the observed value, λ jis the weighting coefficient; the Kriging equations are solved to obtain the weighting coefficient, the estimated value of the total electron content of the ionosphere is calculated based on the weighting coefficient, and the predicted value of the ionospheric delay at the line galloping monitoring point is calculated by the estimated value of the total electron content of the ionosphere; the predicted value of the tropospheric delay at the line galloping monitoring point is calculated, and the tropospheric related error is divided into a dry delay part and a wet delay part. The dry delay part is corrected by the GPT2w or ITG model, and the wet delay part is calculated according to the tropospheric wet delay result solved by each CORS station by using the Kriging interpolation algorithm based on height correction, and the tropospheric correction number of the line galloping monitoring point is calculated, and the tropospheric delay is calculated according to the tropospheric correction number at the line galloping monitoring point. The Kriging interpolation algorithm based on height correction specifically includes taking the average of the elevations of each CORS station to obtain the tropospheric wet delay correction of the interpolation area The elevation datum is used as the basis for calculating the tropospheric wet delay correction model for each CORS station to the tropospheric error correction datum. The spatial structure analysis and variability analysis of the tropospheric wet delay of each CORS station are carried out on the elevation datum. The CORS station interpolates the tropospheric wet delay correction number of the tropospheric wet delay correction datum based on the approximate coordinates of the line galloping monitoring point and the spatial structure analysis and variability analysis results of the tropospheric wet delay using the Kriging algorithm. The elevation difference between the line galloping monitoring point and the tropospheric error correction datum is calculated based on the approximate coordinate elevation of the line galloping monitoring point. The predicted value of the 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. The predicted value of the comprehensive error at the line galloping monitoring point is calculated, and the predicted value of the comprehensive error is obtained by interpolation using the inverse distance weighted method.

[0089] It should be noted that the virtual observation value includes the virtual observation value of the line galloping monitoring point generated according to the tropospheric delay, ionospheric delay and comprehensive error prediction value of the line galloping monitoring point; the calculation method of the virtual observation value of the line galloping monitoring point is to use the coordinates and satellite ephemeris of the line galloping monitoring point and the CORS station to convert the observation value of the CORS station to the line galloping monitoring point, and then take into account the relative difference between the predicted value of the tropospheric delay and the predicted value of the ionospheric delay to obtain the observation value of the line galloping monitoring point; convert the observation value of the CORS station to the line galloping monitoring point, expressed as:

[0090] Φ' V =Φ A -ρ A +ρ V

[0091] P' V =P A -ρ A +ρ V

[0092] Among them, Φ' V is the carrier observation value of the line galloping monitoring point, P' V is the pseudo-range observation value of the line galloping monitoring point, Φ A is the carrier observation value of the CORS site, P A is the pseudorange observation value of the CORS station, ρ A is the geometric distance from the CORS site to the satellite, ρ V is the geometric distance from the line dancing monitoring point to the satellite; calculate the difference after double difference of the tropospheric delay error of the CORS station It is expressed as:

[0093]

[0094] Calculate the difference after double difference of ionospheric delay error at CORS station It is expressed as:

[0095]

[0096] 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 is the geometric distance between the satellite and the receiver; the difference of the double-difference of the tropospheric delay error at the line dancing monitoring point is calculated by the spatial interpolation algorithm The difference between the double difference of the ionospheric delay error and Calculate the observation values ​​of the line galloping monitoring points.

[0097] It should also be noted that the short baseline double difference equation is formed by the line galloping monitoring point, which uses the virtual observation value of the line galloping monitoring point and the actual observation value of the line galloping monitoring point to form a short baseline double difference observation equation. The observation value of the line galloping monitoring point includes the carrier observation value of the line galloping monitoring point and the pseudo-range observation value of the line galloping monitoring point; the carrier observation value Φ of the line galloping monitoring point is calculated. V , expressed as:

[0098]

[0099] in, is the double difference error correction number of the carrier observation value at the line galloping monitoring point, Including the double difference error correction of the carrier observation values ​​corresponding to frequency point 1 and frequency point 2 respectively and It is expressed as:

[0100]

[0101] Calculate the pseudo-range observation value P of the line sway monitoring point V , expressed as:

[0102]

[0103] in, It represents the double difference error correction of pseudo-range of line galloping monitoring point, 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 It is expressed as:

[0104]

[0105] in, and They are the positive numbers of pseudo-range double difference errors of the line galloping monitoring points at frequency points 1 and 2, is the difference between the ionospheric delay errors and the double difference.

[0106] It should also be noted that the short baseline double difference equation formed by the virtual observation value and the actual observation value can effectively eliminate or weaken the common errors in the observation data, such as satellite orbit error, clock error, etc. At the same time, the real-time filtering of the short baseline double difference observation equation through the Kalman filter model can further improve the accuracy and real-time performance of line dance monitoring. The Kalman filter model can effectively integrate the observation data and prior information to obtain a more accurate time series of the line dance monitoring point coordinates.

[0107] Example 2 is an embodiment of the present invention, which provides a three-dimensional state monitoring method for line galloping in areas with unstable signals. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0108] First, the purpose of the experiment is to verify the accuracy and effectiveness of the present invention in three-dimensional state monitoring of line galloping in areas with unstable signals, and to compare it with the existing technology.

[0109] Experimental process: Collect grid correction time series data from CORS station in real time, with a data interval of 1 second and a total of 1000 seconds of data collection; use VMD algorithm to decompose the data and obtain 5 intrinsic mode components; use TensorFlow framework to build LSTM model, with 5 intrinsic mode components as input and 5 intrinsic mode components as output; set model training parameters as follows: number of hidden layer neurons is 50, learning rate is 0.001, training batch size is 64, and number of training rounds is 100; set threshold N to 100 and sliding window length L to 50. Sliding window values ​​are taken for intrinsic mode components with length greater than 100 to create the first matrix; perform SVD decomposition on the first matrix and remove the smallest 10% singular values ​​and the corresponding The left and right singular vectors are used to reconstruct the first matrix, and the matrix is ​​supplemented with 0 to form a second matrix; the eigenmode is reconstructed by taking the average of each column of the second matrix, and the grid correction prediction value is calculated; the error prediction is performed by using the calculated grid correction prediction value to predict the ionospheric delay, tropospheric delay and comprehensive error; the observation value of the CORS station is converted to the line galloping monitoring point by using the coordinates and satellite ephemeris of the line galloping monitoring point and the CORS station, and the relative difference between the predicted value of the tropospheric delay and the predicted value of the ionospheric delay is taken into account to obtain the observation value of the line galloping monitoring point; the virtual observation value and the actual observation value of the line galloping monitoring point are formed into a short baseline double difference equation, and the Kalman filter model is used to perform Kalman filtering in real time to obtain the coordinate time series of the line galloping monitoring point.

[0110] The accuracy of the line dancing monitoring point coordinate time series obtained by the method of the present invention is compared with the coordinate time series obtained by the traditional Kalman filtering method, and the positioning error is calculated, as shown in Table 1 below.

[0111] Table 1 Comparison of data between the present invention and the prior art

[0112]

[0113] It can be seen from the table data that our invention is superior to the existing technology in terms of ionospheric delay prediction error, tropospheric delay prediction error, comprehensive error prediction error and positioning error. This is mainly because our invention adopts VMD decomposition and LSTM model, which can effectively extract and predict the main components in the grid correction number, thereby improving the error prediction accuracy. In addition, our invention further eliminates noise and redundant information through sliding window value selection and SVD decomposition, thereby improving the efficiency and accuracy of data processing.

[0114] Example 3, reference Figure 2 , which is an embodiment of the present invention, provides a line galloping three-dimensional state monitoring system for signal unstable areas, including a VMD decomposition module, a prediction value calculation module, and a double difference observation module.

[0115] Among them, the VMD decomposition module is used to collect the time series data of grid correction numbers in real time, calculate the predicted value of the intrinsic mode component of the grid correction number based on VMD decomposition, and set the sliding window to create the first matrix; the predicted value calculation module is used to perform SVD decomposition on the first matrix, reconstruct the first matrix, form the second matrix after filling the matrix with 0, take the average to reconstruct the intrinsic mode, and calculate the predicted value of the grid correction number; the double difference observation module is used to generate virtual observation values ​​based on the approximate coordinates of the line dancing monitoring point and the predicted value of the grid correction number, and the virtual observation values ​​and the actual observation values ​​form a short baseline double difference equation, and through real-time Kalman filtering, the coordinate time series of the line dancing monitoring point is obtained.

[0116] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0117] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0118] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0119] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art 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.

[0120] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. 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 three-dimensional state monitoring method for line galloping in areas with unstable signals, characterized in that: include: Collect grid correction time series data in real time, calculate the predicted value of the intrinsic mode component of the grid correction based on VMD decomposition, and set the sliding window to create the first matrix; Perform SVD decomposition on the first matrix, reconstruct the first matrix, fill the matrix with 0 to form the second matrix, take the average reconstructed eigenmode, and calculate the predicted value of the grid correction number; Based on the approximate coordinates of the line galloping monitoring points and the predicted values ​​of the grid correction numbers, virtual observation values ​​are generated. The virtual observation values ​​and the actual observation values ​​are used to form a short baseline double difference equation, and the coordinate time series of the line galloping monitoring points is obtained through real-time Kalman filtering.

2. The line galloping three-dimensional state monitoring method for signal unstable areas according to claim 1, characterized in that: The first matrix created by setting the sliding window includes real-time acquisition of grid correction time series data released by the CORS station, and the data is decomposed by VMD to obtain the intrinsic mode components of the grid correction number, {X(0) i , X(1) i , ..., X(N) i }, the intrinsic mode component constructs the LSTM model and obtains the predicted value pd of the intrinsic mode component i ; When the length N of the eigenmode component is greater than the first set threshold, the sliding window L is set, and the length of the sliding window L is less than half of N. Then the sliding window starts to slide from the beginning of the eigenmode component X(0)i to obtain the window data {X(t) i ,X(t+1) i , ..., X(t+L) i }, the sliding step is 1; The intrinsic mode components of the windowed data {X(t) i ,X(t+1) i , ..., X(t+L) i }Create the corresponding first matrix F i , the first matrix F i The creation method is to convert the window data {X(t) corresponding to the eigenmode i i ,X(t+1) i , ..., X(t+L) i } as the rows of the matrix to form the first matrix F i .

3. The line galloping three-dimensional state monitoring method for signal unstable areas according to claim 2, characterized in that: The matrix is ​​supplemented with 0 to form a second matrix, which includes the first matrix F i Perform SVD decomposition, expressed as: Among them, U i and are the left and right singular vectors of the i-th component, ∑ i represents the singular value matrix of the i-th component; ∑ i The singular value ρ i Sort and remove the smallest a% of all singular values and the corresponding left singular vector U i and the right singular vectors Using the remaining singular values ​​ρ i * and the corresponding left singular vector U i * and the right singular vectors The first matrix of the reconstructed component, the first matrix of the reconstructed component, is expressed as: Among them, F i * is the first matrix of the reconstructed components, U i * The remaining singular value ρ i * The corresponding left singular vectors are, The remaining singular value ρ i * The corresponding right singular vectors; Take out each row of the first matrix of the reconstructed eigenmode from top to bottom, and add 0 to the beginning and end of all the taken rows. The rule of adding 0 is to add m-1 0s before the first element of the mth row, and to add NL 0s after the Lth element of the mth row. All the rows after adding 0 are sequentially composed into the second matrix H i * OK.

4. The line galloping three-dimensional state monitoring method for signal unstable areas according to claim 3, characterized in that: The calculation of the grid correction number prediction value includes converting the second matrix H i * Each column of is averaged to reconstruct the eigenmode Calculate the Euclidean distance between the eigenmodes before and after reconstruction, expressed as: Among them, R i is the Euclidean distance of the i-th eigenmode component, is the i-th reconstructed eigenmode component, X(t) i is the eigenmode component before the i-th reconstruction, t is the time point, t≤N; Calculate the weight of the eigenmode component, expressed as: Among them, ρ i is the weight of the i-th eigenmode component; Calculate the predicted value of the grid correction, expressed as: Among them, y is the predicted value of the grid correction number, and M is the number of eigenmodes after VMD decomposition.

5. The line galloping three-dimensional state monitoring method for signal unstable areas according to claim 4, characterized in that: The method based on the approximate coordinates and grid correction number prediction value of the line galloping monitoring point includes interpolating 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 prediction value y of the line galloping monitoring point; Calculate the predicted value of ionospheric delay at the line galloping monitoring point, perform spatial structural analysis and spatial variability analysis at the central ionosphere altitude, use the preset altitude as the ionosphere error correction reference plane, perform non-difference ionospheric delay error interpolation calculation at the line galloping monitoring point on the ionosphere error correction reference plane, and obtain the ionospheric correction number at the line galloping monitoring point. The ionospheric delay is calculated based on the ionospheric correction number at the line galloping monitoring point. The non-difference ionospheric delay error interpolation calculation includes the Kriging equation group of ionospheric total electron content interpolation under linear unbiased and optimal estimation conditions, which is expressed as: Where μ is the Lagrange multiplier factor, d ij is the distance between the CORS site and the line galloping monitoring point, d i0 is the distance between the interpolation point and the observed value, λ j is the weighting coefficient; The Kriging equations are solved to obtain weighting coefficients, and the estimated value of the total electron content of the ionosphere is calculated based on the weighting coefficients. The predicted value of the ionospheric delay at the line galloping monitoring point is calculated by the estimated value of the total electron content of the ionosphere. Calculate the predicted value of tropospheric delay at the line galloping monitoring point, divide the tropospheric related errors into dry delay part and wet delay part, the dry delay part is corrected by GPT2w or ITG model, and the wet delay part is calculated according to the tropospheric wet delay results solved by each CORS station using the Kriging interpolation algorithm based on height correction, calculate the tropospheric correction number of the line galloping monitoring point, and calculate the tropospheric delay according to the tropospheric correction number at the line galloping monitoring point. The Kriging interpolation algorithm based on height correction specifically includes taking the average of the elevations of each CORS station to obtain the elevation reference plane of the tropospheric wet delay correction in the interpolation area, and calculating according to the tropospheric wet delay height difference correction model. The tropospheric wet delay correction number of each CORS station to the tropospheric error correction datum is obtained, and the spatial structure analysis and variability analysis of the tropospheric wet delay of each CORS station are performed on the elevation datum. According to the approximate coordinates of the line galloping monitoring point, the CORS station uses the Kriging algorithm to interpolate the tropospheric wet delay correction number of the tropospheric wet delay correction datum according to the spatial structure analysis and variability analysis results of the tropospheric wet delay. According to the approximate coordinate elevation of the line galloping monitoring point, the height difference between the line galloping monitoring point and the tropospheric error correction datum is calculated. According to the tropospheric wet delay height difference correction model, the predicted value of the tropospheric wet delay at the approximate coordinate elevation of the line galloping monitoring point is calculated. The comprehensive error prediction value at the line galloping monitoring point is calculated and interpolated by the inverse distance weighted method to obtain the comprehensive error prediction value.

6. The line galloping three-dimensional state monitoring method for signal unstable areas according to claim 5, characterized in that: The virtual observation value comprises generating a line galloping monitoring point virtual observation value according to the tropospheric delay, ionospheric delay and comprehensive error prediction value of the line galloping monitoring point; The calculation method of the virtual observation value of the line galloping monitoring point is to use the coordinates and satellite ephemeris of the line galloping monitoring point and the CORS station, convert the observation value of the CORS station to the line galloping monitoring point, and then take into account the relative difference between the predicted value of the tropospheric delay and the predicted value of the ionospheric delay to obtain the observation value of the line galloping monitoring point; The observation value of the CORS station is converted to the line galloping monitoring point, which is expressed as: F′ V =Φ A -r A +r V P′ V =P A -r A +r V Among them, Φ′ V is the carrier observation value of the line galloping monitoring point, P′ V is the pseudo-range observation value of the line galloping monitoring point, Φ A is the carrier observation value of the CORS site, P A is the pseudorange observation value of the CORS station, ρ A is the geometric distance from the CORS site to the satellite, ρ V The geometric distance from the line dancing monitoring point to the satellite; Calculate the difference between the tropospheric delay errors of the CORS site and the double difference It is expressed as: Calculate the difference after double difference of ionospheric delay error at CORS station It is expressed as: 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 is the geometric distance between the satellite and the receiver; 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 Calculate the observation values ​​of the line galloping monitoring points.

7. The line galloping three-dimensional state monitoring method for signal unstable areas according to claim 6, characterized in that: The short baseline double difference equation is formed by the line galloping monitoring point using the line galloping monitoring point virtual observation value and the line galloping monitoring point actual observation value to form a short baseline double difference observation equation, and the line galloping monitoring point observation value includes the line galloping monitoring point carrier observation value and the line galloping monitoring point pseudorange observation value; Calculate the carrier observation value Φ of the line galloping monitoring point V , expressed as: in, is the double difference error correction number of the carrier observation value at the line galloping monitoring point, Including the double difference error correction of the carrier observation values ​​corresponding to frequency point 1 and frequency point 2 respectively and It is expressed as: Calculate the pseudo-range observation value P of the line sway monitoring point V , expressed as: in, It represents the double difference error correction of pseudo-range of line galloping monitoring point, 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 It is expressed as: in, and They are the positive numbers of pseudo-range double difference errors of the line galloping monitoring points at frequency points 1 and 2, is the difference between the ionospheric delay errors and the double difference.

8. A system using the line galloping three-dimensional state monitoring method for signal unstable areas according to any one of claims 1 to 7, characterized in that: It includes VMD decomposition module, prediction value calculation module and double difference observation module; The VMD decomposition module is used to collect grid correction number time series data in real time, calculate the predicted value of the intrinsic mode component of the grid correction number based on VMD decomposition, and set the sliding window to create the first matrix; The prediction value calculation module is used to perform SVD decomposition on the first matrix, reconstruct the first matrix, fill the matrix with 0 to form a second matrix, take the average reconstructed eigenmode, and calculate the prediction value of the grid correction number; The double-difference observation module is used to generate virtual observation values ​​based on the approximate coordinates of the line sway monitoring points and the predicted values ​​of the grid correction numbers. The virtual observation values ​​and the actual observation values ​​form a short baseline double-difference equation, and through real-time Kalman filtering, the coordinate time series of the line sway monitoring points is obtained.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the line galloping three-dimensional state monitoring method for signal unstable areas according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the line galloping three-dimensional state monitoring method for signal unstable areas according to any one of claims 1 to 7 are implemented.

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