A method for monitoring the tilt and settlement status of towers based on Beidou

Abstract

The present invention relates to the technical field of status monitoring, and discloses a method for monitoring the tilt and settlement status of a tower based on Beidou. The present invention first uses an improved wavelet threshold denoising algorithm and a Kalman filter algorithm to denoise the tower positioning data; secondly, a carrier phase differential positioning method is used to establish a double-difference carrier phase expression; then an observation equation is established based on the double-difference carrier phase expression, and the observation equation is solved to obtain the three-dimensional coordinates of the tower to be measured, and the three-dimensional coordinates of the tower to be measured are converted to calculate the inclination angle of the tower; finally, an ELM neural network is trained to obtain an ELM neural network prediction model, and a predicted value of the tower inclination angle is output to realize the monitoring of the tilt and settlement status of the tower. The present invention achieves the purpose of monitoring the tilt and settlement status of the tower by analyzing and processing the tower data, and the method is accurate and objective.

Description

Technical Field

[0001] The present invention relates to the technical field of status monitoring, and in particular to a Beidou-based tower inclination and settlement status monitoring method. Background Art

[0002] Chinese patent CN112284335A discloses a tower settlement and inclination monitoring system and method based on Beidou positioning. The method specifically includes: first determining the reference coordinates, and correcting the reference coordinates to obtain the correction value, using the coordinates of the first monitoring mobile station to solve the coordinates minus the correction value to obtain the coordinates of the monitoring point of the first monitoring mobile station, and calculating the offsets of the monitoring point of the first monitoring mobile station in the X, Y, and H directions based on the coordinates of the monitoring point of the first monitoring mobile station, wherein the offsets in the X and Y directions are recorded as displacement, and the offset in the H direction is recorded as settlement; similarly, calculating the offsets of the monitoring point of the second monitoring mobile station in the X, Y, and H directions; comparing the offsets of the monitoring points of the first and second monitoring mobile stations, if the offsets have a certain offset in the X and Y directions, the second monitoring mobile station is displaced, and at this time, calculating the difference in the H direction of the monitoring points of the first and second monitoring mobile stations, calculating the inclination angle of the tower pole according to the difference and the tower pole length, and judging whether the tower pole has settled by comparing with the threshold value, completing the settlement and inclination monitoring of the tower pole. This method does not accurately correct the positioning data, and the result has errors.

[0003] The traditional method of monitoring the tilt and settlement status of pole towers mainly relies on manual surveys. The obtained tilt and settlement data have low measurement accuracy, some tilt and settlement data require large amounts of calculation, and there are problems such as repeated data collection, which cannot meet the needs of real-time monitoring of pole towers. At the same time, without high-tech technologies such as neural networks, tilt and settlement prediction cannot be achieved, which brings inconvenience to the monitoring of the tilt and settlement status of pole towers. Summary of the invention

[0004] In view of the problems in the related technology, the present invention provides a Beidou-based tower inclination and settlement status monitoring method to overcome the above-mentioned technical problems existing in the existing related technology.

[0005] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0006] The present invention is a method for monitoring the tilt and settlement state of a tower based on Beidou, comprising the following steps:

[0007] S1. Install a Beidou positioning device on the tower, obtain tower positioning data, obtain an initial tower positioning data set, perform a first denoising process on the initial tower positioning data set based on an improved wavelet threshold denoising algorithm, and then perform a second denoising process using a Kalman filter algorithm to obtain a processed tower positioning data set;

[0008] S2, obtaining a reference station positioning data set, obtaining a processed reference station positioning data set after noise reduction processing, combining the processed tower positioning data set, using a carrier phase differential positioning method to calculate a carrier phase observation value, and establishing a double difference carrier phase expression according to the carrier phase observation value;

[0009] S3, according to the double-difference carrier phase expression, establish an observation equation, solve the observation equation to obtain the three-dimensional coordinates of the tower to be measured, then transform the three-dimensional coordinates of the tower to be measured, calculate the tower inclination angle of the tower to be measured, and obtain the tower inclination angle data;

[0010] S4, training the ELM neural network to obtain an ELM neural network prediction model, inputting the tower inclination angle data into the ELM neural network prediction model, outputting a tower inclination angle prediction value, and realizing tower inclination settlement status monitoring.

[0011] The invention obtains an initial tower positioning data set, uses an improved wavelet threshold denoising algorithm to set a noise threshold, performs a first denoising process on the initial tower positioning data set, and then uses a Kalman filter algorithm to perform a second denoising process on the tower positioning data based on the optimal estimate value; the improved wavelet threshold denoising algorithm sets the optimal noise threshold, which can effectively remove noise signals and make the noise curve continuous and smooth. The Kalman filter algorithm can further improve the denoising effect for signals with smaller errors, and the data accuracy is high; secondly, the carrier phase differential positioning method is used to calculate the carrier phase observation value, and a double-difference carrier phase expression is established based on the carrier phase observation value; the double-difference carrier phase observation method can be used Eliminating satellite clock error, ephemeris error and receiver clock error can effectively improve positioning accuracy; then establish an observation equation, solve the observation equation to obtain the three-dimensional coordinates of the tower to be measured, and then convert the three-dimensional coordinates of the tower to be measured to obtain the tower inclination angle; performing differential operations through the observation equation can not only eliminate the errors on the base station and the tower, but also facilitate the calculation of the inclination angle; finally, train the ELM neural network to obtain the ELM neural network prediction model, output the predicted value of the tower inclination angle, and realize the monitoring of the tower inclination and settlement status; this neural network does not need to back-propagate the error, only the number of hidden layer neurons needs to be set to obtain the optimal solution, which effectively solves the problem of complex parameter settings of other neural networks.

[0012] Preferably, the S1 comprises the following steps:

[0013] S11. Install a Beidou positioning device on the tower, denoted as the first Beidou positioning device and the second Beidou positioning device, obtain tower positioning data according to the first Beidou positioning device, the tower positioning data includes a data code, a ranging code and a carrier signal, etc., and select the carrier signal to form an initial tower positioning data set ,in The signal length ism Initial tower positioning data; using an improved wavelet threshold denoising algorithm to perform a first denoising process on the initial tower positioning data in the initial tower positioning data set to obtain a processed tower positioning data set, the specific steps are as follows:

[0014] S111, set the wavelet basis function to F, the decomposition level to a , the wavelet basis function decomposes the initial tower positioning data in the initial tower positioning data set according to the decomposition layer number to obtain the wavelet decomposition coefficient; the noise standard deviation is set to , the attenuation factor is , then the noise threshold is calculated as follows:

[0015] ;

[0016] in, represents the noise threshold;

[0017] S112, comparing the wavelet decomposition coefficient with the noise threshold, when the wavelet decomposition coefficient is less than the noise threshold, setting the initial tower positioning data corresponding to the wavelet decomposition coefficient to 0, when the wavelet decomposition coefficient is greater than or equal to the noise threshold, converting the initial tower positioning data corresponding to the wavelet decomposition coefficient into the wavelet decomposition coefficient after noise reduction, the calculation formula is as follows:

[0018] ;

[0019] in, represents the wavelet decomposition coefficient after denoising, represents the wavelet decomposition coefficient, sgn represents the sign function;

[0020] Performing an inverse transformation on the wavelet decomposition coefficients after denoising to complete the one-dimensional wavelet reconstruction of the wavelet decomposition coefficients after denoising to obtain processed pole tower positioning data; performing a first denoising process on all the initial pole tower positioning data in the initial pole tower positioning data set in turn to obtain a processed pole tower positioning data set;

[0021] S12, using the Kalman filter algorithm to perform a second denoising process on the processed tower positioning data set to obtain a processed tower positioning data set, and the calculation formula is as follows:

[0022] S121, set the signal length obtained at time c to The initial tower positioning data is used to set the state at time c and the state at time c-1, and a Kalman filter algorithm model is established according to the state at time c and the state at time c-1. The Kalman filter algorithm model is used to predict the error covariance to obtain the state at time c+1;

[0023] S122. Use the state at time c+1 to update the error covariance to obtain a new error covariance, and correct the processed tower positioning data in the processed tower positioning data set according to the new error covariance; at this time, based on the state at time c+1, the new error covariance is updated again by calculating the state at time c+2 until the filtering is completed to obtain the processed tower positioning data set.

[0024] The invention uses an improved wavelet threshold denoising algorithm to set a noise threshold to perform the first denoising process on the initial tower positioning data set, which can effectively remove noise signals and make the noise curve continuous and smooth; then uses the Kalman filter algorithm to perform a second denoising process on the tower positioning data based on the optimal estimate value, which can further improve the denoising effect and increase data accuracy.

[0025] Preferably, S2 comprises the following steps:

[0026] S21, select two Beidou satellites, respectively recorded as the first Beidou satellite and the second Beidou satellite, the reference station and the tower corresponding to the first Beidou satellite and the second Beidou satellite are respectively the standard reference station and the tower to be measured; obtain the reference station positioning data set, obtain the processed reference station positioning data set after noise reduction processing, and combine the processed tower positioning data set to obtain the carrier phase observation values ​​of the first Beidou satellite for the standard reference station and the tower to be measured, respectively and The carrier phase observation values ​​of the second BeiDou satellite to the standard reference station and the tower to be measured are and ; Set the carrier frequency to , the speed of light is , then the carrier coefficient ;

[0027] Set the geometric distance between the first BeiDou satellite and the standard reference station to , the geometric distance between the first BeiDou satellite and the tower to be measured is The signal sent by the first Beidou satellite reaches the standard reference station through the ionosphere delay and troposphere delay respectively. and The signal sent by the first Beidou satellite reaches the tower to be measured. The delays through the ionosphere and troposphere are and ; The clock error of the standard reference station is , the clock error of the tower to be measured is , the atomic clock error of the first BeiDou satellite is The integer ambiguity of the first BeiDou satellite monitored by the standard reference station is The integer ambiguity of the first Beidou satellite monitored by the tower to be measured is ; The noise of the standard reference station and the tower to be measured is negligible, then the calculation formula of the carrier phase observation value of the first Beidou satellite to the standard reference station and the tower to be measured is as follows:

[0028] ;

[0029] ;

[0030] Set the geometric distance between the second BeiDou satellite and the standard reference station to , the geometric distance between the second BeiDou satellite and the tower to be measured is When the signal sent by the second BeiDou satellite reaches the standard reference station, the ionospheric delay and tropospheric delay are and When the signal sent by the second BeiDou satellite reaches the tower to be measured, the ionospheric delay and tropospheric delay are and ; The clock error of the standard reference station is , the clock error of the tower to be measured is , the atomic clock error of the second BeiDou satellite is The integer ambiguity of the second BeiDou satellite monitored by the standard reference station is The integer ambiguity of the second BeiDou satellite monitored by the tower to be measured is ; The calculation formula of the carrier phase observation value of the second Beidou satellite to the standard reference station and the tower to be measured is as follows:

[0031] ;

[0032] ;

[0033] S22, calculate the difference between the carrier phase observation values ​​of the first Beidou satellite to the standard reference station and the tower to be measured, and record it as the first carrier phase measurement value , and then calculate the difference between the carrier phase observation values ​​of the second Beidou satellite to the standard reference station and the tower to be measured, and record it as the second carrier phase measurement value , perform a differential operation on the first carrier phase measurement value and the second carrier phase measurement value to obtain a carrier differential value; use the carrier phase observation value calculation formula of the first Beidou satellite to the standard reference station and the tower to be measured and the carrier phase observation value calculation formula of the second Beidou satellite to the standard reference station and the tower to be measured to express the carrier differential value, and obtain the double difference carrier phase expression as follows:

[0034] ;

[0035] ;

[0036] in, It represents the difference between the geometric distance between the first BeiDou satellite and the standard reference station and the geometric distance between the first BeiDou satellite and the tower to be measured. It represents the difference between the integer ambiguity of the first BeiDou satellite monitored by the standard reference station and the integer ambiguity of the first BeiDou satellite monitored by the tower to be measured. Indicates the error of the first Beidou satellite; It represents the difference between the geometric distance between the second BeiDou satellite and the standard reference station and the geometric distance between the second BeiDou satellite and the tower to be measured. It represents the difference between the integer ambiguity of the second BeiDou satellite monitored by the standard reference station and the integer ambiguity of the second BeiDou satellite monitored by the tower to be measured. Indicates the second Beidou satellite error.

[0037] This invention calculates the carrier phase observation value by using the carrier phase differential positioning method, which can eliminate satellite clock error, ephemeris error and receiver clock error, effectively improve positioning accuracy, and then establish a double-difference carrier phase expression based on the carrier phase observation value to facilitate subsequent calculations.

[0038] Preferably, S3 comprises the following steps:

[0039] S31. According to the double-difference carrier phase expression, an observation equation is established. The specific steps are as follows:

[0040] S311, setting the position vector of the tower to be measured relative to the standard reference station to , the unit vector from the standard reference station to the first BeiDou satellite is , the unit vector from the standard reference station to the second BeiDou satellite is , place the tower to be measured on the unit vector The distance difference between the direction and the standard reference station is recorded as the first unit direction geometric distance , the tower to be measured is in the unit vector The difference in distance between the direction and the standard reference station is recorded as the second unit direction geometric distance , the calculation formula is as follows:

[0041] , ;

[0042] The first unit direction geometric distance is equal to the difference between the geometric distance between the first Beidou satellite and the standard reference station and the geometric distance between the first Beidou satellite and the tower to be measured; the second unit direction geometric distance is equal to the difference between the geometric distance between the second Beidou satellite and the standard reference station and the geometric distance between the second Beidou satellite and the tower to be measured;

[0043] S312: combining the double-difference carrier phase expression, the first unit direction geometric distance and the second unit direction geometric distance, the double-difference carrier phase expression is updated to obtain the final double-difference carrier phase expression as follows:

[0044] ;

[0045] in, represents the difference of the double-difference carrier phase expression, represents the integer ambiguity difference in the double-difference carrier phase expression, represents the final error;

[0046] Select Beidou satellites again, record them as the third Beidou satellite and the fourth Beidou satellite, calculate the difference between the double difference carrier phase expressions of the first Beidou satellite and the third Beidou satellite and the difference between the double difference carrier phase expressions of the first Beidou satellite and the fourth Beidou satellite, combine the final double difference carrier phase expressions, and jointly obtain the observation equation to obtain the three-dimensional coordinates of the first tower to be measured. ;

[0047] S32, obtaining the tower positioning data according to the second Beidou positioning device, and calculating the three-dimensional coordinates of the second tower to be measured According to the observation equation, the coordinate correction value is obtained by using the least squares method. , then the coordinate difference calculation formula between the first Beidou positioning device and the second Beidou positioning device is as follows:

[0048] ;

[0049] in, , and Indicates the coordinate difference between the first Beidou positioning device and the second Beidou positioning device;

[0050] The three-dimensional coordinates of the first tower to be measured and the three-dimensional coordinates of the second tower to be measured are in a geocentric earth-fixed rectangular coordinate system, and the horizontal and vertical angles in the geocentric earth-fixed rectangular coordinate system are set to be and , convert the geocentric earth-fixed rectangular coordinate system to the northeast celestial coordinate system, and obtain the converted coordinate difference. The conversion formula is as follows:

[0051] ;

[0052] in, , and Represents the coordinate difference after transformation;

[0053] The tower inclination angle of the tower to be measured is calculated according to the converted coordinate difference, and the calculation formula is as follows:

[0054] ;

[0055] in, Indicates the inclination angle of the tower to be measured;

[0056] S33, setting the tower inclination angle threshold When the tower inclination angle of the tower to be measured is less than the tower inclination angle threshold, the tower inclination angle of the tower to be measured is good, otherwise the tower inclination angle of the tower to be measured is in a dangerous state, the tower inclination angle data is obtained, and the tower inclination angle data is transmitted to the monitoring platform.

[0057] The invention establishes an observation equation and performs differential operations on the observation equation, which can not only eliminate the errors on the reference station and the tower, but also facilitates solving the observation equation to obtain the three-dimensional coordinates of the tower to be measured, and then converts the three-dimensional coordinates of the tower to be measured to obtain the tower inclination angle. The method is simple.

[0058] Preferably, S4 comprises the following steps:

[0059] S41. Obtain historical pole tower tilt angle data on the monitoring platform to form a historical pole tower tilt angle data set ,in Indicates i The historical tower inclination angle data set is used to train the ELM (extreme learning machine) neural network to obtain the ELM neural network prediction model. The specific steps are as follows;

[0060] S411, set a time series, establish a functional relationship between the historical pole tower inclination angle data in the historical pole tower inclination angle data set and the time series, set the sliding window size to n, use the sliding window to select the historical pole tower inclination angle data, obtain m historical pole tower inclination angle data subsets, generate a historical pole tower inclination angle data matrix, the historical pole tower inclination angle data matrix has m rows and n columns; divide the historical pole tower inclination angle data matrix into a sample training set and a sample test set, set the number of ELM neural network input nodes to , the number of neurons in the hidden layer is , the number of nodes in the output layer is 1, and the activation function is the Sigmoid function;

[0061] S412, input the sample training set into the ELM neural network, iterate continuously until the ELM neural network converges, and obtain a trained ELM neural network; input the sample test set into the trained ELM neural network, set the accuracy threshold, and when the output result accuracy is greater than the accuracy threshold, obtain the ELM neural network prediction model, otherwise adjust the weight until the output result accuracy is greater than the accuracy threshold;

[0062] S42, inputting the tower inclination angle data into the ELM neural network model, outputting the tower inclination angle prediction value, and realizing the tower inclination settlement state monitoring.

[0063] This invention obtains the ELM neural network prediction model by training the ELM neural network, outputs the predicted value of the tower inclination angle, and realizes the monitoring of the tower inclination and settlement status; this neural network does not need to backpropagate the error, and only needs to set the number of hidden layer neurons to obtain the optimal solution, which effectively solves the problem of complex parameter settings of other neural networks.

[0064] This embodiment also discloses a system for monitoring the tilt and settlement state of a tower based on Beidou, which specifically includes: a tower positioning data denoising module, a double-difference carrier phase expression establishment module, a tower tilt angle data calculation module and a tower tilt angle prediction module;

[0065] The tower positioning data denoising module is used to perform data denoising using an improved wavelet threshold denoising algorithm and a Kalman filter algorithm;

[0066] The double-difference carrier phase expression establishing module is used to establish a double-difference carrier phase expression using a carrier phase differential positioning method;

[0067] The tower inclination angle data calculation module is used to calculate the three-dimensional coordinates of the tower to be measured, and then convert the three-dimensional coordinates to obtain the tower inclination angle data;

[0068] The pole tower inclination angle prediction module is used to train the ELM neural network to obtain an ELM neural network prediction model and output a pole tower inclination angle prediction value.

[0069] The present invention has the following beneficial effects:

[0070] 1. The invention uses an improved wavelet threshold denoising algorithm to set a noise threshold to perform the first denoising process on the initial tower positioning data set, which can effectively remove noise signals and make the noise curve continuous and smooth; then uses the Kalman filter algorithm to perform a second denoising process on the tower positioning data based on the optimal estimate value, which can further improve the denoising effect and increase the data accuracy.

[0071] 2. The present invention calculates the carrier phase observation value by using the carrier phase differential positioning method, which can eliminate the satellite clock error, ephemeris error and receiver clock error, effectively improve the positioning accuracy, and then establishes a double-difference carrier phase expression based on the carrier phase observation value to facilitate subsequent calculations.

[0072] 3. The invention can eliminate the errors on the reference station and the tower by establishing an observation equation and performing differential operations on the observation equation, and obtain the three-dimensional coordinates of the tower to be measured by solving the observation equation, and then convert the three-dimensional coordinates of the tower to be measured to obtain the tower inclination angle. The method is simple.

[0073] 4. The invention obtains the ELM neural network prediction model by training the ELM neural network. The neural network does not need to back-propagate the error, and only needs to set the number of hidden layer neurons to obtain the optimal solution, which effectively solves the problem of complex parameter settings of other neural networks.

[0074] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For ordinary technicians in this field, they can also obtain drawings based on these drawings without paying creative work.

[0076] Figure 1 A schematic diagram of the process of monitoring the tilt and settlement state of a tower by a Beidou-based tower tilt and settlement state monitoring system provided by the present invention. DETAILED DESCRIPTION

[0077] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0078] In the description of the present invention, it is necessary to understand that the terms "opening", "upper", "lower", "top", "middle", "inside" and the like indicating orientation or positional relationship are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the components or elements referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the invention.

[0079] Example 1

[0080] See also Figure 1 This embodiment discloses a method for monitoring the tilt and settlement status of a tower based on Beidou, which specifically includes the following contents:

[0081] S1. Install a Beidou positioning device on the tower, obtain tower positioning data, obtain an initial tower positioning data set, perform a first denoising process on the initial tower positioning data set based on an improved wavelet threshold denoising algorithm, and then perform a second denoising process using a Kalman filter algorithm to obtain a processed tower positioning data set;

[0082] The S1 comprises the following steps:

[0083] S11. Install a Beidou positioning device on the tower, denoted as the first Beidou positioning device and the second Beidou positioning device, obtain tower positioning data according to the first Beidou positioning device, the tower positioning data includes a data code, a ranging code and a carrier signal, etc., and select the carrier signal to form an initial tower positioning data set ,in The signal length is m Initial tower positioning data; using an improved wavelet threshold denoising algorithm to perform a first denoising process on the initial tower positioning data in the initial tower positioning data set to obtain a processed tower positioning data set, the specific steps are as follows:

[0084] S111, set the wavelet basis function to F, the decomposition level to a , the wavelet basis function decomposes the initial tower positioning data in the initial tower positioning data set according to the decomposition layer number to obtain the wavelet decomposition coefficient; the noise standard deviation is set to , the attenuation factor is , then the noise threshold is calculated as follows:

[0085] ;

[0086] in, represents the noise threshold;

[0087] S112, comparing the wavelet decomposition coefficient with the noise threshold, when the wavelet decomposition coefficient is less than the noise threshold, setting the initial tower positioning data corresponding to the wavelet decomposition coefficient to 0, when the wavelet decomposition coefficient is greater than or equal to the noise threshold, converting the initial tower positioning data corresponding to the wavelet decomposition coefficient into the wavelet decomposition coefficient after noise reduction, the calculation formula is as follows:

[0088] ;

[0089] in, represents the wavelet decomposition coefficient after denoising, represents the wavelet decomposition coefficient, sgn represents the sign function;

[0090] Performing an inverse transformation on the wavelet decomposition coefficients after denoising to complete the one-dimensional wavelet reconstruction of the wavelet decomposition coefficients after denoising to obtain processed pole tower positioning data; performing a first denoising process on all the initial pole tower positioning data in the initial pole tower positioning data set in turn to obtain a processed pole tower positioning data set;

[0091] S12, using the Kalman filter algorithm to perform a second denoising process on the processed tower positioning data set to obtain a processed tower positioning data set, and the calculation formula is as follows:

[0092] S121, set the signal length obtained at time c to The initial tower positioning data is used to set the state at time c and the state at time c-1, and a Kalman filter algorithm model is established according to the state at time c and the state at time c-1. The Kalman filter algorithm model is used to predict the error covariance to obtain the state at time c+1;

[0093] S122, using the state at time c+1 to update the error covariance to obtain a new error covariance, and correcting the processed tower positioning data in the processed tower positioning data set according to the new error covariance; at this time, based on the state at time c+1, the new error covariance is updated again by calculating the state at time c+2 until the filtering is completed to obtain a processed tower positioning data set;

[0094] S2, obtaining a reference station positioning data set, obtaining a processed reference station positioning data set after noise reduction processing, combining the processed tower positioning data set, using a carrier phase differential positioning method to calculate a carrier phase observation value, and establishing a double difference carrier phase expression according to the carrier phase observation value;

[0095] The S2 comprises the following steps:

[0096] S21, select two Beidou satellites, respectively recorded as the first Beidou satellite and the second Beidou satellite, the reference station and the tower corresponding to the first Beidou satellite and the second Beidou satellite are respectively the standard reference station and the tower to be measured; obtain the reference station positioning data set, obtain the processed reference station positioning data set after noise reduction processing, and combine the processed tower positioning data set to obtain the carrier phase observation values ​​of the first Beidou satellite for the standard reference station and the tower to be measured, respectively and The carrier phase observation values ​​of the second BeiDou satellite to the standard reference station and the tower to be measured are and ; Set the carrier frequency to , the speed of light is , then the carrier coefficient ;

[0097] Set the geometric distance between the first BeiDou satellite and the standard reference station to , the geometric distance between the first BeiDou satellite and the tower to be measured is The signal sent by the first Beidou satellite reaches the standard reference station through the ionosphere delay and troposphere delay respectively. and The signal sent by the first Beidou satellite reaches the tower to be measured. The delays through the ionosphere and troposphere are and ; The clock error of the standard reference station is , the clock error of the tower to be measured is , the atomic clock error of the first BeiDou satellite is The integer ambiguity of the first BeiDou satellite monitored by the standard reference station is The integer ambiguity of the first Beidou satellite monitored by the tower to be measured is ; The noise of the standard reference station and the tower to be measured is negligible, then the calculation formula of the carrier phase observation value of the first Beidou satellite to the standard reference station and the tower to be measured is as follows:

[0098] ;

[0099] ;

[0100] Set the geometric distance between the second BeiDou satellite and the standard reference station to , the geometric distance between the second BeiDou satellite and the tower to be measured is When the signal sent by the second BeiDou satellite reaches the standard reference station, the ionospheric delay and tropospheric delay are and When the signal sent by the second BeiDou satellite reaches the tower to be measured, the ionospheric delay and tropospheric delay are and ; The clock error of the standard reference station is , the clock error of the tower to be measured is , the atomic clock error of the second BeiDou satellite is The integer ambiguity of the second BeiDou satellite monitored by the standard reference station is The integer ambiguity of the second BeiDou satellite monitored by the tower to be measured is ; The calculation formula of the carrier phase observation value of the second Beidou satellite to the standard reference station and the tower to be measured is as follows:

[0101] ;

[0102] ;

[0103] S22, calculate the difference between the carrier phase observation values ​​of the first Beidou satellite to the standard reference station and the tower to be measured, and record it as the first carrier phase measurement value , and then calculate the difference between the carrier phase observation values ​​of the second Beidou satellite to the standard reference station and the tower to be measured, and record it as the second carrier phase measurement value , perform a differential operation on the first carrier phase measurement value and the second carrier phase measurement value to obtain a carrier differential value; use the carrier phase observation value calculation formula of the first Beidou satellite to the standard reference station and the tower to be measured and the carrier phase observation value calculation formula of the second Beidou satellite to the standard reference station and the tower to be measured to express the carrier differential value, and obtain the double difference carrier phase expression as follows:

[0104] ;

[0105] ;

[0106] in, It represents the difference between the geometric distance between the first BeiDou satellite and the standard reference station and the geometric distance between the first BeiDou satellite and the tower to be measured. It represents the difference between the integer ambiguity of the first BeiDou satellite monitored by the standard reference station and the integer ambiguity of the first BeiDou satellite monitored by the tower to be measured. Indicates the error of the first Beidou satellite; It represents the difference between the geometric distance between the second BeiDou satellite and the standard reference station and the geometric distance between the second BeiDou satellite and the tower to be measured. It represents the difference between the integer ambiguity of the second BeiDou satellite monitored by the standard reference station and the integer ambiguity of the second BeiDou satellite monitored by the tower to be measured. Indicates the second Beidou satellite error;

[0107] S3, according to the double-difference carrier phase expression, establish an observation equation, solve the observation equation to obtain the three-dimensional coordinates of the tower to be measured, then transform the three-dimensional coordinates of the tower to be measured, calculate the tower inclination angle of the tower to be measured, and obtain the tower inclination angle data;

[0108] The S3 comprises the following steps:

[0109] S31. According to the double-difference carrier phase expression, an observation equation is established. The specific steps are as follows:

[0110] S311, setting the position vector of the tower to be measured relative to the standard reference station to , the unit vector from the standard reference station to the first BeiDou satellite is , the unit vector from the standard reference station to the second BeiDou satellite is , place the tower to be measured on the unit vector The difference in distance between the direction and the standard reference station is recorded as the first unit direction geometric distance , the tower to be measured is in the unit vector The difference in distance between the direction and the standard reference station is recorded as the second unit direction geometric distance , the calculation formula is as follows:

[0111] , ;

[0112] The first unit direction geometric distance is equal to the difference between the geometric distance between the first Beidou satellite and the standard reference station and the geometric distance between the first Beidou satellite and the tower to be measured; the second unit direction geometric distance is equal to the difference between the geometric distance between the second Beidou satellite and the standard reference station and the geometric distance between the second Beidou satellite and the tower to be measured;

[0113] S312: combining the double-difference carrier phase expression, the first unit direction geometric distance and the second unit direction geometric distance, the double-difference carrier phase expression is updated to obtain the final double-difference carrier phase expression as follows:

[0114] ;

[0115] in, represents the difference of the double-difference carrier phase expression, represents the integer ambiguity difference in the double-difference carrier phase expression, represents the final error;

[0116] Select Beidou satellites again, record them as the third Beidou satellite and the fourth Beidou satellite, calculate the difference between the double difference carrier phase expressions of the first Beidou satellite and the third Beidou satellite and the difference between the double difference carrier phase expressions of the first Beidou satellite and the fourth Beidou satellite, combine the final double difference carrier phase expressions, and jointly obtain the observation equation to obtain the three-dimensional coordinates of the first tower to be measured. ;

[0117] S32, obtaining the tower positioning data according to the second Beidou positioning device, and calculating the three-dimensional coordinates of the second tower to be measured According to the observation equation, the coordinate correction value is obtained by using the least squares method. , then the coordinate difference calculation formula between the first Beidou positioning device and the second Beidou positioning device is as follows:

[0118] ;

[0119] in, , and Indicates the coordinate difference between the first Beidou positioning device and the second Beidou positioning device;

[0120] The three-dimensional coordinates of the first tower to be measured and the three-dimensional coordinates of the second tower to be measured are in a geocentric earth-fixed rectangular coordinate system, and the horizontal and vertical angles in the geocentric earth-fixed rectangular coordinate system are set to be and , convert the geocentric earth-fixed rectangular coordinate system to the northeast celestial coordinate system, and obtain the converted coordinate difference. The conversion formula is as follows:

[0121] ;

[0122] in, , and Represents the coordinate difference after transformation;

[0123] The tower inclination angle of the tower to be measured is calculated according to the converted coordinate difference, and the calculation formula is as follows:

[0124] ;

[0125] in, Indicates the inclination angle of the tower to be measured;

[0126] S33, setting the tower inclination angle threshold When the tower inclination angle of the tower to be measured is less than the tower inclination angle threshold, the tower inclination angle of the tower to be measured is good, otherwise the tower inclination angle of the tower to be measured is in a dangerous state, the tower inclination angle data is obtained, and the tower inclination angle data is transmitted to the monitoring platform;

[0127] S4, training the ELM neural network to obtain an ELM neural network prediction model, inputting the tower inclination angle data into the ELM neural network prediction model, outputting a tower inclination angle prediction value, and realizing tower inclination settlement state monitoring;

[0128] The S4 comprises the following steps:

[0129] S41. Obtain historical pole tower tilt angle data on the monitoring platform to form a historical pole tower tilt angle data set ,in Indicates i The historical tower inclination angle data set is used to train the ELM neural network to obtain the ELM neural network prediction model. The specific steps are as follows:

[0130] S411, set a time series, establish a functional relationship between the historical pole tower inclination angle data in the historical pole tower inclination angle data set and the time series, set the sliding window size to n, use the sliding window to select the historical pole tower inclination angle data, obtain m historical pole tower inclination angle data subsets, generate a historical pole tower inclination angle data matrix, the historical pole tower inclination angle data matrix has m rows and n columns; divide the historical pole tower inclination angle data matrix into a sample training set and a sample test set, set the number of ELM neural network input nodes to , the number of neurons in the hidden layer is , the number of nodes in the output layer is 1, and the activation function is the Sigmoid function;

[0131] S412, input the sample training set into the ELM neural network, iterate continuously until the ELM neural network converges, and obtain a trained ELM neural network; input the sample test set into the trained ELM neural network, set the accuracy threshold, and when the output result accuracy is greater than the accuracy threshold, obtain the ELM neural network prediction model, otherwise adjust the weight until the output result accuracy is greater than the accuracy threshold;

[0132] S42, inputting the tower inclination angle data into the ELM neural network model, outputting the tower inclination angle prediction value, and realizing the tower inclination settlement state monitoring.

[0133] Example 2

[0134] This embodiment also discloses a system for monitoring the tilt and settlement state of a tower based on Beidou, which specifically includes: a tower positioning data denoising module, a double-difference carrier phase expression establishment module, a tower tilt angle data calculation module and a tower tilt angle prediction module;

[0135] The tower positioning data denoising module is used to perform data denoising using an improved wavelet threshold denoising algorithm and a Kalman filter algorithm;

[0136] The double-difference carrier phase expression establishing module is used to establish a double-difference carrier phase expression using a carrier phase differential positioning method;

[0137] The tower inclination angle data calculation module is used to calculate the three-dimensional coordinates of the tower to be measured, and then convert the three-dimensional coordinates to obtain the tower inclination angle data;

[0138] The pole tower inclination angle prediction module is used to train the ELM neural network to obtain an ELM neural network prediction model and output a pole tower inclination angle prediction value.

[0139] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0140] The preferred embodiments of the invention disclosed above are only used to help explain the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the invention, so that those skilled in the art can understand and use the invention well.

Claims

1. A method for monitoring the tilt and settlement status of a tower based on Beidou, characterized in that: The steps include: S1. Install a Beidou positioning device on the tower, obtain tower positioning data, obtain an initial tower positioning data set, perform a first denoising process on the initial tower positioning data set based on an improved wavelet threshold denoising algorithm, and then perform a second denoising process using a Kalman filter algorithm to obtain a processed tower positioning data set; S2, obtaining a reference station positioning data set, obtaining a processed reference station positioning data set after noise reduction processing, combining the processed tower positioning data set, using a carrier phase differential positioning method to calculate a carrier phase observation value, and establishing a double difference carrier phase expression according to the carrier phase observation value; S3, according to the double-difference carrier phase expression, establish an observation equation, solve the observation equation to obtain the three-dimensional coordinates of the tower to be measured, then transform the three-dimensional coordinates of the tower to be measured, calculate the tower inclination angle of the tower to be measured, and obtain the tower inclination angle data; S4, training the ELM neural network to obtain an ELM neural network prediction model, inputting the tower inclination angle data into the ELM neural network prediction model, outputting a tower inclination angle prediction value, and realizing tower inclination settlement state monitoring; The S1 comprises the following steps: S11, installing a first Beidou positioning device and a second Beidou positioning device on the tower, obtaining tower positioning data according to the first Beidou positioning device to form an initial tower positioning data set; performing a first denoising process on the initial tower positioning data set using an improved wavelet threshold denoising algorithm to obtain a processed tower positioning data set; S12, performing a second denoising process on the processed pole tower positioning data set using a Kalman filter algorithm to obtain a processed pole tower positioning data set; The S2 comprises the following steps: S21, select two Beidou satellites, respectively recorded as the first Beidou satellite and the second Beidou satellite, the reference station and the tower corresponding to the first Beidou satellite and the second Beidou satellite are respectively the standard reference station and the tower to be measured; obtain the reference station positioning data set, obtain the processed reference station positioning data set after noise reduction processing, and calculate the carrier phase observation value of the first Beidou satellite to the standard reference station and the tower to be measured and the carrier phase observation value of the second Beidou satellite to the standard reference station and the tower to be measured in combination with the processed tower positioning data set; S22, use the first Beidou satellite to measure the carrier phase observation values ​​of the standard reference station and the tower to be measured and The carrier phase observation values ​​of the second BeiDou satellite for the standard reference station and the tower to be measured represent the carrier difference value, and obtain the double-difference carrier phase expression.

2. A method for monitoring the tilt and settlement status of a tower based on Beidou according to claim 1, characterized in that: The S11 comprises the following steps: S111, setting a wavelet basis function and a decomposition layer number, wherein the wavelet basis function decomposes the initial tower positioning data in the initial tower positioning data set according to the decomposition layer number to obtain a wavelet decomposition coefficient, and then calculates a noise threshold; S112, comparing the wavelet decomposition coefficient with a noise threshold to obtain the wavelet decomposition coefficient after noise reduction, performing one-dimensional wavelet reconstruction on the wavelet decomposition coefficient after noise reduction to obtain a processed tower positioning data set.

3. The method for monitoring the tilt and settlement status of a tower based on Beidou according to claim 1, characterized in that: The S3 comprises the following steps: S31, establishing an observation equation according to the double-difference carrier phase expression, and solving the observation equation to obtain the three-dimensional coordinates of the first tower to be measured; S32, acquiring tower positioning data according to the second Beidou positioning device, calculating the three-dimensional coordinates of the second tower to be measured, performing coordinate system conversion on the three-dimensional coordinates of the first tower to be measured and the three-dimensional coordinates of the second tower to be measured to obtain a converted coordinate difference; calculating the tower inclination angle of the tower to be measured according to the converted coordinate difference; S33, setting the tower inclination angle threshold When the tower inclination angle of the tower to be measured is less than the tower inclination angle threshold, the tower inclination angle of the tower to be measured is good, otherwise the tower inclination angle of the tower to be measured is in a dangerous state, the tower inclination angle data is obtained, and the tower inclination angle data is transmitted to the monitoring platform.

4. The method for monitoring the tilt and settlement status of a tower based on Beidou according to claim 3 is characterized in that: The S31 comprises the following steps: S311, setting the position vector of the tower to be measured relative to the standard reference station to , the unit vector from the standard reference station to the first BeiDou satellite is , the unit vector from the standard reference station to the second BeiDou satellite is , place the tower to be measured on the unit vector The distance difference between the direction and the standard reference station is recorded as the first unit direction geometric distance , the tower to be measured is in the unit vector The difference in distance between the direction and the standard reference station is recorded as the second unit direction geometric distance ; S312, combining the double-difference carrier phase expression, the first unit direction geometric distance and the second unit direction geometric distance, updating the double-difference carrier phase expression to obtain a final double-difference carrier phase expression; Select Beidou satellites again, recorded as the third Beidou satellite and the fourth Beidou satellite, calculate the difference between the double difference carrier phase expressions of the first Beidou satellite and the third Beidou satellite and the difference between the double difference carrier phase expressions of the first Beidou satellite and the fourth Beidou satellite, respectively, combine the final double difference carrier phase expressions, obtain the observation equations together, and calculate the three-dimensional coordinates of the first tower to be measured.

5. The method for monitoring the tilt and settlement status of a tower based on Beidou according to claim 1, characterized in that: The S4 comprises the following steps: S41, acquiring historical pole tower inclination angle data on the monitoring platform to form a historical pole tower inclination angle data set, using the historical pole tower inclination angle data set to train the ELM neural network to obtain the ELM neural network prediction model; S42, inputting the tower inclination angle data into the ELM neural network model, outputting the tower inclination angle prediction value, and realizing the tower inclination settlement state monitoring.

6. The method for monitoring the tilt and settlement status of a tower based on Beidou according to claim 5 is characterized in that: The method of using the historical tower inclination angle data set to train the ELM neural network to obtain the ELM neural network prediction model comprises the following steps: Set a time series, establish a functional relationship between the historical pole tower inclination angle data in the historical pole tower inclination angle data set and the time series, set the sliding window size to n, use the sliding window to select the historical pole tower inclination angle data, obtain m historical pole tower inclination angle data subsets, and generate a historical pole tower inclination angle data matrix, wherein the historical pole tower inclination angle data matrix has m rows and n columns; divide the historical pole tower inclination angle data matrix into a sample training set and a sample test set, set the number of ELM neural network input nodes to , the number of neurons in the hidden layer is , the number of nodes in the output layer is 1, and the activation function is the Sigmoid function; Inputting the sample training set into the ELM neural network, iterating continuously until the ELM neural network converges, and obtaining a trained ELM neural network; The sample test set is input into the trained ELM neural network, and the accuracy threshold is set. When the accuracy of the output result is greater than the accuracy threshold, the ELM neural network prediction model is obtained. Otherwise, the weight is adjusted until the accuracy of the output result is greater than the accuracy threshold.

7. A system for implementing the Beidou-based tower tilt and settlement status monitoring method as claimed in any one of claims 1 to 6, characterized in that: Specifically include: Tower positioning data denoising module, double-difference carrier phase expression building module, tower tilt angle data calculation module and tower tilt angle prediction module; The tower positioning data denoising module is used to perform data denoising using an improved wavelet threshold denoising algorithm and a Kalman filter algorithm; The double-difference carrier phase expression establishing module is used to establish a double-difference carrier phase expression using a carrier phase differential positioning method; The tower inclination angle data calculation module is used to calculate the three-dimensional coordinates of the tower to be measured, and then convert the three-dimensional coordinates to obtain the tower inclination angle data; The pole tower inclination angle prediction module is used to train the ELM neural network to obtain an ELM neural network prediction model and output a pole tower inclination angle prediction value.

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