A method for monitoring power tower tilt and settlement based on improved ADO BeiDou measurement

Abstract

The present invention discloses a method for monitoring the tilt and settlement of power towers using Beidou measurement based on an improved ADO. This method relates to the technical field of monitoring the tilt and settlement of power towers. The method comprises the following steps: receiving radio frequency carrier signals from Beidou satellites using dual antennas, detecting environmental interference, analyzing non-interference signals, and transmitting them to a cloud computing center via an encrypted gateway; establishing a mathematical model for the attitude angle of the tower; solving the mathematical model using an improved adaptive dragonfly optimization algorithm to obtain the baseline attitude angle; and calculating the tilt and settlement of transmission line towers. The present invention offers greater flexibility and adaptability, effectively addressing issues such as high computing resource and time costs and high load, enabling more efficient solution of the mathematical model for the attitude angle, thereby improving detection accuracy and efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of power tower inclination and settlement monitoring, and in particular to a method for monitoring power tower inclination and settlement based on improved ADO Beidou measurement. Background Art

[0002] Accidents involving the tilt, subsidence, and collapse of transmission line towers are primarily caused by natural factors (such as strong winds, snow, hail, heavy rain, and earthquakes) and human factors (such as construction and mining), and are a major safety hazard for power transmission systems. These accidents not only result in enormous economic losses but also pose a serious threat to the stability and reliability of the power system. The "Electric Power Industry Standard of the People's Republic of China - Operating Procedures for Overhead Transmission Lines" DL / T 741-2019 specifies the tilt parameter ranges for power towers of different heights. If these ranges are exceeded, prompt warnings and maintenance should be issued. Therefore, automated, intelligent, and precise monitoring of the tilt and subsidence of power towers is of great significance.

[0003] Currently, there are three types of automated methods for detecting the tilt and settlement of transmission line towers:

[0004] 1. Tilt Sensor Method: Traditional tilt sensor methods for monitoring the tilt of transmission line towers have certain limitations. Tilt sensors can only monitor the local area where they are installed and cannot reflect the tilt and settlement of the entire tower. Furthermore, due to multiple factors such as the site environment and the tower structure, the measurement error of tilt sensors is relatively large, failing to meet the tilt accuracy requirements of the DL / T 741-2019 specification. 2. GPS-based Positioning Technology: This method requires the construction of a reference station on the transmission line tower or the use of a CORS system. GPS rover stations are located via the reference station to calculate the tower's tilt and settlement. However, this method is costly, and the calculation of tower tilt based on single-point positioning results is subject to certain errors, especially when the tower base is not fixed. 3. Beidou-based Positioning Technology: This method uses dual antennas to receive the RF carrier signals of Beidou satellites. The Beidou receiver then analyzes and processes these signals to obtain carrier phase, satellite elevation, and azimuth data, which are then transmitted to the main processor. On the main processor, the carrier phase double-difference equation is used to establish a mathematical model of the attitude angle, and various optimization algorithms are used to solve the attitude to determine the inclination and settlement degree of the transmission line tower.

[0005] These automated methods typically employ optimization algorithms such as genetic algorithms, simulated annealing, bee colony algorithms, and particle swarm optimization. However, these methods face challenges such as high time and computational costs and heavy workloads. This is particularly true when monitoring a large number of transmission line towers, as these methods incur significant computational and time costs, leaving room for improvement. Summary of the Invention

[0006] This invention aims to address the shortcomings of existing technologies by proposing a method for monitoring power tower tilt and settlement using Beidou-based measurement using an improved ADO. Its advantages include greater flexibility and adaptability; it effectively addresses issues such as high computational resources and time costs, as well as high loads; and it enables a more efficient solution of the attitude angle mathematical model, thereby improving detection accuracy and efficiency.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A method for monitoring the tilt and settlement of power towers based on improved ADO Beidou measurement includes the following steps:

[0009] Step 1: Dual antennas receive the RF carrier signal from the Beidou satellite

[0010] Install dual antennas A and B on the top of the power tower to receive the RF carrier signals of n epochs from m BeiDou satellites (m, n ≥ 4), and the vector from the center of antenna A to the center of antenna B is called baseline b.

[0011] Step 2: Environmental interference detection

[0012] Set two empty sets T(n) and J(n), where T(n) is the non-interference frequency set and J(n) is the interference frequency set; perform fast Fourier transform (FFT) on the received signal envelope r(n) to obtain its frequency domain representation R(n), and use the envelope calculation formula to calculate the envelope A(n) of the received signal; sort A(n) in descending order, take the q spectral lines with smaller values, define them as the original non-interference frequency set T(n), and use the spectral line mean calculation formula to calculate its spectral line mean; set the false alarm probability of the interference detection algorithm to P f , calculate the threshold factor a according to the threshold factor calculation formula, and then calculate the original threshold value T1 = a × E{I(n)};

[0013] Compare the above step T(n) with the original threshold value T1, move the elements greater than the threshold value to the interference frequency point set J(n), otherwise retain them in the T(n) set; recalculate the updated spectrum line mean E{I(n)} of T(n) according to step 2, and calculate the new threshold value T2 according to step 2 until all elements in T(n) are less than or equal to the threshold value; finally, obtain the non-interference frequency point set T(n) and the interference frequency point set J(n);

[0014] Step 3: Analyze the non-interference signal and transmit it to the cloud computing center through the encrypted gateway

[0015] Use the BeiDou board to analyze the non-interference signals received by the dual antennas A and B respectively, and obtain the carrier phase of n epochs of m BeiDou satellites Satellite elevation angle and satellite azimuth The parsed information such as carrier phase, satellite altitude angle, satellite azimuth angle, etc. is encrypted and transmitted to the cloud computing center through the gateway service for attitude solution;

[0016] Step 4: Establish a mathematical model of the tower attitude angle

[0017] The cloud computing center decrypts the encrypted information sent by the receiver; calculates the carrier phase of the kth epoch received by antenna A and antenna B respectively. and And calculate the difference between them to get the carrier phase single difference Similarly, calculate the carrier phase of the kth epoch of the jth satellite received by antenna A and antenna B and And calculate the difference between them to get the carrier phase single difference

[0018] Using the carrier phase single difference calculated in the above steps, the carrier phase double difference equation can be calculated according to the carrier phase double difference equation calculation formula; the attitude angle mathematical model of the baseline b is established according to the attitude angle mathematical model calculation formula;

[0019] Step 5: Use the improved adaptive dragonfly optimization algorithm to solve the attitude angle mathematical model and obtain the attitude angle of baseline b

[0020] Randomly generate a group of dragonflies X i (i=1,2,…,n), and set their positions and velocities to random values; suppose the dimension of each dragonfly is 2-dimensional (x,y). In order to meet the constraints, ensure that the position of each dragonfly in the initial population meets the following conditions: the first dimension is the pitch angle θ, [y min ,y max ] is set to [-90°, +90°], the second dimension is the heading angle γ, [x min ,x max ] is set to [0°, 360°]; the corresponding step vector ΔX is randomly initialized for each dragonfly i (i=1,2,…,n);

[0021] Calculate the objective function value of all dragonflies: For each dragonfly, calculate the fitness value of its current iteration position, that is, the objective function value; according to the position and speed of the dragonfly, calculate its next position; suppose t represents the number of current iterations, x i (t) and y i(t) represents the coordinate position of the i-th dragonfly, v x,i (t) and v y,i (t) represents the current speed of the dragonfly, and the next position of the dragonfly can be expressed using the dragonfly position calculation formula. When updating the speed, add a random perturbation term: assuming c1 and c2 are two constants, and r1 and r2 are two random numbers, the next speed of the dragonfly can be expressed using the dragonfly speed calculation formula.

[0022] In order to enable dragonflies to find better solutions during the search process, it is necessary to guide the dragonflies to move in a better direction by updating the positions of "food" and "natural enemies". Specifically, it is necessary to calculate the fitness values of all dragonflies in the population, find the dragonfly with the smallest fitness value (i.e., the optimal solution), and set its position as the "food" position; at the same time, it is also necessary to find the dragonfly with the largest fitness value (i.e., the worst solution), and set its position as the "natural enemy" position; then, it is necessary to update the speed and position of all dragonflies to make them move towards the "food" position; use the food source position calculation formula to update the food source position, and use the natural enemy position calculation formula to update the natural enemy position;

[0023] Update the dragonfly's neighborhood radius based on the current optimal solution. The neighborhood radius can be calculated using the neighborhood radius calculation formula. Based on the position and orientation information of the dragonfly in the neighborhood, the separation, arrangement, and aggregation formulas in the original dragonfly algorithm are used to calculate the separation, arrangement, and aggregation degrees, respectively, and update the dragonfly's movement direction. Determine the stopping condition. If the stopping condition is not met, repeat the above steps.

[0024] Define a maximum number of iterations T max Or the maximum number of function evaluations FEs max , when the number of algorithm iterations exceeds T max Or the function evaluation times exceeds FEs max , the algorithm terminates; define a convergence threshold When the algorithm iterates, the gap between two adjacent optimal solutions is less than The algorithm can also be terminated early; the end condition of the dragonfly algorithm can be expressed by the end condition calculation formula; when the algorithm reaches the stop condition, the position of the dragonfly with the best fitness in the current population is output as the optimal pitch angle θ and heading angle γ;

[0025] Step 6: Calculate the inclination and settlement of transmission line towers

[0026] Given the heading angle α(0) and pitch angle β(0) of baseline b at initial installation, as well as the pitch angle θ and heading angle γ of baseline b obtained in step 5, the angle calculation formula is used to obtain the inclination angle Δθ and horizontal torsion angle Δγ of the tower; the inclination η of the transmission line tower can be calculated using the inclination calculation formula;

[0027] For the settlement monitoring of power towers, the line between antenna A on the tower and the Beidou base station is selected as the new baseline, and a mathematical model of the attitude angle is established. Then, the improved ADO algorithm is used to search and solve to obtain the new baseline length, pitch angle, and heading angle. The new baseline vector is superimposed on the spatial coordinates of the monitoring point to obtain the precise coordinates of the vector end, thereby monitoring the settlement degree of the power tower in real time.

[0028] The present invention is further configured such that the envelope calculation formula is: The spectrum line mean calculation formula is: The threshold factor calculation formula is:

[0029] The present invention is further configured such that the carrier phase double difference equation calculation formula is: Where |b| represents the baseline length between the two antennas, λ is the wavelength of the RF carrier signal, θ and γ are the quantities to be determined, representing the pitch angle and heading angle of the baseline b, respectively. and are the elevation angles of satellite i and satellite j to baseline b (antenna A or B), respectively. and denote the azimuths of satellite i and satellite j to baseline b (antenna A or B), respectively. is the phase double-difference integer ambiguity, is the observation noise.

[0030] The present invention is further configured such that the mathematical model calculation formula of the attitude angle is: The attitude angle mathematical model calculation formula is established based on the carrier phase double difference equation, and its purpose is to solve the pitch angle θ and heading angle γ of the baseline b, so that in That is, the target value of formula (5) is obtained as Fitness(θ,γ)→n(m-1).

[0031] The present invention is further configured such that the dragonfly position calculation formula is x i (t+1)=x i (t)+v x,i (t)

[0032] y i (t+1)=y i (t)+v y,i (t).

[0033] The present invention is further configured such that the dragonfly speed calculation formula is: Where w is the inertia weight, x best (t) and y best(t) represents the position of the dragonfly with the best fitness in the current population, x food (t) and y food (t) represent the location of the current optimal solution (i.e., “food”).

[0034] The present invention is further configured such that the food source location calculation formula is: in, represents the position of the i-th dragonfly in the j-th dimension, t represents the number of iterations, β is the adjustment factor, Indicates the position of the current optimal dragonfly in the jth dimension, is the disturbance factor, and N(0,1) represents the standard normal distribution.

[0035] The present invention is further configured such that the natural enemy position calculation formula is: Among them, γ is the regulating factor, Indicates the position of the current worst dragonfly in the jth dimension.

[0036] The present invention is further configured such that the neighborhood radius calculation formula is: Among them, R i represents the neighborhood radius of dragonfly i, R min and R max Respectively represent the minimum and maximum values of the neighborhood radius, fit i represents the fitness value of dragonfly i, f best and f worst Represent the current optimal fitness value and the worst fitness value respectively; the end condition calculation formula is if t>T max or FEs>FEs max or Where t represents the current iteration number, represents the function value of the optimal solution in the current iteration, Indicates the convergence threshold.

[0037] The present invention is further configured such that the angle calculation formula is: The inclination calculation formula is: η=tan(Δθ).

[0038] The beneficial effects of the present invention are:

[0039] This Beidou measurement method for monitoring the inclination and settlement of power towers based on the improved ADO transfers the solution process to the cloud, which can fully utilize the advantages of cloud computing and improve computing efficiency and resource utilization. At the same time, the adaptive dragonfly optimization algorithm can be adaptively adjusted according to different problems and environments, so as to better adapt to different solution requirements and have better flexibility and adaptability. It can effectively solve problems such as high computing resources and time costs, high load, etc., and achieve more efficient solution of attitude angle mathematical models, thereby improving detection accuracy and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a schematic diagram of the detection system structure of a method for monitoring the tilt and settlement of power towers using Beidou measurement based on improved ADO proposed by the present invention;

[0041] Figure 2 This is a schematic diagram of the process structure of the adaptive dragonfly optimization algorithm of the Beidou measurement power tower tilt and settlement monitoring method based on improved ADO proposed by the present invention. DETAILED DESCRIPTION

[0042] The technical solution of this patent is further described in detail below in conjunction with specific implementation methods.

[0043] The following describes in detail embodiments of the present invention, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0044] Reference Figure 1 and Figure 2 , a Beidou measurement method for monitoring the tilt and settlement of power towers based on improved ADO, comprising the following steps:

[0045] Step 1: Dual antennas receive the RF carrier signal from the Beidou satellite

[0046] Install dual antennas A and B on the top of the power tower to receive the RF carrier signals of n epochs from m BeiDou satellites (m, n ≥ 4), and the vector from the center of antenna A to the center of antenna B is called baseline b.

[0047] Step 2: Environmental interference detection

[0048] Set two empty sets T(n) and J(n), where T(n) is the non-interference frequency set and J(n) is the interference frequency set; perform fast Fourier transform (FFT) on the received signal envelope r(n) to obtain its frequency domain representation R(n), and use the envelope calculation formula to calculate the envelope A(n) of the received signal; sort A(n) in descending order, take the q spectral lines with smaller values, define them as the original non-interference frequency set T(n), and use the spectral line mean calculation formula to calculate its spectral line mean; set the false alarm probability of the interference detection algorithm to P f , calculate the threshold factor a according to the threshold factor calculation formula, and then calculate the original threshold value T1 = a × E{I(n)};

[0049] The envelope calculation formula is: The formula for calculating the spectral line mean is: The threshold factor calculation formula is:

[0050] Compare the above step T(n) with the original threshold value T1, move the elements greater than the threshold value to the interference frequency point set J(n), otherwise retain them in the T(n) set; recalculate the updated spectrum line mean E{I(n)} of T(n) according to step 2, and calculate the new threshold value T2 according to step 2 until all elements in T(n) are less than or equal to the threshold value; finally, obtain the non-interference frequency point set T(n) and the interference frequency point set J(n);

[0051] In the above steps, the false alarm probability refers to the probability of misjudging an interference signal when there is no interference signal. The threshold factor a is used to control the threshold value, P f The smaller the threshold, the higher the probability of detecting an interference signal. Each time the threshold is updated, the non-interference frequency set and the interference frequency set need to be re-detected until all non-interference frequencies are determined.

[0052] Step 3: Analyze the non-interference signal and transmit it to the cloud computing center through the encrypted gateway

[0053] Use the BeiDou board to analyze the non-interference signals received by the dual antennas A and B respectively, and obtain the carrier phase of n epochs of m BeiDou satellites Satellite elevation angle and satellite azimuth The parsed information such as carrier phase, satellite altitude angle, satellite azimuth angle, etc. is encrypted and transmitted to the cloud computing center through the gateway service for attitude solution;

[0054] Step 4: Establish a mathematical model of the tower attitude angle

[0055] The cloud computing center decrypts the encrypted information sent by the receiver for subsequent calculations; it calculates the carrier phase of the kth epoch received by antenna A and antenna B respectively. and And calculate the difference between them to get the carrier phase single difference Similarly, calculate the carrier phase of the kth epoch of the jth satellite received by antenna A and antenna B and And calculate the difference between them to get the carrier phase single difference

[0056] Using the carrier phase single difference calculated in the above steps, the carrier phase double difference equation can be calculated according to the carrier phase double difference equation calculation formula; the attitude angle mathematical model of the baseline b is established according to the attitude angle mathematical model calculation formula;

[0057] The calculation formula of the carrier phase double difference equation is: Where |b| represents the baseline length between the two antennas, λ is the wavelength of the RF carrier signal, θ and γ are the quantities to be determined, representing the pitch angle and heading angle of the baseline b, respectively. and are the elevation angles of satellite i and satellite j to baseline b (antenna A or B), respectively. and denote the azimuths of satellite i and satellite j to baseline b (antenna A or B), respectively. is the phase double-difference integer ambiguity, is the observation noise;

[0058] The mathematical model calculation formula of attitude angle is: The mathematical model calculation formula of attitude angle is established based on the carrier phase double difference equation, and its purpose is to solve the pitch angle θ and heading angle γ of baseline b so that in That is, the target value of formula (5) is obtained as Fitness(θ,γ)→n(m-1).

[0059] Step 5: Use the improved adaptive dragonfly optimization algorithm to solve the attitude angle mathematical model and obtain the attitude angle of baseline b

[0060] Randomly generate a group of dragonflies X i (i=1,2,…,n), and set their positions and velocities to random values; suppose the dimension of each dragonfly is 2-dimensional (x,y). In order to meet the constraints, ensure that the position of each dragonfly in the initial population meets the following conditions: the first dimension is the pitch angle θ, [y min ,y max ] is set to [-90°, +90°], the second dimension is the heading angle γ, [x min ,xmax ] is set to [0°, 360°]; the corresponding step vector ΔX is randomly initialized for each dragonfly i (i=1,2,…,n);

[0061] Calculate the objective function value of all dragonflies: For each dragonfly, calculate the fitness value of its current iteration position, that is, the objective function value; according to the position and speed of the dragonfly, calculate its next position; suppose t represents the number of current iterations, x i (t) and y i (t) represents the coordinate position of the i-th dragonfly, v x,i (t) and v y,i (t) represents the current speed of the dragonfly, and the next position of the dragonfly can be expressed using the dragonfly position calculation formula. When updating the speed, a random perturbation term is added, so that the dragonfly has a certain degree of randomness in the search process, which can better explore the solution space. Assuming that c1 and c2 are two constants, and r1 and r2 are two random numbers, the next speed of the dragonfly can be expressed using the dragonfly speed calculation formula.

[0062] The formula for calculating the dragonfly position is: The formula for calculating dragonfly speed is: Where w is the inertia weight, x best (t) and y best (t) represents the position of the dragonfly with the best fitness in the current population, x food (t) and y food (t) represent the location of the current optimal solution (i.e., “food”).

[0063] In order to enable dragonflies to find better solutions during the search process, it is necessary to guide the dragonflies to move in a better direction by updating the positions of "food" and "natural enemies". Specifically, it is necessary to calculate the fitness values of all dragonflies in the population, find the dragonfly with the smallest fitness value (i.e., the optimal solution), and set its position as the "food" position; at the same time, it is also necessary to find the dragonfly with the largest fitness value (i.e., the worst solution), and set its position as the "natural enemy" position; then, it is necessary to update the speed and position of all dragonflies to make them move towards the "food" position; use the food source position calculation formula to update the food source position, and use the natural enemy position calculation formula to update the natural enemy position;

[0064] The formula for calculating the food source location is: in, represents the position of the i-th dragonfly in the j-th dimension, t represents the number of iterations, β is the adjustment factor, Indicates the position of the current optimal dragonfly in the jth dimension, is the disturbance factor, N(0,1) represents the standard normal distribution; the calculation formula for the natural enemy position is Among them, γ is the regulating factor, Indicates the position of the current worst dragonfly in the jth dimension.

[0065] The calculation formulas for the food source position and the natural enemy position are based on the basic principle of the original dragonfly algorithm, that is, adjusting the position of the dragonfly by random walk and being affected by the optimal and worst dragonfly positions to find the optimal solution.

[0066] Update the dragonfly's neighborhood radius based on the current optimal solution. The neighborhood radius can be calculated using the neighborhood radius calculation formula. Based on the position and orientation information of the dragonfly in the neighborhood, the separation, arrangement, and aggregation formulas in the original dragonfly algorithm are used to calculate the separation, arrangement, and aggregation degrees, respectively, and update the dragonfly's movement direction. Determine the stopping condition. If the stopping condition is not met, repeat the above steps.

[0067] The calculation formula for the neighborhood radius is: Among them, R i represents the neighborhood radius of dragonfly i, R min and R max Respectively represent the minimum and maximum values of the neighborhood radius, fit i represents the fitness value of dragonfly i, f best and f worst Represent the current optimal fitness value and the worst fitness value respectively; the end condition calculation formula is if t>T max or FEs>FEs max or Where t represents the current iteration number, represents the function value of the optimal solution in the current iteration, Indicates the convergence threshold.

[0068] The neighborhood radius calculation formula calculates the neighborhood radius of each dragonfly based on the fitness values of the current optimal and worst solutions. A larger neighborhood radius allows the dragonfly to perceive more neighboring information, thereby increasing its exploration range. A smaller neighborhood radius emphasizes a more localized search.

[0069] Define a maximum number of iterations T max Or the maximum number of function evaluations FEs max , when the number of algorithm iterations exceeds T max Or the function evaluation times exceeds FEs max , the algorithm terminates; define a convergence threshold When the algorithm iterates, the gap between two adjacent optimal solutions is less than The algorithm can also be terminated early; the end condition of the dragonfly algorithm can be expressed by the end condition calculation formula; when the algorithm reaches the stop condition, the position of the dragonfly with the best fitness in the current population is output as the optimal pitch angle θ and heading angle γ;

[0070] Step 6: Calculate the inclination and settlement of transmission line towers

[0071] Given the heading angle α(0) and pitch angle β(0) of baseline b at initial installation, as well as the pitch angle θ and heading angle γ of baseline b obtained in step 5, the angle calculation formula is used to obtain the inclination angle Δθ and horizontal torsion angle Δγ of the tower; the inclination η of the transmission line tower can be calculated using the inclination calculation formula.

[0072] The angle calculation formula is: The formula for calculating the inclination is: η = tan(Δθ).

[0073] For the settlement monitoring of power towers, the line between antenna A on the tower and the Beidou base station is selected as the new baseline, and a mathematical model of the attitude angle is established. Then, the improved ADO algorithm is used to search and solve to obtain the new baseline length, pitch angle, and heading angle. The new baseline vector is superimposed on the spatial coordinates of the monitoring point to obtain the precise coordinates of the vector end, thereby monitoring the settlement degree of the power tower in real time.

[0074] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for monitoring the tilt and settlement of power towers using Beidou measurement based on improved ADO, characterized in that: The following steps are involved: Step 1: Dual antennas receive the RF carrier signal from the Beidou satellite Install dual antennas A and B on the top of the power tower to receive BeiDou satellites epochs of radio frequency carrier signal, The vector from the center of antenna A to the center of antenna B is called the baseline ; Step 2: Environmental interference detection Set two empty sets and ,in, is a set of non-interference frequencies, Is the set of interference frequencies; the received signal envelope Perform fast Fourier transform to obtain its frequency domain representation , use the envelope calculation formula to find the envelope of the received signal ;right Sort in descending order, taking the smaller value spectral lines, defined as the set of original non-interfering frequency points , and use the spectrum line mean calculation formula to calculate the spectrum line mean; set the false alarm probability of the interference detection algorithm to , calculate the threshold factor according to the threshold factor calculation formula , and then calculate the original threshold value ; Compare the above steps With the original threshold , move the elements greater than the threshold value to the interference frequency point set Otherwise, keep In the collection; after recalculating the update according to step 2 The spectral mean , and calculate the new threshold value according to step 2 ,until All elements in are less than or equal to the threshold value; finally, the non-interference frequency point set is obtained and interference frequency set ; Step 3: Analyze the non-interference signal and transmit it to the cloud computing center through the encrypted gateway The BeiDou board is used to analyze the non-interference signals received by the dual antennas A and B respectively, and the BeiDou satellites Carrier phase of epochs , satellite elevation angle and satellite azimuth The parsed carrier phase, satellite altitude angle, and satellite azimuth information are encrypted and transmitted to the cloud computing center through the gateway service for attitude calculation; Step 4: Establish a mathematical model of the tower attitude angle The cloud computing center decrypts the encrypted information sent by the receiver; the computing antenna A and antenna B receive the first Satellite Carrier phase of epochs and , and calculate the difference between them to get the carrier phase single difference Similarly, calculate the first Satellite Carrier phase of epochs and , and calculate the difference between them to get the carrier phase single difference ; Using the single difference of the carrier phase calculated in the above steps, the carrier phase double difference equation is calculated according to the carrier phase double difference equation calculation formula; the baseline is established according to the attitude angle mathematical model calculation formula Mathematical model of attitude angle; Step 5: Use the improved adaptive dragonfly optimization algorithm to solve the attitude angle mathematical model and obtain the baseline attitude angle Randomly generate a group of dragonflies , and set their positions and velocities to random values; set the dimension of each dragonfly to 2 dimensions , in order to meet the constraints, ensure that the position of each dragonfly in the initial population meets the following requirements: The first dimension is the pitch angle , [ , ] is set to [-90°, +90°], the second dimension is the heading angle , [ , ] is set to [0°, 360°]; the corresponding step vector is randomly initialized for each dragonfly ; Calculate the objective function value of all dragonflies: For each dragonfly, calculate the fitness value of its current iteration position, that is, the objective function value; according to the position and speed of the dragonfly, calculate its next position; assuming Indicates the number of current iterations, and Indicates the The coordinate position of the dragonfly, and Represents the current speed of the dragonfly, and the next position of the dragonfly is expressed using the dragonfly position calculation formula; add a random perturbation term when updating the speed: Assume and Represent two constants, and Represent two random numbers respectively, and the speed of the dragonfly's next step is expressed by the dragonfly speed calculation formula; To help dragonflies find better solutions during their search, it's necessary to guide them toward a more optimal direction by updating the positions of "food" and "natural enemies." Specifically, the fitness values of all dragonflies in the population need to be calculated, and the dragonfly with the smallest fitness value, the optimal solution, is found, and its position is set to the "food" location. At the same time, the dragonfly with the largest fitness value, the worst solution, is found, and its position is set to the "natural enemy" location. Then, the speed and position of all dragonflies need to be updated to steer them toward the "food" location. The food source position is updated using the food source position calculation formula, and the natural enemy position is updated using the natural enemy position calculation formula. Update the dragonfly's neighborhood radius based on the current optimal solution. The neighborhood radius is calculated using the neighborhood radius calculation formula. Based on the position and orientation information of the dragonfly in the neighborhood, the separation, arrangement, and aggregation formulas in the original dragonfly algorithm are used to calculate the separation, arrangement, and aggregation degrees, respectively, and update the dragonfly's movement direction. Determine the stopping condition. If the stopping condition is not met, repeat the above steps. Define a maximum number of iterations Or the maximum number of function evaluations , when the number of algorithm iterations exceeds or the function is evaluated more than , the algorithm terminates; define a convergence threshold , when the gap between two adjacent optimal solutions is less than , the algorithm terminates early; the end condition of the dragonfly algorithm is expressed by the end condition calculation formula; when the algorithm reaches the stop condition, the position of the dragonfly with the best fitness in the current population is output as the optimal pitch angle and heading angle ; Step 6: Calculate the inclination and settlement of transmission line towers Known baseline Heading angle at initial installation and pitch angle , and the baseline obtained in step 5 Pitch angle and heading angle , use the angle calculation formula to obtain the inclination angle of the tower and horizontal torsion angle ; The inclination of the transmission line tower is calculated by the inclination calculation formula ; For the settlement monitoring of power towers, the line between antenna A on the tower and the Beidou base station is selected as the new baseline, and a mathematical model of the attitude angle is established. Then, the improved ADO algorithm is used to search and solve to obtain the new baseline length, pitch angle, and heading angle. The new baseline vector is superimposed on the spatial coordinates of the monitoring point to obtain the precise coordinates of the vector end, thereby monitoring the settlement degree of the power tower in real time.

2. The method for monitoring the tilt and settlement of power towers using Beidou measurement based on improved ADO according to claim 1, characterized in that: The envelope calculation formula is: , the spectrum line mean calculation formula is: , the threshold factor calculation formula is: .

3. The method for monitoring the tilt and settlement of power towers using Beidou measurement based on improved ADO according to claim 1, characterized in that: The carrier phase double difference equation calculation formula is: ,in, represents the baseline length between the two antennas, is the wavelength of the RF carrier signal, and are the quantities to be determined, representing the baselines The pitch and heading angles, and Represents satellites and satellite Baseline The altitude angle, and Represents satellites and satellite Baseline The azimuth of is the phase double-difference integer ambiguity, is the observation noise.

4. The method for monitoring the tilt and settlement of power towers using Beidou measurement based on improved ADO according to claim 3, characterized in that: The mathematical model calculation formula of the attitude angle is: The mathematical model calculation formula of the attitude angle is established based on the carrier phase double difference equation, and its purpose is to solve the baseline Pitch angle and heading angle , making in , that is, the target value .

5. The method for monitoring the tilt and settlement of power towers using Beidou measurement based on improved ADO according to claim 1, characterized in that: The dragonfly position calculation formula is: .

6. The method for monitoring the tilt and settlement of power towers using Beidou measurement based on improved ADO according to claim 5, characterized in that: The dragonfly speed calculation formula is: ,in, is the inertia weight, and Respectively represent the position of the dragonfly with the best fitness in the current population, and They represent the positions of the current optimal solutions respectively.

7. The method for monitoring the tilt and settlement of power towers using Beidou measurement based on improved ADO according to claim 1, characterized in that: The food source location calculation formula is: ,in, Indicates the A dragonfly in the Position in dimensions, represents the number of iterations, is the regulating factor, Indicates that the current optimal dragonfly is in Position in dimensions, is the disturbance factor, represents the standard normal distribution.

8. The method for monitoring the tilt and settlement of power towers using Beidou measurement based on improved ADO according to claim 7, characterized in that: The calculation formula for the natural enemy position is: ,in, is the regulating factor, The worst dragonfly is currently Position in one dimension.

9. The method for monitoring the tilt and settlement of power towers using Beidou measurement based on improved ADO according to claim 1, characterized in that: The neighborhood radius calculation formula is: ,in, Represents dragonfly The neighborhood radius of and Represent the minimum and maximum values of the neighborhood radius, Represents dragonfly The fitness value of and Represent the current optimal fitness value and the worst fitness value respectively; the end condition calculation formula is if or or ,in, Indicates the current iteration number, represents the function value of the optimal solution in the current iteration, Indicates the convergence threshold.

10. The method for monitoring the tilt and settlement of power towers using Beidou measurement based on improved ADO according to claim 9, characterized in that: The angle calculation formula is: The inclination calculation formula is: .

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