Transmission conductor three-dimensional attitude estimation method, system and device based on RTK and inertia measurement coupling and medium

Abstract

The invention discloses a transmission conductor three-dimensional attitude estimation method, system and equipment based on RTK and inertial measurement coupling and a medium, and belongs to the technical field of three-dimensional state monitoring, and the method comprises the following steps: fusing RTK absolute positioning and IMU inertial measurement data, and realizing accurate estimation of the transmission conductor three-dimensional attitude by using extended Kalman filtering; and abnormal identification and risk early warning are carried out based on an attitude calculation result, and finally real-time monitoring and intelligent warning of the state of the power transmission line are realized through a three-dimensional visual platform. According to the method, RTK and IMU data are fused, accurate sensing and continuous tracking of the three-dimensional attitude of the power transmission line are achieved through extended Kalman filtering, the defect that a single technology is insufficient in reliability in a complex environment is overcome, and finally a complete monitoring closed loop of the safety state of the power transmission line is achieved through intelligent early warning and three-dimensional visualization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of three-dimensional state monitoring, in particular to a power transmission conductor three-dimensional posture estimation method, system, device and medium based on RTK and inertial measurement coupling. BACKGROUND

[0002] In the current field of three-dimensional state monitoring of power transmission lines, the commonly used technologies mainly include GNSS (Global Navigation Satellite System, especially RTK real-time dynamic positioning) based positioning technology, video image based visual recognition technology and inertial measurement unit (IMU) based inertial navigation technology. Although these methods have shown certain application value under certain conditions, there are still obvious deficiencies in complex power transmission environments.

[0003] For example, RTK technology can provide centimeter-level static positioning accuracy in open areas, so it is widely used in surveying and mapping and power transmission line tower positioning. However, when applied to overhead conductors, due to the influence of wind load, icing, terrain obstruction and multipath effect, it is easy to cause unstable calculation and frequent loss of lock, resulting in a decrease in posture monitoring accuracy and reliability.

[0004] On the other hand, IMU technology has high-frequency dynamic response capability and can maintain the continuity of posture calculation for a short time when GNSS signal is interrupted. However, due to its inherent zero drift and noise accumulation problems, the output error will grow rapidly with time, and the accuracy cannot be guaranteed for a long time without external correction, making it difficult to meet the high-precision monitoring requirements of power conductors.

[0005] Some studies attempt to improve system robustness by fusing GNSS and IMU, but existing methods are mostly oriented towards rigid platforms (such as vehicles, robots), lacking dynamic modeling and constraint mechanisms for flexible structures of power transmission conductors. In addition, such methods often ignore the differences and asynchronous problems between RTK and IMU in sampling frequency, lack high-precision time registration and multi-source redundant observation mechanisms, making it difficult to achieve stable and continuous three-dimensional posture estimation in high dynamic and strong interference environments.

[0006] In summary, the existing technology generally has the problems of insufficient RTK calculation stability, serious long-term drift of IMU, single fusion strategy and lack of flexible body modeling when facing complex working conditions of power transmission conductors, which cannot effectively realize dynamic high-precision monitoring of key parameters such as conductor sag, wind deflection and icing deformation. This situation poses a great challenge to the safe operation and intelligent operation of the power system, and also provides a real demand and technical space for the proposal of new methods. SUMMARY

[0007] In view of the above problems, the present application provides a power transmission conductor three-dimensional posture estimation method, system, device and medium based on RTK and inertial measurement coupling.

[0008] Therefore, the technical problem solved by the present application is that the existing power transmission conductor three-dimensional state monitoring technology has insufficient stability due to the loss of lock of RTK in complex environments, and the long-term precision of IMU cannot be guaranteed due to the accumulation of inherent errors, and the existing GNSS / IMU fusion method is designed for rigid body motion, lacks a dynamic model for the flexible vibration characteristics of the conductor and a high-precision time synchronization mechanism, thereby making it difficult to realize continuous, stable and high-precision real-time monitoring and early warning of key safety parameters such as conductor sag, wind deflection and ice deformation under complex working conditions such as strong wind and shielding.

[0009] To solve the above technical problems, the present application provides the following technical solutions: a power transmission conductor three-dimensional attitude estimation method based on coupling of RTK and inertial measurement, comprising, A time-synchronized multi-sensor monitoring node is constructed, and time synchronization initialization of the positioning and inertial measurement unit is completed through a time reference signal; the positioning and inertial measurement unit collects motion inertia parameters of the conductor at a first frequency and calculates short-time attitude changes; the positioning and inertial measurement unit receives satellite signals at a second frequency and obtains absolute positioning information of the conductor in combination with differential base station solution; the absolute positioning information is preprocessed; under the extended Kalman filter framework, the short-time attitude changes are used to drive state prediction, and the preprocessed absolute positioning information is used to trigger observation update, data fusion is performed to obtain conductor attitude and position information; the conductor attitude and position information are converted into Euler angles and three-dimensional space coordinates of the conductor, and the geometric state of the conductor is calculated; the geometric state of the conductor is compared with historical reference data to perform attitude anomaly identification and risk warning; the results of attitude anomaly identification and risk warning are uploaded to the background monitoring platform through wireless communication and are displayed in three dimensions.

[0010] As a preferred scheme of the power transmission conductor three-dimensional attitude estimation method based on coupling of RTK and inertial measurement, wherein: the first frequency at which the motion inertia parameters of the conductor are collected and the short-time attitude changes are calculated comprises, The positioning and inertial measurement unit compensates for errors of the collected motion inertia parameters.

[0011] Based on the error-compensated motion inertia parameters, attitude update is performed through attitude solution, and real-time attitude of the conductor is calculated by integration.

[0012] The current calculated attitude is compared with the attitude at the previous moment, and the attitude change is output.

[0013] As a preferred scheme of the power transmission conductor three-dimensional attitude estimation method based on coupling of RTK and inertial measurement, wherein: the second frequency at which the satellite signals are received and the absolute positioning information of the conductor is solved in combination with the differential base station comprises, The positioning and inertial measurement unit performs real-time differential processing on the satellite signals received by itself and the raw data of the differential base station.

[0014] On the basis of the differential processing, the integer ambiguity of the carrier phase is resolved.

[0015] After the ambiguity is fixed, the absolute positioning information of the conductor is resolved and output.

[0016] As a preferred scheme of the power transmission conductor three-dimensional attitude estimation method based on the coupling of RTK and inertial measurement, wherein the preprocessing of the absolute positioning information comprises, According to the preset quality judgment criterion, the absolute positioning information is subjected to validity test.

[0017] The screened effective absolute positioning information is subjected to time synchronization with the master clock.

[0018] The screened and aligned absolute positioning information is subjected to noise suppression processing, and is adapted to the target data frequency according to the data fusion requirement.

[0019] As a preferred scheme of the power transmission conductor three-dimensional attitude estimation method based on the coupling of RTK and inertial measurement, wherein the data fusion comprises, In each high-frequency processing period, the current state vector and uncertainty of the conductor are predicted by a dynamic model using the short-time attitude change.

[0020] When the preprocessed absolute positioning information is received, it is compared with the predicted state vector as an observation value, and the difference between them is calculated.

[0021] According to the uncertainty of the state vector and the observation value, the Kalman gain is calculated, and the difference between the state vector and the observation value is weighted and fused using the Kalman gain to obtain the optimal state estimation of the current time state vector.

[0022] The fused optimal state estimation is output as the initial value of the next time state prediction to complete the filtering iteration.

[0023] The beneficial effects of the preferred technical solution are that the high-frequency short-time attitude change and the low-frequency absolute positioning information are cooperatively fused in the extended Kalman filtering framework, the continuous state prediction is driven by the inertial data, and the accurate observation update is triggered by the absolute positioning, so that the high-frequency dynamic response capability of the IMU is fully utilized to capture the instantaneous motion details of the conductor, and the absolute accuracy of the RTK is used to periodically correct the cumulative drift of the IMU, thereby realizing complementary advantages at the algorithm level and effectively solving the core contradiction that the monitoring accuracy and continuity of a single sensor cannot be compatible in a complex power transmission environment.

[0024] As a preferred scheme of the power transmission conductor three-dimensional attitude estimation method based on coupling of RTK and inertial measurement, the method comprises the steps of: Converting the attitude information obtained through data fusion into Euler angles of pitch angle, roll angle and yaw angle.

[0025] Based on the converted three-dimensional space coordinates, the relative spatial relationship between the conductor suspension point and the monitoring point is determined in the specified reference coordinate system.

[0026] According to the catenary model or the spatial geometric relationship of the conductor, the coordinates and the attitude of the monitoring point are used to calculate the key geometric parameters representing the spatial form.

[0027] According to the data of a series of monitoring points on the conductor, the overall spatial form and the dynamic change curve of the conductor are reconstructed through curve fitting.

[0028] The beneficial effects of the preferred technical solution are that the abstract attitude and position information after fusion are converted into intuitive Euler angles and three-dimensional coordinates, and the key geometric parameters are calculated based on the conductor catenary model and the spatial geometric relationship, so as to realize accurate mapping from sensor data to conductor engineering state parameters; further, through curve fitting of the data of multiple monitoring points, the overall spatial form and the dynamic change curve of the conductor are reconstructed, which not only improves the calculation accuracy and intuitiveness of the sag, wind deviation and other safety core parameters, but also provides a reliable data basis for subsequent attitude anomaly identification, load analysis and safety warning.

[0029] As a preferred scheme of the power transmission conductor three-dimensional attitude estimation method based on coupling of RTK and inertial measurement, the method comprises the steps of: The real-time calculated key geometric parameters are compared with the preset historical safe operation reference value to calculate the deviation amount.

[0030] Based on the preset determination rule, it is identified whether the deviation amount exceeds the static threshold.

[0031] According to the result of anomaly identification, the current risk level is evaluated in combination with the environmental parameters and the abnormal duration.

[0032] When the risk level exceeds the preset threshold, the risk warning information of the corresponding level is generated and triggered.

[0033] The beneficial effects of the preferred technical solution are that by systematically comparing the real-time calculated geometric parameters with the historical safety benchmark, and based on the multi-level decision rule, the precise identification from the static threshold overrun to the dynamic trend anomaly is realized, effectively improving the timeliness and accuracy of hidden danger discovery; further, the comprehensive risk assessment and graded warning are carried out in combination with the environmental parameters and the abnormal duration, not only overcoming the problems of poor adaptability and high false alarm rate of the traditional single threshold criterion, but also realizing the leap from passive monitoring to active warning, from qualitative judgment to quantitative evaluation, enhancing the intelligent level and risk pre-control ability of the transmission line operation and maintenance management.

[0034] The application provides a transmission line three-dimensional posture estimation system based on coupling of RTK and inertial measurement.

[0035] To solve the above technical problems, the application provides the following technical scheme: a transmission line three-dimensional posture estimation system based on coupling of RTK and inertial measurement, comprising: a synchronous data acquisition module, a multi-source data fusion processing module, a line state calculation module, an intelligent early warning analysis module, and a data communication and visualization module.

[0036] The synchronous data acquisition module constructs a time-synchronized multi-sensor monitoring node, and completes the time synchronization initialization of the positioning and inertial measurement unit through a time reference signal.

[0037] The multi-source data fusion processing module performs data fusion to obtain the line posture and position information by driving state prediction with short-time posture changes and triggering observation update with preprocessed absolute positioning information under the extended Kalman filter framework.

[0038] The line state calculation module converts the line posture and position information into the Euler angle and three-dimensional space coordinates of the line, and calculates the geometric state of the line.

[0039] The intelligent early warning analysis module compares the geometric state of the line with the historical benchmark data, performs posture anomaly identification and risk warning.

[0040] The data communication and visualization module uploads the results of posture anomaly identification and risk warning to the background monitoring platform through wireless communication, and performs three-dimensional visualization display.

[0041] The application provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements steps of the three-dimensional posture estimation method for power transmission conductor based on coupling of RTK and inertial measurement when executing the computer program.

[0042] The application provides a computer readable storage medium, which stores a computer program, wherein the computer program implements steps of the three-dimensional posture estimation method for power transmission conductor based on coupling of RTK and inertial measurement when executed by a processor.

[0043] The application has the beneficial effects that: the application constructs a time-synchronized multi-sensor monitoring node, and deeply fuses RTK absolute positioning information and IMU motion inertia parameters based on an extended Kalman filtering framework, so that high-precision and high-frequency estimation of the three-dimensional posture of the power transmission conductor is realized, and the inherent defects that the traditional RTK technology is prone to loss of lock in a complex environment and the IMU technology cannot be reliably accumulated for a long time due to drift are effectively solved. In a high-frequency dynamic scene such as strong wind vibration, the system can use the IMU to maintain continuous tracking, and still has robustness for short-term autonomous operation when the RTK signal is interrupted due to shielding, finally outputs key geometric state parameters such as conductor sag and wind deflection, and realizes a complete monitoring closed loop from data acquisition, abnormal identification to risk warning and three-dimensional visualization through real-time comparison with historical benchmarks. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can also be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0045] Figure 1 A three-dimensional posture estimation method for power transmission conductor based on coupling of RTK and inertial measurement provided for an embodiment of the application has a general flowchart.

[0046] Figure 2 A three-dimensional posture estimation system for power transmission conductor based on coupling of RTK and inertial measurement provided for an embodiment of the application has a general framework diagram. DETAILED DESCRIPTION

[0047] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0048] Embodiment 1, refer to Figure 1 For an embodiment of the present application, the embodiment provides a power transmission conductor three-dimensional attitude estimation method based on coupling of RTK and inertial measurement, comprising: S1, a time-synchronized multi-sensor monitoring node is constructed, and time synchronization initialization of the positioning and inertial measurement unit is completed through a time reference signal.

[0049] S2, the positioning and inertial measurement unit collects the motion inertia parameters of the conductor at a first frequency, and calculates the short-time attitude change.

[0050] S3, the positioning and inertial measurement unit receives satellite signals at a second frequency, and obtains the absolute positioning information of the conductor in combination with differential base station solution.

[0051] S4, the absolute positioning information is preprocessed.

[0052] S5, under the extended Kalman filtering framework, the short-time attitude change is used to drive state prediction, and the preprocessed absolute positioning information is used to trigger observation update, data fusion is performed to obtain the conductor attitude and position information.

[0053] S6, the conductor attitude and position information are converted into Euler angles and three-dimensional space coordinates of the conductor, and the geometric state of the conductor is calculated.

[0054] S7, the geometric state of the conductor is compared with historical reference data to perform attitude anomaly identification and risk warning.

[0055] S8, the results of attitude anomaly identification and risk warning are uploaded to the background monitoring platform through wireless communication, and three-dimensional visualization display is performed.

[0056] The application realizes high-precision and high-frequency continuous estimation of the three-dimensional posture of the power transmission conductor by constructing a time-synchronized multi-sensor monitoring node and deeply fusing high-frequency inertial measurement and low-frequency absolute positioning data based on an extended Kalman filtering framework, effectively overcoming the inherent defects of single RTK technology in complex environments and IMU technology in long-term reliable accumulation due to drift. This method not only maintains stable monitoring in dynamic scenes such as strong wind vibration, but also maintains short-term robust operation through inertial prediction during signal shielding, and finally solves key geometric parameters such as sag and wind deflection and compares historical benchmarks to complete the whole process of data acquisition, abnormal identification, risk warning and three-dimensional visualization. Embodiment 2, which is an embodiment of the application, provides a three-dimensional posture estimation method for a power transmission conductor based on coupling of RTK and inertial measurement based on the previous embodiment, comprising: S1 constructs a time-synchronized multi-sensor monitoring node, and completes time synchronization initialization of the positioning and inertial measurement unit through a time reference signal. The multi-sensor monitoring node is RTK, IMU and MCU terminal, the time reference signal is PPS, the unified time reference is completed by accessing PPS hardware second pulse, the time stamp is synchronized and the zero offset initial calibration is completed.

[0057] Further, S2 includes collecting motion inertia parameters of the conductor at a first frequency, and calculating short-time attitude changes, including steps A1-A3: A1, the positioning and inertial measurement unit compensates for errors in the collected motion inertia parameters.

[0058] A2, based on the error-compensated motion inertia parameters, the attitude is updated through attitude solving, and the real-time attitude of the conductor is calculated by integration.

[0059] A3, compare the current calculated attitude with the attitude at the previous moment, and output the attitude change.

[0060] The first frequency refers to the original data sampling frequency of the inertial measurement unit (IMU), which is usually between 100 Hz and 1000 Hz. This high frequency range is selected to accurately capture the high-frequency dynamic response of the conductor caused by wind-induced vibration, dancing, etc., providing high-time resolution attitude change information for subsequent data fusion. Motion inertia parameters are acceleration and angular velocity information.

[0061] In the embodiments of the present application, the attitude solving in A2 is a gradient descent method based on quaternion. This method compares the specific force information measured by the accelerometer with the estimated value of the gravity vector in the body coordinate system, and uses the error to correct the attitude obtained by gyro integration. Specifically, this method is implemented by the following steps: using the angular velocity measured by the gyroscope, updating the quaternion by the first-order Runge-Kutta method to complete the attitude prediction. Calculate the error vector of the theoretical gravity vector under the current attitude and the measured vector of the accelerometer. Convert the above error vector into a quaternion correction amount by the gradient descent algorithm. Feedback the correction amount to the attitude prediction result of gyro integration, and finally output a stable attitude optimized by the acceleration information and capable of effectively suppressing the long-term drift of the gyroscope.

[0062] Specifically, the observation vector .

[0063] Error: .

[0064] wherein, is the observation of RTK position / velocity; is the observation matrix for extracting the predicted position and velocity from the state; is the prior state of step 2; is the observation error.

[0065] In an alternative embodiment, the attitude solving can be a vector observation method based on complementary filtering. This method fuses the attitude obtained by gyro integration with the attitude solved by the observation vectors of the accelerometer and the magnetometer. The specific steps include: respectively integrating the attitude prediction by the gyro angular velocity, and solving the attitude observation by the TRIAD or QUEST algorithm from the accelerometer and magnetometer data. Design a complementary filter to weight and fuse the high-frequency gyro attitude signal and the low-frequency vector observation attitude signal. By adjusting the filter cutoff frequency, a balance is achieved between dynamic response and anti-interference ability, and the final attitude is output.

[0066] In another alternative embodiment, the attitude solving can also be an equivalent rotation vector method. This method is specifically used to handle the non-commutative error (i.e. conical error) in high dynamic environment. The specific steps are: sampling the angular increment output by the gyroscope within one attitude update period. Using the angular increments of multiple sub-periods, an equivalent rotation vector is constructed by an algorithm, which can more accurately describe the spatial finite rotation of the carrier in a limited time. Convert the equivalent rotation vector into a quaternion increment, and update the current attitude quaternion.

[0067] In the embodiments of the present application, the differential base station solution in S3 is a gradient descent method based on quaternion. By taking the specific force information measured by the accelerometer as the observation vector, and comparing it with the estimated value of the gravity vector in the body coordinate system, the error is used to correct the attitude obtained by gyroscopic integration. Specifically, the method is implemented by the following steps: The angular velocity measured by the gyroscope is used to update the quaternion by the first-order Runge-Kutta method, and the attitude prediction is completed.

[0068] The error vector of the theoretical gravity vector under the current attitude and the measured vector of the accelerometer is calculated.

[0069] The error vector is converted into a quaternion correction amount by the gradient descent algorithm.

[0070] The correction amount is fed back to the attitude prediction result of gyroscopic integration, and finally an optimized stable attitude is output, which can effectively suppress the long-term drift of the gyroscope.

[0071] Specifically, the EKF update is performed: Kalman gain ; state update .

[0072] Covariance update .

[0073] where, is the prior covariance, is the observation noise covariance, is the Kalman gain, is the posterior covariance.

[0074] In an alternative embodiment, the differential base station solution can be a vector observation method based on complementary filtering. This method fuses the attitude obtained by gyroscopic integration with the attitude calculated from the accelerometer and magnetometer observation vectors. The specific steps include: The attitude prediction is obtained by integrating the angular velocity of the gyroscope, and the attitude observation is calculated from the accelerometer and magnetometer data by the TRIAD or QUEST algorithm.

[0075] A complementary filter is designed to weight and fuse the high-frequency gyro attitude signal and the low-frequency vector observation attitude signal.

[0076] By adjusting the filter cutoff frequency, a balance is achieved between dynamic response and anti-interference capability, and the final attitude is output.

[0077] In another alternative embodiment, the differential base station solution can also be an equivalent rotation vector method. This method is specifically used to handle the non-commutative error (i.e. conical error) in high dynamic environments. The specific steps are: During one attitude update cycle, the angular increment output by the gyroscope is sampled.

[0078] Using the angular increments of multiple sub-cycles, an equivalent rotation vector is constructed by algorithm, which can more accurately describe the spatial finite rotation of the carrier in a limited time.

[0079] The equivalent rotation vector is converted into a quaternion increment, and the current attitude quaternion is updated.

[0080] Further, the receiving satellite signals at the second frequency in S3 and combining the differential base station to solve the absolute positioning information of the conductor includes steps B1-B3: B1, the positioning and inertial measurement unit real-time differential processing of the satellite signals received by itself and the original data of the differential base station.

[0081] B2, on the basis of differential processing, the carrier phase integer ambiguity is solved.

[0082] B3, after ambiguity fixing, the absolute positioning information of the conductor is solved and output.

[0083] Further, the second frequency specifically refers to the update frequency of RTK positioning solution. It is a systematic working frequency, and its setting is far from arbitrary choice, but a comprehensive decision based on technical limitations, power consumption considerations, and coordination with the IMU working frequency (first frequency).

[0084] The core of RTK to achieve centimeter-level accuracy lies in the fixing of carrier phase integer ambiguity (i.e. step B2). This is a complex search and optimization calculation process that requires continuous tracking of stable signals from multiple satellites. The higher the frequency, the shorter the time left for each solution, and in complex environments (such as near power lines with obstructions and multipath effects), the failure rate of ambiguity fixing will rise sharply, leading to frequent jumps between "float solution" (decimeter-level error) and "fixed solution" (centimeter-level accuracy) in positioning results, and a decline in data quality.

[0085] Therefore, the second frequency is usually a relatively low frequency, generally ranging from 1Hz to 10Hz. This provides enough time for the RTK receiver to receive satellite data, communicate with the reference station, and robustly complete ambiguity resolution, ensuring that each output absolute positioning information is a high-confidence "fixed solution".

[0086] The absolute positioning information is the spatial coordinate and velocity information of the guide line.

[0087] RTK solves the absolute position and velocity to form a low-frequency absolute reference: get .

[0088] where, a position vector output by the RTK, a velocity vector output by the RTK.

[0089] interpolating the RTK observations to the IMU time to achieve asynchronous alignment: wherein, an IMU timestamp, a neighboring RTK timestamp.

[0090] In the embodiments of the present application, the resolution of the carrier phase integer ambiguity in B2 adopts a fixed solution search strategy based on the LAMBDA algorithm. Specifically, it includes the following steps: first, using the differential pseudo-range and carrier phase observations, a floating solution containing coordinates, ambiguity, etc. and its covariance matrix are obtained by least squares method or Kalman filtering. The covariance matrix of the ambiguity is processed by LAMBDA algorithm to reduce the search space and improve the search efficiency. In the transformed space, the optimal integer ambiguity vector combination is searched according to the (weighted) least squares criterion. The optimal solution and the suboptimal solution obtained by searching are statistically tested. If the ratio is greater than the preset threshold (usually 2.0-3.0), it is confirmed that the fixing is successful, and the integer ambiguity is substituted back to obtain a centimeter-level fixed solution.

[0091] In an alternative embodiment, the resolution of the carrier phase integer ambiguity can be a wide / narrow lane combination technique. This method uses dual-frequency observations to form a wide lane combination with longer wavelength, which is easier to fix the ambiguity. Specifically, it includes the following steps: first, a wide lane combination is formed using dual-frequency observations. Since its wavelength is long, the ambiguity is easy to solve or quickly fix by pseudo-range. On the basis of fixing the wide lane ambiguity, the ambiguity of the ionosphere-free combination or the L1 / L2 original frequency is further solved.

[0092] In another alternative embodiment, the resolution of the carrier phase integer ambiguity can also be a partial ambiguity fixing strategy. Specifically, when it is not possible to fix the ambiguity of all satellites, the following steps are taken to seek a usable solution: according to the satellite elevation angle, signal-to-noise ratio, error size and other indicators, the visible satellites are sorted and screened. Select a subset with small ambiguity variance and good geometric structure, and try to fix the ambiguity of the subset. The fixed subset is verified for effectiveness. Even if only part of the satellites are successfully fixed, the positioning accuracy and reliability can be significantly improved.

[0093] Further, the preprocessing of the absolute positioning information in S4 includes the following steps C1-C3: C1, according to a preset quality determination criterion, the absolute positioning information is tested for effectiveness.

[0094] Further, the quality determination criterion includes: Solution type determination: Check if the RTK positioning solution is a "fixed solution". Only when the solution type is "fixed solution", it is considered to have centimeter-level accuracy, and "float solution" or "single point solution" is considered as invalid or low-precision data and is rejected.

[0095] Precision index threshold determination: Check the horizontal precision factor and the elevation precision factor of the positioning result report. When either the horizontal precision factor or the elevation precision factor exceeds the preset threshold (for example, horizontal precision factor > 0.05 meters, elevation precision factor > 0.08 meters), it is determined that the data is not reliable enough and is rejected.

[0096] Satellite geometry determination: Check the position dilution of precision. When the position dilution of precision exceeds the preset threshold (for example, position dilution of precision > 3), it indicates that the spatial distribution geometry of the current satellite is not good, and the positioning result is weak in error resistance. The data should be marked as unreliable.

[0097] Reasonable range determination: Compare the calculated three-dimensional coordinates with the known coordinates of the suspension point of the traverse, the direction of the traverse, and the maximum possible physical motion range of the traverse. If the positioning result exceeds the preset reasonable spatial boundary (for example, there is a instantaneous jump of tens of meters), it is determined to be obviously abnormal data (usually caused by multipath effect or cycle slip), and is rejected.

[0098] Only when all the above criteria are met, the absolute positioning information is considered as "valid data" and enters the subsequent processing flow.

[0099] C2, synchronize the screened valid absolute positioning information with the master clock in time.

[0100] C3, perform noise suppression processing on the screened and aligned absolute positioning information, and adapt to the target data frequency according to the data fusion requirements.

[0101] In the embodiment of the application, the extended Kalman filter framework in S5 adopts an indirect filtering method based on error state quantity. This method divides the complete state of the system into nominal state and error state, and only estimates and updates the error state in the filter. The specific implementation steps are as follows: Define a complete state vector containing the position, velocity, attitude of the traverse, and the IMU sensor error (gyro zero bias, accelerometer zero bias), and divide it into nominal state (used for high-frequency IMU recursion) and error state (used for filter estimation).

[0102] Using the acceleration and angular velocity measured by the IMU, the nominal state (position, velocity, attitude) is directly and high-frequency updated through the inertial navigation mechanics equation.

[0103] Error state prediction: Within the filter, the mean and covariance of the error state are predicted based on the IMU error model.

[0104] When an RTK observation arrives, the difference between the nominal state prediction observation and the actual RTK observation (position, velocity) is calculated, and this difference is used as the observation and input into the error state extended Kalman filter to update the error state estimate.

[0105] The estimated error state quantity is fed back to the nominal state to correct it. Then the error state is cleared to zero and its covariance is partially reset to complete one closed-loop correction.

[0106] In one alternative implementation, the extended Kalman filter framework can be a direct filtering method. This method directly predicts and updates all system states (position, velocity, attitude, sensor errors) within a single filter. The steps are as follows: Using IMU data as system input, the mean and covariance of all state variables are directly predicted through the complete dynamic equations of the inertial navigation system.

[0107] When an RTK observation arrives, it is directly compared with the predicted position and velocity in the state vector, and all state variables are updated at once using the standard extended Kalman filter formula.

[0108] In another alternative implementation, the extended Kalman filter framework can also be an unscented Kalman filter. This method uses deterministic sampling (Sigma points) to approximate the state distribution to better handle nonlinear problems. The steps are as follows: Before the prediction and update steps, a set of Sigma points are selected based on the mean and covariance of the current state.

[0109] These Sigma points are passed through the nonlinear system equation and the observation equation respectively. Then, the predicted and updated state mean and covariance are calculated based on the passed point set, thus avoiding the linearization of the nonlinear function. Furthermore, in S5, state prediction is driven by short-term attitude changes, and observation updates are triggered by preprocessed absolute positioning information. Data fusion is performed, including steps D1-D4: D1. In each high-frequency processing cycle, the current state vector and uncertainty of the conductor are predicted by using the short-term attitude change and a dynamic model.

[0110] Furthermore, the dynamic model is a readily available and mature inertial navigation solution framework that specifies how to use IMU angular velocity and acceleration measurements to deduce the vehicle's motion state. Its core components include: The vehicle's attitude is updated using the angular velocity measured by the gyroscope. This is a standard algorithm used to determine the orientation of the vehicle's coordinate system relative to the navigation coordinate system.

[0111] Specifically, the attitude quaternion update is represented as follows: Speed ​​prediction is expressed as, Location prediction is expressed as, in, For attitude quaternions, This is quaternion multiplication; The direction cosine matrix is ​​generated by quaternions; For IMU acceleration readings, It is the gravity vector; The sampling period; These are position and velocity, respectively.

[0112] D2. When the preprocessed absolute positioning information is received, it is used as an observation value and compared with the predicted state vector to calculate the difference between the two.

[0113] D3. Based on the uncertainties of both the state vector and the observations, calculate the Kalman gain, and use the Kalman gain to perform weighted fusion of the differences between the state vector and the observations to obtain the optimal state estimate of the state vector at the current time.

[0114] D4. Output the optimal state estimate after fusion, as the initial value for the state prediction at the next moment, and complete the filtering iteration.

[0115] Furthermore, in S6, the conductor's attitude and position information are converted into the conductor's Euler angles and three-dimensional spatial coordinates, and the conductor's geometric state is calculated, including steps E1-E4: E1. Convert the attitude information obtained from data fusion into pitch angle, roll angle, and yaw angle Euler angle representations.

[0116] E2. Based on the transformed three-dimensional spatial coordinates, determine the relative spatial relationship between the conductor suspension point and the monitoring point under the specified reference coordinate system.

[0117] E3. Based on the catenary model or spatial geometric relationship of the conductor, use the coordinates and orientation of the monitoring points to calculate the key geometric parameters that characterize the spatial morphology.

[0118] E4. Based on the data from a series of monitoring points on the conductor, the overall spatial shape and dynamic change curve of the conductor are reconstructed through curve fitting.

[0119] The overall spatial morphology and dynamic change curves of the conductor are specifically the conductor sag, wind deflection angle, and attitude change curves.

[0120] Specifically, the quaternion pose is converted into Euler angles for engineering output: Scroll .

[0121] Looking up and down .

[0122] course .

[0123] in, Rotation matrix elements, These are roll, pitch, and yaw angles, respectively.

[0124] Furthermore, the pose anomaly identification and risk warning in S7 includes steps F1-F4: F1. Compare the key geometric parameters calculated in real time with the preset historical safe operation benchmark values ​​to calculate the deviation.

[0125] Specifically, a fixed safety upper limit is set for each key geometric parameter (such as sag and wind deflection angle) by comparing it with preset historical safe operating benchmarks. This is the most basic and quickest judgment; once the real-time data exceeds this hard threshold, a primary alarm is immediately triggered. For example: "Sag value > maximum allowable sag in design".

[0126] F2. Based on preset judgment rules, identify whether the deviation exceeds the static threshold.

[0127] Monitor the rate of change of key geometric parameters per unit time. This rule is used to identify a sharp deterioration in the conductor's condition, such as a sag rate of change > 0.5 m / min.

[0128] Specifically, calculate and report conductor morphology: sag. Wind deflection angle .

[0129] It was then packaged and uploaded via LoRa / 4G for 3D visualization.

[0130] in, For reference height, Current altitude; and Reference and current respectively The projection vector of the plane, This refers to the wind deflection angle.

[0131] F3. Based on the results of anomaly identification, combined with environmental parameters and the duration of the anomaly, assess the current risk level.

[0132] F4. When the risk level exceeds the preset threshold, generate and trigger the corresponding level of risk warning information.

[0133] Example 3, referring to Figure 2 This embodiment of the present invention provides a three-dimensional attitude estimation system for transmission lines based on RTK and inertial measurement coupling, comprising: a synchronous data acquisition module, a multi-source data fusion processing module, a conductor state calculation module, an intelligent early warning analysis module, and a data communication and visualization module.

[0134] The synchronous data acquisition module constructs a time-synchronized multi-sensor monitoring node and completes the time synchronization initialization of the positioning and inertial measurement units through the time reference signal.

[0135] The multi-source data fusion processing module, under the extended Kalman filter framework, uses short-term attitude changes to drive state prediction and preprocessed absolute positioning information to trigger observation updates, thereby performing data fusion to obtain conductor attitude and position information.

[0136] The conductor state calculation module converts the conductor's attitude and position information into Euler angles and three-dimensional spatial coordinates, and calculates the conductor's geometric state.

[0137] The intelligent early warning and analysis module compares the geometric state of the conductor with historical benchmark data to identify attitude anomalies and issue risk warnings.

[0138] The data communication and visualization module uploads the results of posture anomaly identification and risk warning to the background monitoring platform via wireless communication and displays them in three-dimensional visualization.

[0139] This embodiment also provides an electronic device applicable to a three-dimensional attitude estimation method for power transmission lines based on RTK and inertial measurement coupling, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the three-dimensional attitude estimation method for power transmission lines based on RTK and inertial measurement coupling as proposed in the above embodiment.

[0140] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a three-dimensional attitude estimation method for transmission lines based on RTK and inertial measurement coupling as proposed in the above embodiment.

[0141] The storage medium proposed in this embodiment belongs to the same inventive concept as the method for three-dimensional attitude estimation of transmission lines based on RTK and inertial measurement coupling proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0142] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0143] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A three-dimensional attitude estimation method for transmission lines based on RTK and inertial measurement coupling, characterized in that: include, Construct a time-synchronized multi-sensor monitoring node, and complete the time synchronization initialization of the positioning and inertial measurement units through a time reference signal; The positioning and inertial measurement unit acquires the motion inertial parameters of the conductor at a first frequency and calculates short-term attitude changes; The positioning and inertial measurement unit receives satellite signals at a second frequency and calculates the absolute positioning information of the conductor using the differential base station. Preprocess the absolute positioning information; Under the extended Kalman filter framework, state prediction is driven by short-term attitude changes, and observation updates are triggered by preprocessed absolute positioning information. Data fusion is then performed to obtain conductor attitude and position information. The conductor's attitude and position information are converted into Euler angles and three-dimensional spatial coordinates, and the geometric state of the conductor is calculated. The geometric state of the conductor is compared with historical benchmark data to identify attitude anomalies and provide risk warnings. The results of posture anomaly identification and risk warning are uploaded to the back-end monitoring platform via wireless communication and displayed in 3D visualization.

2. The method for three-dimensional attitude estimation of transmission lines based on RTK and inertial measurement coupling as described in claim 1, characterized in that: The process of acquiring the inertial parameters of the conductor at a first frequency and calculating short-term attitude changes includes, The positioning and inertial measurement unit performs error compensation on the collected motion inertial parameters; Based on the motion inertial parameters after error compensation, the attitude is updated through attitude calculation, and the real-time attitude of the conductor is obtained by integral calculation. The current attitude is compared with the attitude at the previous moment, and the attitude change is output.

3. The method for three-dimensional attitude estimation of transmission lines based on RTK and inertial measurement coupling as described in claim 2, characterized in that: The process of receiving satellite signals at a second frequency and combining this with differential base station calculations to obtain the absolute positioning information of the conductor includes: The positioning and inertial measurement unit performs real-time differential processing on the satellite signals it receives and the raw data from the differential base station; Based on differential processing, the carrier phase integer ambiguity is resolved; After fixing the ambiguity, the absolute positioning information of the conductor is calculated and output.

4. The method for three-dimensional attitude estimation of transmission lines based on RTK and inertial measurement coupling as described in claim 3, characterized in that: The preprocessing of the absolute positioning information includes... The absolute positioning information is validated based on a preset quality judgment criterion. Synchronize the filtered valid absolute positioning information with the master clock. The filtered and aligned absolute positioning information is subjected to noise suppression processing, and adapted to the target data frequency according to the data fusion requirements.

5. The method for three-dimensional attitude estimation of transmission lines based on RTK and inertial measurement coupling as described in claim 4, characterized in that: The process of driving state prediction with short-term attitude changes and triggering observation updates with preprocessed absolute positioning information, along with data fusion, includes... In each high-frequency processing cycle, the current state vector and uncertainty of the conductor are predicted by the dynamic model using the short-term attitude change. When the preprocessed absolute positioning information is received, it is used as an observation and compared with the predicted state vector to calculate the difference between the two. Based on the uncertainties of both the state vector and the observations, the Kalman gain is calculated, and the Kalman gain is used to perform weighted fusion of the differences between the state vector and the observations to obtain the optimal state estimate of the state vector at the current time. The optimal state estimate after fusion is output as the initial value for the state prediction at the next time step, thus completing the filtering iteration.

6. The method for three-dimensional attitude estimation of transmission lines based on RTK and inertial measurement coupling as described in claim 4, characterized in that: The process of converting the conductor's attitude and position information into Euler angles and three-dimensional spatial coordinates, and calculating the conductor's geometric state, includes: The attitude information obtained from data fusion is converted into pitch angle, roll angle, and yaw angle Euler angle representations. Based on the transformed three-dimensional spatial coordinates, the relative spatial relationship between the conductor suspension point and the monitoring point is determined under the specified reference coordinate system; Based on the catenary model or spatial geometric relationship of the conductor, the key geometric parameters characterizing the spatial morphology are calculated using the coordinates and orientation of the monitoring points; Based on data from a series of monitoring points along the conductor, the overall spatial shape and dynamic change curve of the conductor are reconstructed through curve fitting.

7. The method for three-dimensional attitude estimation of transmission lines based on RTK and inertial measurement coupling as described in claim 4, characterized in that: The aforementioned posture anomaly identification and risk warning includes, The key geometric parameters calculated in real time are compared with the preset historical safe operation benchmark values ​​to calculate the deviation. Based on preset judgment rules, it identifies whether the deviation exceeds the static threshold. Based on the results of anomaly identification, combined with environmental parameters and the duration of the anomaly, the current risk level is assessed; When the risk level exceeds the preset threshold, a risk warning message of the corresponding level is generated and triggered.

8. A system for improving short-term high-frequency energy storage efficiency, employing a three-dimensional attitude estimation method for transmission lines based on RTK and inertial measurement coupling as described in any one of claims 1 to 7, characterized in that, include: The system includes a synchronous data acquisition module, a multi-source data fusion and processing module, a conductor status calculation module, an intelligent early warning and analysis module, and a data communication and visualization module. The synchronous data acquisition module constructs a time-synchronized multi-sensor monitoring node and completes the time synchronization initialization of the positioning and inertial measurement units through a time reference signal; The multi-source data fusion processing module, under the extended Kalman filter framework, uses short-term attitude change to drive state prediction and preprocessed absolute positioning information to trigger observation updates, performs data fusion, and obtains conductor attitude and position information. The conductor state calculation module converts the conductor's attitude and position information into the conductor's Euler angles and three-dimensional spatial coordinates, and calculates the conductor's geometric state. The intelligent early warning analysis module compares the geometric state of the conductor with historical benchmark data to identify abnormal posture and provide risk warnings. The data communication and visualization module uploads the results of posture anomaly identification and risk warning to the background monitoring platform via wireless communication and displays them in three-dimensional visualization.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the three-dimensional attitude estimation method for transmission lines based on RTK and inertial measurement coupling as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the three-dimensional attitude estimation method for transmission lines based on RTK and inertial measurement coupling as described in any one of claims 1 to 7.

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