Conductor galloping monitoring method and system based on Beidou radar space-time calibration

Abstract

The invention provides a conducting wire galloping monitoring method and system based on Beidou radar space-time calibration, and belongs to the technical field of conducting wire monitoring, the monitoring method is applied to a monitoring system comprising a data acquisition module, a data processing module and a conducting wire galloping monitoring module, and the monitoring method specifically comprises the following steps: synchronously acquiring Beidou positioning data and radar ranging data; obtaining multi-source fusion data of conductor galloping in combination with space-time calibration; layered error compensation is carried out on the multi-source fusion data in sequence through third-order galloping compensation; performing atmospheric delay error correction on the multi-source fusion data after the third-order compensation according to double-grid fusion correction; and solving a conductor galloping track according to the corrected multi-source fusion data in combination with a historical galloping mode and conductor physical parameters. Multi-source data fusion is realized through space-time calibration, sensor hardware inherent errors in multi-source fusion data and errors caused by environmental interference are compensated by further combining third-order galloping compensation and double-grid fusion correction, and conductor galloping monitoring accuracy is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of conductor monitoring technology, and in particular to a method and system for monitoring conductor galloping based on BeiDou radar spatiotemporal calibration. Background Technology

[0002] Conductor galloping, a low-frequency, large-amplitude periodic oscillation that occurs on transmission lines under meteorological conditions such as strong winds and icing, can easily lead to conductor fatigue fracture, tower tilting, or even line tripping, posing a serious threat to the safe operation of the power grid. High-precision monitoring is needed to achieve real-time perception and risk warning of the conductor galloping status of transmission lines.

[0003] Currently, mainstream conductor galloping monitoring is mainly achieved through BeiDou positioning or radar monitoring. BeiDou positioning monitoring primarily collects BeiDou pseudorange and carrier phase data at the conductor attachment points, and calculates the conductor position by combining this with differential information from a reference station. Radar monitoring, on the other hand, collects conductor point cloud data using millimeter-wave or microwave radar, and analyzes coordinate changes to extract galloping characteristics. This single-data-source monitoring method is inherently limited by the principles of its own sensors and their environmental adaptability, and generally suffers from accuracy bottlenecks. BeiDou positioning is susceptible to atmospheric delay and multipath effects, while radar is susceptible to atmospheric refraction and phase noise interference, ultimately leading to insufficient data accuracy and limiting the accuracy and reliability of monitoring.

[0004] To overcome the accuracy limitations of single data sources, some solutions attempt to achieve monitoring through multi-source data fusion. However, existing fusion methods mostly remain at the level of shallow data overlay, simply combining the monitoring results from different sensors to obtain conductor galloping monitoring results. This simple overlay method still cannot overcome the interference from different environmental factors that different sensors face. Furthermore, different sensors have different data acquisition principles and encounter different environmental error interference terms. This data overlay method is prone to error accumulation, resulting in insufficient data accuracy and making it difficult to guarantee the accuracy of conductor galloping monitoring. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies that use a single data source or simple multi-source data fusion to monitor conductor galloping, which ignore the inherent limited accuracy of sensors and susceptibility to environmental interference, resulting in insufficient data acquisition accuracy and low monitoring accuracy of conductor galloping. This invention provides a method and system for monitoring conductor galloping based on BeiDou radar spatiotemporal calibration. By achieving multi-source data fusion through spatiotemporal calibration, and further combining third-order galloping compensation and dual-grid fusion correction, the invention compensates for inherent sensor hardware errors and errors caused by environmental interference in the multi-source fused data, optimizes data accuracy, and thus improves the subsequent monitoring accuracy of conductor galloping.

[0006] The objective of this invention is achieved through the following technical solution: A method for monitoring conductor galloping based on BeiDou radar spatiotemporal calibration includes: Simultaneously collect BeiDou positioning data and radar ranging data, and combine spatiotemporal calibration to obtain multi-source fusion data of conductor galloping; The multi-source fusion data is sequentially layered with error compensation through third-order dance compensation. Atmospheric delay error correction is performed on the third-order compensated multi-source fusion data based on dual-grid fusion correction. By combining historical galloping patterns and conductor physical parameters, and based on the corrected multi-source fusion data, the galloping trajectory of the conductor is solved.

[0007] By fusing BeiDou positioning data and radar ranging data through spatiotemporal calibration, the accuracy limitations of a single data source are avoided. Third-order galloping compensation is used to specifically reduce errors caused by inherent sensor hardware errors and environmental interference, improving the accuracy of the fused data. Furthermore, atmospheric delay error is specifically corrected based on dual-grid fusion correction, further reducing the impact of atmospheric factors on data accuracy. Finally, the galloping trajectory of the conductor is solved by combining historical galloping patterns and conductor physical parameters, ensuring that the trajectory results conform to the actual movement laws and physical characteristics of the conductor, thus improving the data accuracy and monitoring results of conductor galloping monitoring.

[0008] Furthermore, the simultaneous acquisition of BeiDou positioning data and radar ranging data, combined with spatiotemporal calibration to obtain multi-source fusion data of conductor galloping, includes: Collect BeiDou positioning data and radar ranging data, and preprocess the BeiDou positioning data and radar ranging data respectively; By combining the static basic data of the monitoring area, temporal and spatial error equations are established and solved to obtain the spatiotemporal calibration matrix; Spatiotemporal calibration of BeiDou positioning data and radar ranging data is performed based on the spatiotemporal calibration matrix, and BeiDou positioning data and radar ranging data are associated with corresponding timestamps to obtain multi-source fusion data of conductor galloping.

[0009] Furthermore, the step of performing hierarchical error compensation on the multi-source fusion data through third-order dancing compensation includes: Hardware error parameters are calculated based on the pre-calibration parameters of the data acquisition equipment and the temperature data of the monitoring area. Hardware error compensation is then performed on the multi-source fusion data based on the hardware error parameters. Environmental error parameters are calculated based on meteorological data, geographical data and historical climate data of the monitoring area, and environmental error compensation is performed on the multi-source fusion data after first-order hardware error compensation. Kinematic error parameters are calculated based on the performance parameters of the data acquisition equipment, the motion state information of the monitoring area, and the motion constraints. Kinematic error compensation is then performed on the multi-source fusion data after second-order environmental error compensation based on the kinematic error parameters.

[0010] Furthermore, the step of calculating hardware error parameters based on the pre-calibration parameters of the data acquisition device and the temperature data of the monitoring area, and performing hardware error compensation on the multi-source fusion data based on the hardware error parameters, includes: Based on the pre-calibration parameters of the data acquisition equipment, obtain the fixed delay value, phase center offset, and multi-channel signal delay deviation value of the equipment. The time synchronization deviation of the multi-source fusion data is corrected based on the fixed delay value of the equipment and the delay deviation value of the multi-channel signal, and the spatial coordinates of the multi-source fusion data are calibrated based on the phase center offset. The temperature error coefficient and calibration temperature are obtained based on the pre-calibration parameters of the data acquisition equipment, and the temperature drift is calculated by combining the temperature data of the monitoring points. The measurement deviation of multi-source fusion data is compensated based on the temperature offset.

[0011] Furthermore, the step of calculating environmental error parameters based on meteorological data, geographical data, and historical climate data of the monitoring area, and performing environmental error compensation on the multi-source fusion data after first-order hardware error compensation, includes: The atmospheric delay and path error values ​​are calculated based on meteorological and geographical data of the monitored area, respectively. Based on historical climate data of the monitoring area, an environmental error compensation coefficient is calculated by combining atmospheric delay and path error values, and correction weights for atmospheric delay and path error values ​​are set according to the environmental error compensation coefficient. Based on atmospheric delay and path error values, the spatial coordinates of the multi-source fusion data after first-order hardware error compensation are corrected by combining the corresponding correction weights.

[0012] Furthermore, the step of calculating kinematic error parameters based on the performance parameters of the data acquisition equipment, the motion state information of the monitoring area, and the motion constraints, and then performing kinematic error compensation on the multi-source fusion data after second-order environmental error compensation based on the kinematic error parameters, includes: Calculate the dynamic response delay of the data acquisition equipment based on its performance parameters, and then perform time correction on the multi-source fusion data after second-order environmental error compensation based on the dynamic response delay. Calculate the motion model deviation value and physical constraint deviation value based on the motion state information and motion constraint conditions of the monitored area; Spatial coordinate correction is performed on the time-corrected multi-source fusion data based on the motion model deviation value and the physical constraint deviation value.

[0013] Furthermore, the atmospheric delay error correction of the third-order compensated multi-source fusion data based on dual-grid fusion correction includes: The raw observation data and historical climate data of the monitoring area are assimilated, and the total zenith delay for each grid point is calculated accordingly. The zenith delay of the grid points is interpolated to arbitrary positions through spatial interpolation, and a global delay field is generated by combining the mapping function and error propagation analysis. The meteorological field of each monitoring point is reconstructed based on the meteorological data of the monitoring area, and a three-dimensional atmospheric refractive index field of each monitoring point is established based on the corresponding meteorological field. Based on the three-dimensional atmospheric refractive index field of each monitoring point, and combined with the three-dimensional ray tracing algorithm, the actual transmission path of the corresponding signal is iteratively calculated to construct a local delay field. The fused delay field is obtained by spatially weighted fusion of the global delay field and the local delay field; Atmospheric delay error correction is performed on the third-order compensated multi-source fusion data based on the fusion delay field.

[0014] Furthermore, the step of combining historical galloping patterns and conductor physical parameters, and solving the conductor galloping trajectory based on the corrected multi-source fusion data, includes: A galloping compensation model is constructed based on catenary theory. With the goal of minimizing residuals, the model parameters of the galloping compensation model are iteratively optimized by combining historical galloping patterns and conductor physical parameters. The corrected multi-source fusion data is input into the optimized galloping compensation model to reconstruct the conductor galloping trajectory.

[0015] A conductor galloping monitoring system based on BeiDou radar spatiotemporal calibration, used to perform any of the above-mentioned conductor galloping monitoring methods based on BeiDou radar spatiotemporal calibration, includes: The data acquisition module is used to collect BeiDou positioning data and radar ranging data for each monitoring point within the monitoring area; The data processing module is used to fuse the collected BeiDou positioning data and radar ranging data by combining spatiotemporal calibration, and to perform error compensation and error correction on the fused multi-source data through third-order galloping compensation and dual-grid fusion correction. The conductor galloping monitoring module is used to combine historical galloping patterns and conductor physical parameters, and to solve the conductor galloping trajectory based on the corrected multi-source fusion data.

[0016] Furthermore, the conductor galloping monitoring module also includes: The risk alarm unit is used to conduct a safety assessment based on the conductor galloping trajectory and issue an early warning based on the safety assessment results.

[0017] The beneficial effects of this invention are: (1) The fusion of BeiDou positioning data and radar ranging data is achieved through spatiotemporal calibration, avoiding the accuracy limitations of a single data source. Third-order galloping compensation is used to specifically reduce errors caused by inherent sensor hardware errors and environmental interference, thereby improving the accuracy of the fused data. Then, atmospheric delay error is specifically corrected based on dual-grid fusion correction, further reducing the impact of atmospheric factors on data accuracy. Finally, the galloping trajectory of the conductor is solved by combining historical galloping patterns and conductor physical parameters, ensuring that the trajectory results conform to the actual motion law and physical characteristics of the conductor, thereby improving the data accuracy and monitoring results accuracy of conductor galloping monitoring.

[0018] (2) Through third-order dancing compensation, which includes hardware error compensation, environmental error compensation, and kinematic error compensation, targeted processing of different types of errors is achieved, reducing the overall error of multi-source fusion data. Furthermore, combined with dual-grid fusion correction, atmospheric delay error is corrected by constructing global and local delay fields that cover large-area trends and small-area anomalies.

[0019] (3) Based on the catenary theory, a galloping compensation model is constructed and combined with historical patterns and conductor physical parameters for iterative optimization. This makes the reconstructed conductor galloping trajectory conform to the laws of mechanics and match the historical galloping characteristics of the line, effectively improving the accuracy of conductor galloping monitoring. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of a process of the present invention; Figure 2 This is a flowchart of a spatiotemporal calibration method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a structure according to an embodiment of the present invention.

[0021] The components include: 1. Data acquisition module, 2. Data processing module, 3. Conductor galloping monitoring module, and 31. Risk alarm unit. Detailed Implementation

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] Example: A method for monitoring conductor galloping based on BeiDou radar spatiotemporal calibration, such as Figure 1 As shown, it includes: Simultaneously collect BeiDou positioning data and radar ranging data, and combine spatiotemporal calibration to obtain multi-source fusion data of conductor galloping; The multi-source fusion data is sequentially layered with error compensation through third-order dance compensation. Atmospheric delay error correction is performed on the third-order compensated multi-source fusion data based on dual-grid fusion correction. By combining historical galloping patterns and conductor physical parameters, and based on the corrected multi-source fusion data, the galloping trajectory of the conductor is solved.

[0024] BeiDou positioning data excels at providing the three-dimensional global coordinates and temporal movement trends of a guideline, but it is susceptible to signal interference in close-range high-precision ranging and complex terrain scenarios, resulting in lower data accuracy. Radar ranging data, on the other hand, has the advantages of strong anti-interference capabilities and high accuracy in close-range ranging, and can accurately capture the minute displacements of the guideline's local swaying, but it lacks a global coordinate reference and cannot be directly converted into a complete three-dimensional trajectory.

[0025] Therefore, this embodiment simultaneously collects BeiDou positioning data and radar ranging data, and fuses the two types of data. The BeiDou positioning data supplements the global coordinates, and the radar ranging data supplements the local accuracy, so as to achieve the complementary advantages of BeiDou positioning data and radar ranging data, ensure the spatial integrity of the conductor galloping trajectory, and at the same time ensure the monitoring accuracy.

[0026] Considering the inherent differences in hardware clocks and coordinate systems used during the acquisition of BeiDou positioning data and radar ranging data, direct fusion could lead to problems such as coordinate misalignment and timing asynchrony, affecting the accuracy of subsequent conductor galloping monitoring. Therefore, the acquired BeiDou positioning data and radar ranging data are further spatiotemporally calibrated to achieve multi-source data fusion.

[0027] The simultaneous acquisition of BeiDou positioning data and radar ranging data, combined with spatiotemporal calibration to obtain multi-source fusion data of conductor galloping, includes: Collect BeiDou positioning data and radar ranging data, and preprocess the BeiDou positioning data and radar ranging data respectively; By combining the static basic data of the monitoring area, temporal and spatial error equations are established and solved to obtain the spatiotemporal calibration matrix; Spatiotemporal calibration of BeiDou positioning data and radar ranging data is performed based on the spatiotemporal calibration matrix, and BeiDou positioning data and radar ranging data are associated with corresponding timestamps to obtain multi-source fusion data of conductor galloping.

[0028] The data structure of the collected BeiDou positioning data is {timestamp, position, velocity, heading angle, position accuracy factor, number of visible satellites, positioning quality}, where the position information includes at least latitude, longitude, and elevation.

[0029] The data structure of the collected radar ranging data is {radar internal clock, radar point cloud}, and the radar point cloud specifically includes range, azimuth angle, elevation angle, radial velocity, radar cross section and signal-to-noise ratio.

[0030] During the data acquisition process, the acquisition frequency of the two types of data needs to be matched with the dynamic characteristics of the conductor's movement to ensure that no key motion details are missed.

[0031] Subsequently, the BeiDou positioning data and radar ranging data were preprocessed to ensure their accuracy. For BeiDou positioning data, signal quality was screened based on signal-to-noise ratio (SNR), removing data with an SNR below a threshold. Multipath effect correction and gross error removal were also applied to eliminate abnormal coordinates beyond the physical motion range. For radar ranging data, point cloud denoising was performed to filter raindrops and clutter interference, and ranging accuracy was calibrated and invalid data was removed.

[0032] Then, by combining the static basic data of the monitoring area, temporal and spatial error equations are established, and the spatiotemporal calibration matrix is ​​obtained by solving them. The static basic data here includes ground control point data, sensor installation location information, boom compensation data, coordinate transformation basic parameters, clock synchronization initial data, and sensor error data, etc. Among them, the ground control point data includes the observation position and time of the same physical point in the BeiDou and radar coordinate systems. The ground control points can be tower apexes, specific reflective targets, etc.

[0033] Based on the above data, we construct a spatial error equation that reflects the deviation in spatial coordinate mapping and a time error equation that reflects the differences in the time system.

[0034] The expression for the spatial error equation is: ; The expression for the time error equation is: ; in, For the first Spatial error of each ground control point For the first Time error of each ground control point For the first The measured positions of ground control points in the radar coordinate system For the first The measured position of each ground control point in the BeiDou coordinate system For the first Measurement time of each ground control point in the BeiDou coordinate system For rotation matrix, The coordinates of the preset spatial reference point, It is a spatial translation vector. For velocity compensation vector, For the first Measurement time of each ground control point in the radar coordinate system For a fixed time offset, For time drift rate, These are time synchronization parameters.

[0035] Then, with minimizing the weighted sum of squared errors as the optimization objective, the spatial error equation and the time error equation are solved by alternating iterative methods of solving spatial parameters with fixed time parameters and solving time parameters with fixed spatial parameters, so as to obtain a spatiotemporal calibration matrix containing time synchronization rules and coordinate transformation logic.

[0036] The expression for the optimization objective is: ; in, The number of ground control points. This is the weighting coefficient for time error.

[0037] The constructed spatiotemporal calibration matrix is ​​a 4×4 matrix, including the rotation matrix R and the spatial translation vector. Velocity compensation vector and time synchronization parameters .

[0038] Finally, the BeiDou positioning data and radar ranging data are spatiotemporally calibrated based on the spatiotemporal calibration matrix, and multi-source fused data is generated. During the spatiotemporal calibration process, on the one hand, the radar time system is unified to the BeiDou time system based on the time parameters in the calibration matrix, eliminating timing misalignments caused by time offsets and drifts. On the other hand, a preset link is established, transforming the data from the BeiDou coordinate system to the local coordinate system, then to the radar coordinate system, and finally to the local coordinate system of the guide wire. The coordinate transformation of the two types of data is completed through the spatial parameters in the spatiotemporal calibration matrix, unifying them to the same target coordinate system.

[0039] After completing the spatiotemporal calibration, using a unified timestamp as an index, the BeiDou positioning data and radar ranging data corresponding to the same time and spatial location are associated to achieve complementary advantages of the two types of data, and finally generate multi-source fusion data that is time-continuous, coordinate consistent and error controllable.

[0040] The spatiotemporal calibration process is as follows: Figure 2 As shown.

[0041] After completing the spatiotemporal calibration, although the acquired multi-source fusion data has achieved the same time reference and the same spatial coordinate system, there are still three types of residual errors that cannot be eliminated through calibration. These errors directly affect the accuracy of solving the conductor galloping trajectory. Specifically, these include hardware and installation-related errors stemming from the characteristics of the equipment itself and limitations of the installation process, such as clock errors of the BeiDou receiver and ranging deviations of the radar, as well as arm deviations that were not fully corrected during sensor installation. They also include environmental interference errors that change dynamically with the environment and cannot be eliminated in advance through static calibration, including signal propagation deviations caused by atmospheric refraction and conductor thermal expansion and contraction offsets caused by temperature changes. Furthermore, they include kinematic errors. Conductor galloping is a complex nonlinear motion, and the discrete observations in the multi-source fusion data may have dynamic sampling deviations. In addition, the kinematic modeling of radar point clouds and BeiDou positioning itself also has deviations due to simplification.

[0042] Therefore, this embodiment specifically sets up a third-order dancing compensation to compensate for errors existing in multi-source fused data, and performs error compensation in a hierarchical manner to avoid mutual interference between different types of errors and improve compensation efficiency.

[0043] The method of performing hierarchical error compensation on multi-source fused data through third-order dancing compensation includes: Hardware error parameters are calculated based on the pre-calibration parameters of the data acquisition equipment and the temperature data of the monitoring area. Hardware error compensation is then performed on the multi-source fusion data based on the hardware error parameters. Environmental error parameters are calculated based on meteorological data, geographical data and historical climate data of the monitoring area, and environmental error compensation is performed on the multi-source fusion data after first-order hardware error compensation. Kinematic error parameters are calculated based on the performance parameters of the data acquisition equipment, the motion state information of the monitoring area, and the motion constraints. Kinematic error compensation is then performed on the multi-source fusion data after second-order environmental error compensation based on the kinematic error parameters.

[0044] Hardware errors mainly include inherent and relatively fixed system errors of the equipment as well as dynamic drift errors that change with temperature. Further error compensation is performed by using the pre-calibration parameters of the data acquisition equipment and the temperature data of the monitoring area to provide more accurate basic data.

[0045] The step of calculating hardware error parameters based on the pre-calibration parameters of the data acquisition equipment and the temperature data of the monitoring area, and then performing hardware error compensation on the multi-source fusion data based on the hardware error parameters, includes: Based on the pre-calibration parameters of the data acquisition equipment, obtain the fixed delay value, phase center offset, and multi-channel signal delay deviation value of the equipment. The time synchronization deviation of the multi-source fusion data is corrected based on the fixed delay value of the equipment and the delay deviation value of the multi-channel signal, and the spatial coordinates of the multi-source fusion data are calibrated based on the phase center offset. The temperature error coefficient and calibration temperature are obtained based on the pre-calibration parameters of the data acquisition equipment, and the temperature drift is calculated by combining the temperature data. The measurement deviation of multi-source fusion data is compensated based on the temperature offset.

[0046] The fixed delay value of the device is the fixed propagation time delay of the signal in the hardware link of the Beidou receiver and the millimeter-wave radar. The phase center offset is the spatial offset between the phase center of the Beidou antenna and the mechanical installation center and the phase center of the radar. The multi-channel signal delay deviation value is the difference in signal transmission delay between the Beidou multi-channel receiver or the radar multi-channel receiving module. These three types of parameters are fixed values ​​calibrated after the device leaves the factory or is installed, directly reflecting the inherent deviation of the hardware itself. The relevant parameter values ​​can be directly extracted from the pre-calibration parameters of the data acquisition equipment corresponding to the Beidou positioning data and the radar ranging data.

[0047] Based on the extracted fixed hardware error parameters, the spatiotemporal deviation of multi-source fusion data is corrected in multiple dimensions. Although the spatiotemporal calibration in multi-source data fusion has achieved coarse time synchronization, the fixed delay of the hardware link and the multi-channel delay can lead to residual time deviations. Therefore, the time synchronization deviation of the multi-source fusion data is further corrected by combining the fixed delay value of the equipment and the delay deviation value of the multi-channel signal, ensuring the temporal consistency between BeiDou positioning data and radar ranging data. Then, a coordinate transformation matrix is ​​constructed based on the phase center offset to transform the observation position from the phase center to the mechanical center, calibrating the spatial coordinates of the multi-source fusion data, thereby avoiding spatial coordinate deviations caused by the inconsistency between the installation center and the signal receiving center.

[0048] Next, from the pre-calibration parameters of the data acquisition equipment, the temperature error coefficient and calibration temperature, which characterize the sensitivity of the equipment performance to temperature changes, are obtained. The calibration temperature is the reference temperature used when calibrating hardware parameters in the laboratory. Then, combined with the real-time temperature data of the corresponding acquired monitoring points, the temperature drift is calculated. The temperature drift is then inversely superimposed onto the position, velocity, and other measurements of the multi-source fusion data to correct the hardware performance deviation error caused by temperature changes.

[0049] First-order hardware compensation only eliminates the inherent fixed errors of the device. However, for dynamic area errors that cannot be covered by hardware compensation, further error compensation is needed for the environmental interference errors.

[0050] The calculation of environmental error parameters based on meteorological data, geographical data, and historical climate data of the monitoring area, and the environmental error compensation of the multi-source fused data after first-order hardware error compensation, includes: The atmospheric delay and path error values ​​are calculated based on meteorological and geographical data of the monitored area, respectively. Based on historical climate data of the monitoring area, an environmental error compensation coefficient is calculated by combining atmospheric delay and path error values, and correction weights for atmospheric delay and path error values ​​are set according to the environmental error compensation coefficient. Based on atmospheric delay and path error values, the spatial coordinates of the multi-source fusion data after first-order hardware error compensation are corrected by combining the corresponding correction weights.

[0051] The meteorological data for the monitored area specifically refers to real-time meteorological data collected by micro-meteorological stations within the monitoring area, including temperature, humidity, and air pressure data. The geographic data includes the digital elevation model and land cover type of the monitored area.

[0052] For atmospheric delay, the dry delay is calculated based on temperature and air pressure, and the wet delay is calculated based on humidity. These are then superimposed to obtain the atmospheric delay that reflects the normal propagation deviation of the signal. The atmospheric delay covers the normal atmospheric effects on the monitoring points and surrounding local areas within the monitoring area.

[0053] For path error values, first identify major multipath reflection sources such as mountains and buildings based on the geographical data of the monitoring area, and then combine the multipath characteristics of BeiDou positioning such as the phase fluctuation of the incoming carrier wave and the signal reflection intensity of the radar point cloud to quantify the path deviation caused by signal reflection.

[0054] Based on historical climate data of the detection area, an environmental compensation coefficient is calculated by combining the atmospheric delay and path error values, and corresponding correction weights are set to ensure that error correction conforms to regional environmental patterns and avoids deviations caused by fluctuations in single real-time data. If the atmospheric delay and path error values ​​conform to the environmental error patterns reflected in historical climate data, the environmental compensation coefficient is set to a normal value, and then the correction weights for atmospheric delay and path error rates are matched according to the environmental compensation coefficient.

[0055] Finally, the atmospheric delay is multiplied by the corresponding correction weight to obtain the coordinate correction for conventional atmospheric delay, and the path error is multiplied by the corresponding correction weight to obtain the coordinate correction for conventional multipath error. The two types of corrections are then superimposed in reverse into the spatial coordinates after first-order hardware compensation to achieve environmental error compensation.

[0056] After completing first-order hardware error compensation and second-order environmental error compensation, further compensation is made for kinematic errors caused by the dynamic performance limitations of data acquisition equipment and the complex nonlinear characteristics of conductor galloping, so as to ensure the accuracy of multi-source fusion data.

[0057] The process of calculating kinematic error parameters based on the performance parameters of the data acquisition equipment, the motion state information of the monitoring area, and the motion constraints, and then performing kinematic error compensation on the multi-source fusion data after second-order environmental error compensation based on the kinematic error parameters, includes: Calculate the dynamic response delay of the data acquisition equipment based on its performance parameters, and then perform time correction on the multi-source fusion data after second-order environmental error compensation based on the dynamic response delay. Calculate the motion model deviation value and physical constraint deviation value based on the motion state information and motion constraint conditions of the monitored area; Spatial coordinate correction is performed on the time-corrected multi-source fusion data based on the motion model deviation value and the physical constraint deviation value.

[0058] The performance parameters include the data update rate of the BeiDou receiver, the scanning rate of the millimeter-wave radar, and the signal processing delay. These performance parameters quantify the corresponding dynamic response delay, and then shift the timestamps of data with large delays, such as radar ranging data, forward by the delay amount to align them with the time reference of the BeiDou positioning data. Interpolation is used to complete discrete data within time gaps, correcting timing lags caused by insufficient dynamic performance of the equipment. This ensures that the data timing matches the actual movement of the conductor, avoiding misalignment of spatial coordinates with the actual position due to time lag.

[0059] Based on the multi-source fusion data after second-order environmental error compensation, the motion state information of the conductor is extracted through differential calculation. Specifically, the observation positions of the conductor at two adjacent time points are taken, and a three-dimensional velocity vector is calculated by the ratio of the position difference to the time difference. Then, the velocity data at subsequent time points are taken, and a three-dimensional acceleration vector is further calculated by the ratio of the velocity difference to the time difference. The three-dimensional velocity vector and the three-dimensional acceleration vector reflect the nonlinear motion characteristics of the conductor caused by wind speed fluctuations and tension changes.

[0060] Next, the linear motion model used in the preceding data processing is obtained. Based on this linear motion model, the theoretical position of the conductor at a certain moment is predicted. Then, the actual position of the conductor is derived through the calculated motion state information. The theoretical position of the conductor and the actual position of the conductor are compared to obtain the deviation value of the motion model.

[0061] The motion constraints are then determined based on the physical parameters of the conductor, including conductor tension, span, and elastic modulus, while the motion constraints include the range of motion of the conductor in dimensions such as position, velocity, and amplitude.

[0062] Based on the motion constraints, the multi-source fusion data after second-order environmental compensation is screened, the data that exceeds the corresponding motion range is screened out, and the physical constraint deviation value is determined according to the corresponding excess amount.

[0063] For each spatial coordinate point in the time-corrected multi-source fusion data, the corresponding motion model deviation value is extracted and then superimposed onto the original coordinates. Next, based on the physical constraints, the corresponding anomalous coordinates are adjusted using neighborhood smoothing or prediction compensation methods to normalize the anomalous coordinates within the physical boundaries and eliminate spurious deviations that do not conform to the characteristics of the conductor.

[0064] After correcting the spatial coordinates of the time-corrected multi-source fusion data based on the motion model deviation value and the physical constraint deviation value, further verification of trajectory continuity is required to ensure the effectiveness of the correction.

[0065] Third-order dance compensation can effectively eliminate hardware errors, environmental errors, and motion errors, ensuring the accuracy of multi-source fusion data. However, in second-order environmental error compensation, the handling of atmospheric delay is essentially based on conventional correction of local real-time meteorological data, ignoring the spatial heterogeneity of atmospheric delay, which easily leaves some atmospheric errors.

[0066] Therefore, based on the initial atmospheric error compensation of third-order galloping compensation, dual-grid fusion is further introduced to correct the residual atmospheric error.

[0067] The step of correcting atmospheric delay error in the third-order compensated multi-source fusion data based on dual-grid fusion correction includes: The raw observation data and historical climate data of the monitoring area are assimilated, and the total zenith delay for each grid point is calculated accordingly. The zenith delay of the grid points is interpolated to arbitrary positions through spatial interpolation, and a global delay field is generated by combining the mapping function and error propagation analysis. The meteorological field of each monitoring point is reconstructed based on the meteorological data of the monitoring area, and a three-dimensional atmospheric refractive index field of each monitoring point is established based on the corresponding meteorological field. Based on the three-dimensional atmospheric refractive index field of each monitoring point, and combined with the three-dimensional ray tracing algorithm, the actual transmission path of the corresponding signal is iteratively calculated to construct a local delay field. The fused delay field is obtained by spatially weighted fusion of the global delay field and the local delay field; Atmospheric delay error correction is performed on the third-order compensated multi-source fusion data based on the fusion delay field.

[0068] The monitoring area is divided into global grid and local grid, with a 50km×50km grid as the global grid granularity and a 1km×1km dynamic grid as the local grid granularity.

[0069] When constructing atmospheric delay data for a large-scale global grid, the input data includes raw observation data of the monitored area, such as carrier phase, pseudorange, and satellite orbital parameters, as well as historical climate data, such as regional atmospheric analysis data, seasonal climate zoning information, and land-sea distribution data. Furthermore, quality control is performed on the raw observation data to remove outliers with low signal-to-noise ratios and incomplete data, ensuring the reliability of the input data.

[0070] The optimal interpolation method is used to assimilate the discrete raw observation data into a regular grid of 50km×50km. At the same time, historical climate data is introduced as a constraint to avoid real-time observation noise causing the grid atmospheric parameters to deviate from the reasonable range. Finally, a gridded atmospheric parameter field containing temperature, air pressure and total water vapor is obtained.

[0071] Based on this, the dry and wet components of the zenith delay are calculated separately. The dry delay is calculated using the Saastamoinen model (tropospheric model), and the wet delay is solved using a parameterized model based on the total atmospheric water vapor. The two components are superimposed to obtain the total zenith delay for each 50km grid point.

[0072] An inverse distance-weighted interpolation method is used to interpolate the total zenith delay of a 50km grid to any location within the monitoring area, such as monitoring points or tower locations. Then, a mapping function is introduced to convert the total zenith delay at each location into the slant path delay corresponding to the propagation direction of the BeiDou satellite or radar signal. This conversion can be accomplished using the Niell model (atmospheric delay mapping model). Simultaneously, an error propagation analysis algorithm is employed to track errors caused by data discreteness during interpolation and model errors in the mapping function conversion, calculating the uncertainty of the slant path delay at each location.

[0073] The final output is a global delay field covering all locations in the monitoring area, including the slant path delay value, delay uncertainty, and the coefficients of the mapping function used for each location.

[0074] When constructing a small-scale local grid atmospheric delay, the input data includes real-time meteorological data from micro-meteorological stations within the monitoring area, as well as a 1km resolution digital elevation model and land cover type data. Furthermore, the real-time meteorological data undergoes mechanical spatial consistency verification to remove outliers caused by sensor malfunctions. Then, co-kriging interpolation is used to combine discrete station data with topographic and land cover auxiliary variables to obtain a 1km×1km resolution meteorological field. The atmosphere is then vertically divided into the near-surface layer, boundary layer, and free atmosphere. Combining the meteorological field, the refractive index is calculated in the near-surface and boundary layers using surface thermal forcing and turbulence effects. For the free atmosphere, standard atmospheric profile constraint parameters are referenced. Finally, a three-dimensional atmospheric refractive index field is formed for each monitoring point to reflect the refractive index values ​​and gradient changes at different altitudes.

[0075] Then, using the three-dimensional atmospheric refractive index field of each monitoring point, the signal transmitting end position of Beidou satellite and millimeter-wave radar, and the signal receiving end position of each monitoring point as inputs, the actual transmission path of the corresponding signal is iteratively calculated by combining the three-dimensional ray tracing algorithm.

[0076] Specifically, from the signal receiver to the transmitter, the path is divided into predetermined steps. At each step, the signal propagation direction is adjusted according to the local refractive index gradient at the current location, combined with Snell's law, until the path extends to the transmitter. This simulates the actual signal propagation trajectory. The difference between the actual signal propagation path length and the path length of light in a vacuum is obtained, and the difference is the local slant path delay at that monitoring point. Finally, the local slant path delays of all monitoring points are integrated into a 1km×1km grid to form a local delay field. The local delay field includes the local slant path delay value of each grid point and the bending angle information of the signal path.

[0077] Finally, spatial weights for the global and local delay fields are determined based on the terrain of the monitoring area and the stability of real-time meteorological data. When the monitoring point is located in a flat area and the meteorological conditions are stable, the global delay field is given a higher spatial weight; when the monitoring point is located in a mountainous area or when the meteorological conditions fluctuate significantly, the local delay field is given a higher spatial weight.

[0078] For each monitoring point, the corresponding slant path delay in the global and local delay fields is weighted and summed according to the corresponding spatial weight to determine the corresponding fused delay value. The fused delay values ​​of each monitoring point are then integrated to obtain the fused delay field covering the monitoring area.

[0079] For each spatial coordinate in the multi-source fusion data after third-order compensation, the fusion delay value at the corresponding time point is extracted, and the impact of the delay on the spatial coordinate is calculated based on the corresponding signal propagation direction. The spatial coordinate is then corrected based on the impact value.

[0080] Although the corrected multi-source fusion data has eliminated hardware, environmental, and atmospheric errors, it is still essentially a discrete observation point and cannot directly reflect the continuous dynamics of conductor galloping. Therefore, by further combining historical galloping patterns and conductor physical parameters, the conductor galloping trajectory is solved based on the corrected multi-source fusion data to ensure the accuracy of the conductor galloping trajectory.

[0081] The process of combining historical galloping patterns and conductor physical parameters, and solving the conductor galloping trajectory based on corrected multi-source fusion data, includes: A galloping compensation model is constructed based on catenary theory. With the goal of minimizing residuals, the model parameters of the galloping compensation model are iteratively optimized by combining historical galloping patterns and conductor physical parameters. The corrected multi-source fusion data is input into the optimized galloping compensation model to reconstruct the conductor galloping trajectory.

[0082] As a flexible suspension structure, the static suspension shape of the conductor can be represented by the catenary equation, which is specifically determined by the conductor's span, tension, and linear density. Conductor galloping essentially involves superimposing dynamic offsets onto a static catenary reference, such as amplitude, frequency, and phase in the vertical, horizontal, or torsional directions. Therefore, a galloping compensation model is constructed using a combination of the static catenary reference and dynamic galloping offset terms.

[0083] The residual is the difference between the conductor position predicted by the galloping compensation model and the actual position in the historical galloping data. The parameter constraints in the optimization iteration process are set by historical galloping patterns and conductor physical parameters. The model parameters of the galloping compensation model are optimized iteratively by least squares method or gradient descent algorithm.

[0084] In the initial stage of optimization iteration, historical catenary data, such as discrete observation points of typical catenary events, are substituted into the model. The residual between the predicted value of the model and the historical data is calculated. The dynamic parameters are adjusted according to the direction of the residual gradient, and the residual is recalculated. This process is repeated until the residual is less than the preset threshold or the parameter change is less than the convergence criterion. Finally, the catenary compensation model with optimized parameters is obtained, ensuring that the obtained catenary compensation model conforms to the physical laws of catenaries and fits the actual catenary characteristics of the monitoring area.

[0085] The corrected multi-source fusion data is input into the optimized galloping compensation model. Through the static benchmark and dynamic offset structure of the model, the time gaps between the corrected multi-source fusion data are filled to reconstruct the conductor galloping trajectory.

[0086] After acquiring the conductor galloping trajectory, the trajectory features are further extracted, including galloping amplitude, galloping frequency, trajectory shape, and parameter abrupt changes. A safety assessment is then performed based on these extracted features. If the trajectory features exceed the corresponding safety threshold, an abnormal galloping situation is identified, and an alarm is issued.

[0087] Another aspect of this embodiment also provides a conductor galloping monitoring system based on BeiDou radar spatiotemporal calibration, such as... Figure 3 As shown, it includes: Data acquisition module 1 is used to collect BeiDou positioning data and radar ranging data for each monitoring point within the monitoring area; Data processing module 2 is used to fuse the collected BeiDou positioning data and radar ranging data by combining spatiotemporal calibration, and to perform error compensation and error correction on the fused multi-source data through third-order galloping compensation and dual-grid fusion correction. The conductor galloping monitoring module 3 is used to combine historical galloping patterns and conductor physical parameters to solve the conductor galloping trajectory based on the corrected multi-source fusion data.

[0088] The conductor galloping monitoring module also includes: Risk alarm unit 31 is used to perform a safety assessment based on the conductor galloping trajectory and issue an early warning based on the safety assessment results.

[0089] The data acquisition module includes a BeiDou monitoring terminal, a millimeter-wave radar, and an environmental sensor. The BeiDou monitoring terminal is a BeiDou receiver that can acquire carrier phase observation data of the monitoring point.

[0090] Both the data processing module and the conductor galloping monitoring module are data processing components with corresponding algorithm programs, such as microprocessors and MCUs.

[0091] The data processing module has built-in algorithms for multi-source data fusion and error compensation correction, and is equipped with interfaces that can access external data platforms to retrieve the required data.

[0092] The conductor galloping monitoring module has built-in algorithms for reconstructing conductor galloping trajectories and also has interfaces for accessing external data platforms to retrieve the required data.

[0093] The conductor galloping monitoring module also includes a risk alarm unit, which can perform a safety assessment of the conductor galloping situation and issue an alarm in a timely manner.

[0094] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Other variations and modifications are possible without departing from the technical solutions described in the claims.

Claims

1. A method for monitoring conductor galloping based on Beidou radar space-time calibration, characterized in that, The method comprises the following steps: Synchronously collecting Beidou positioning data and radar ranging data, combining time and space calibration to obtain multi-source fusion data of conductor galloping; Compensating the multi-source fusion data in layers by three-order galloping compensation; According to the double-grid fusion correction, the multi-source fusion data after three-order compensation is corrected for atmospheric delay error; Combining historical galloping patterns and conductor physical parameters, the conductor galloping trajectory is solved according to the corrected multi-source fusion data.

2. The conductor galloping monitoring method based on Beidou radar space-time calibration according to claim 1, characterized in that, The method of synchronously collecting Beidou positioning data and radar ranging data, combining time and space calibration to obtain multi-source fusion data of conductor galloping comprises the following steps: Collecting Beidou positioning data and radar ranging data, and respectively preprocessing the Beidou positioning data and the radar ranging data; Combining the static basic data of the monitoring area to establish time and space error equations, and solving to obtain a time and space calibration matrix; According to the time and space calibration matrix, the Beidou positioning data and the radar ranging data are time and space calibrated, and the Beidou positioning data and the radar ranging data are associated according to the corresponding time stamp to obtain the multi-source fusion data of conductor galloping.

3. The conductor galloping monitoring method based on Beidou radar space-time calibration according to claim 1, characterized in that, The method of compensating the multi-source fusion data in layers by three-order galloping compensation comprises the following steps: According to the pre-calibration parameters of the data acquisition equipment and the temperature data of the monitoring area, the hardware error parameters are calculated, and the multi-source fusion data is compensated for hardware error according to the hardware error parameters; According to the meteorological data, geographical data and historical climate data of the monitoring area, the environmental error parameters are calculated, and the multi-source fusion data after first-order hardware error compensation is compensated for environmental error; According to the performance parameters of the data acquisition equipment, the motion state information and the motion constraint conditions of the monitoring area, the kinematic error parameters are calculated, and the multi-source fusion data after second-order environmental error compensation is compensated for kinematic error.

4. The conductor galloping monitoring method based on Beidou radar space-time calibration according to claim 3, characterized in that, The method of calculating hardware error parameters according to the pre-calibration parameters of the data acquisition equipment and the temperature data of the monitoring area, and compensating the multi-source fusion data for hardware error according to the hardware error parameters comprises the following steps: According to the pre-calibration parameters of the data acquisition equipment, the device fixed time delay value, the phase center offset and the multi-channel signal delay deviation value are obtained; According to the device fixed time delay value and the multi-channel signal delay deviation value, the time synchronization deviation of the multi-source fusion data is corrected, and the spatial coordinates of the multi-source fusion data are calibrated according to the phase center offset; According to the pre-calibration parameters of the data acquisition equipment, the temperature error coefficient and the calibration temperature are obtained, and the temperature drift is calculated combining the temperature data of the monitoring point; The measurement value deviation of the multi-source fusion data is compensated according to the temperature offset.

5. The conductor galloping monitoring method based on Beidou radar space-time calibration according to claim 3, characterized in that, The method of calculating environmental error parameters according to the meteorological data, geographical data and historical climate data of the monitoring area, and compensating the multi-source fusion data after first-order hardware error compensation for environmental error comprises the following steps: According to the meteorological data and geographical data of the monitoring area, the atmospheric delay and the path error value are calculated respectively; Based on the historical climate data of the monitoring area, the environmental error compensation coefficient is calculated combining the atmospheric delay and the path error value, and the correction weight of the atmospheric delay and the path error value is set according to the environmental error compensation coefficient; Based on the atmospheric delay and the path error value, the spatial coordinates of the multi-source fusion data after first-order hardware error compensation are corrected combining the corresponding correction weight.

6. The conductor galloping monitoring method based on Beidou radar space-time calibration according to claim 3, characterized in that, The kinematic error parameters are calculated according to the performance parameters of the data acquisition device, the motion state information of the monitoring area and the motion constraint conditions, and the multi-source fusion data after the second-order environmental error compensation is kinematically error-compensated according to the kinematic error parameters, including: The dynamic response delay of the device is calculated according to the performance parameters of the data acquisition device, and the multi-source fusion data after the second-order environmental error compensation is time-corrected according to the dynamic response delay; The motion model deviation value and the physical constraint deviation value are calculated according to the motion state information and the motion constraint conditions of the monitoring area; The multi-source fusion data after time correction is spatially coordinate-corrected according to the motion model deviation value and the physical constraint deviation value.

7. The conductor galloping monitoring method based on Beidou radar space-time calibration according to claim 1, characterized in that, The multi-source fusion data after the third-order compensation is atmospherically delay error corrected according to the double-grid fusion correction, including: The original observation data and the historical climate data of the monitoring area are assimilated, and the zenith total delay of each grid point is calculated correspondingly; The grid zenith delay is interpolated to any position through spatial interpolation processing, and the global delay field is generated by combining the mapping function and error propagation analysis; The meteorological field of each monitoring point is reconstructed according to the meteorological data of the monitoring area, and the three-dimensional atmospheric refractive index field of each monitoring point is established according to the corresponding meteorological field; According to the three-dimensional atmospheric refractive index field of each monitoring point, the actual transmission path of the corresponding signal is iteratively calculated by combining the three-dimensional ray tracing algorithm, and the local delay field is constructed; The global delay field and the local delay field are spatially weighted and fused to obtain the fused delay field; The multi-source fusion data after the third-order compensation is atmospherically delay error corrected according to the fused delay field.

8. The conductor galloping monitoring method based on Beidou radar space-time calibration according to claim 1, characterized in that, The conductor galloping trajectory is solved according to the corrected multi-source fusion data by combining the historical galloping mode and the conductor physical parameters, including: A galloping compensation model is constructed based on the catenary theory, and the model parameters of the galloping compensation model are iteratively optimized by combining the historical galloping mode and the conductor physical parameters, with the minimum residual as the target; The corrected multi-source fusion data is input into the optimized galloping compensation model, and the conductor galloping trajectory is reconstructed.

9. The conductor galloping monitoring system based on Beidou radar space-time calibration, used for executing the conductor galloping monitoring method based on Beidou radar space-time calibration in any one of claims 1 to 8, characterized in that, It includes: The data acquisition module is used for acquiring Beidou positioning data and radar ranging data of each monitoring point in the monitoring area; The data processing module is used for data fusion of the acquired Beidou positioning data and radar ranging data by combining time and space calibration, and error compensation and error correction of the fused multi-source fusion data through third-order galloping compensation and double-grid fusion correction; The conductor galloping monitoring module is used for solving the conductor galloping trajectory according to the corrected multi-source fusion data by combining the historical galloping mode and the conductor physical parameters.

10. The conductor galloping monitoring system based on Beidou radar space-time calibration of claim 9, wherein, The conductor galloping monitoring module further includes: The risk alarm unit is used for safety evaluation according to the conductor galloping trajectory, and sends a warning according to the safety evaluation result.

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