Power transmission line monitoring point accurate positioning method and device based on beidou positioning

Abstract

The application relates to the field of satellite positioning technology and discloses a power transmission line monitoring point accurate positioning method and device based on Beidou positioning, which comprises the following steps: collecting original satellite observation data of a monitoring point through a Beidou positioning terminal, completing quality screening according to a carrier-to-noise ratio and a multipath error to obtain first positioning observation data; carrying out collaborative filtering combined with reference data of a preset Beidou reference station along a power transmission line to obtain second positioning observation data; determining an initial positioning coordinate according to the second positioning observation data and an accurate coordinate of the reference station; performing three-dimensional reconstruction on design drawing data and tower structure parameters of the power transmission line to obtain a three-dimensional space data model; and performing space positioning correction on the initial positioning coordinate and accurate space coordinates and structure parameters of the tower in the three-dimensional space data model to obtain a target accurate positioning coordinate; and the application can improve the efficiency of the power transmission line monitoring point accurate positioning based on Beidou positioning.

Description

Technical Field

[0001] This invention relates to the field of satellite positioning technology, and in particular to a method and device for precise positioning of power transmission line monitoring points based on BeiDou positioning. Background Technology

[0002] When traditional BeiDou positioning technology is applied to the positioning of monitoring points on power transmission lines, it lacks a sound quality control system for raw satellite observation data. It does not conduct precise screening based on core indicators such as carrier-to-noise ratio and multipath error, and cannot effectively eliminate low-quality observation data. At the same time, it does not optimize signal quality in conjunction with the geographical characteristics of the monitoring point. Satellite signals are easily affected by factors such as terrain obstruction and electromagnetic interference, making it difficult to guarantee the stability and purity of observation data. The quality of the basic data for positioning calculation has obvious defects.

[0003] The existing positioning scheme for transmission line monitoring points does not link with pre-set BeiDou reference stations along the line for collaborative filtering, which cannot effectively eliminate common errors such as ionospheric delay and tropospheric wet delay, making it difficult to suppress positioning deviations. Furthermore, it does not integrate transmission line design drawings and tower structural parameters to construct a three-dimensional spatial model, lacking a coordinate correction step based on the actual structure of the line. The positioning results calculated solely from satellite observation data are not accurate enough, the data processing flow is cumbersome, and the overall positioning efficiency cannot meet the application requirements of intelligent monitoring of transmission lines. Therefore, how to improve the accuracy and efficiency of transmission line monitoring points based on BeiDou positioning has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a method and apparatus for precise positioning of transmission line monitoring points based on BeiDou positioning, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a method for precise positioning of transmission line monitoring points based on BeiDou positioning, comprising: A1. Obtain the original satellite observation data deployed at the transmission line monitoring point through the Beidou positioning terminal, and perform quality screening on the original satellite observation data according to the carrier-to-noise ratio and multipath error in the original satellite observation data to obtain the first positioning observation data of the transmission line monitoring point; A2. Collect reference observation data and the precise coordinates of the reference stations corresponding to the reference observation data by pre-set Beidou reference stations along the transmission line, and perform collaborative filtering on the first positioning observation data and the reference observation data to obtain the second positioning observation data of the monitoring point of the transmission line. A3. Determine the initial positioning coordinates of the transmission line monitoring point based on the second positioning observation data and the precise coordinates of the base station; A4. Perform three-dimensional reconstruction of the design drawings and tower structure parameters of the transmission line to obtain a three-dimensional spatial data model of the transmission line; A5. The initial positioning coordinates are spatially corrected by comparing them with the precise spatial coordinates and structural parameters of the tower in the three-dimensional spatial data model to obtain the target precise positioning coordinates of the transmission line monitoring point.

[0006] In a preferred embodiment, the step of acquiring raw satellite observation data deployed at the transmission line monitoring point via a BeiDou positioning terminal, and then performing quality screening on the raw satellite observation data based on the carrier-to-noise ratio and multipath error to obtain the first positioning observation data of the transmission line monitoring point, includes: The Beidou positioning terminal collects raw satellite observation data streams deployed at power transmission line monitoring points in real time. The raw satellite observation data streams include satellite pseudorange, carrier phase, Doppler frequency shift, multipath error, and carrier-to-noise ratio. Tensor decomposition is performed on the geographical environmental characteristics of the transmission line monitoring points to obtain the observation environment weighting factors of the transmission line monitoring points; Based on the observation environment weighting factor, the carrier-to-noise ratio is subjected to environmental adaptive weighting to obtain the weighted signal quality index of the carrier-to-noise ratio. Nonlinear state inversion is performed on the spatiotemporal correlation characteristics of the multipath error to construct a dynamic threshold model for the multipath error. The multipath error is then compared with the dynamic threshold model to obtain the multipath effect elimination mark for the transmission line monitoring point. Based on the weighted signal quality index and the multipath effect elimination flag, low-quality observations in the original satellite observation data are eliminated to obtain the first positioning observation data of the transmission line monitoring point.

[0007] In a preferred embodiment, the weighted signal quality index is calculated using the following formula: ; In the formula, The weighted signal quality index is... The carrier-to-noise ratio is... The preset environmental modulation coefficient, The observation environment weighting factor is... For the natural constant An exponential function with base 0.

[0008] In a preferred embodiment, the step of collecting reference observation data and the precise coordinates of the reference stations corresponding to the reference observation data through preset BeiDou reference stations along the transmission line, and performing collaborative filtering on the first positioning observation data and the reference observation data to obtain the second positioning observation data of the transmission line monitoring point includes: By using BeiDou reference stations pre-set along the transmission line, reference observation data is collected, and the ionospheric delay parameter and tropospheric wet delay parameter in the reference observation data are reconstructed by covariance to obtain the reference augmentation data of the reference observation data. Based on the geometric relationship of the satellite signal propagation path, the first positioning observation data and the reference augmentation data are geometrically registered to establish a set of differential observation equation constraints between the first positioning observation data and the reference augmentation data; Based on the constraint set of the differential observation equation, correlation analysis is performed on the common error terms in the first positioning observation data to obtain the error state estimate of the transmission line monitoring point. Based on the error state estimate, residual compensation is performed on the first positioning observation data to obtain the second positioning observation data of the transmission line monitoring point.

[0009] In a preferred embodiment, the step of geometrically registering the first positioning observation data with the reference augmentation data based on the geometric relationship of the satellite signal propagation path, and establishing a set of differential observation equation constraints between the first positioning observation data and the reference augmentation data, includes: Based on the spatiotemporal reference parameters in the first positioning observation data and the reference augmentation data, the satellite ephemeris in the first positioning observation data and the reference station ephemeris in the reference augmentation data are jointly registered to obtain the virtual geometric topology relationship between the transmission line monitoring point and the Beidou reference station. Based on the virtual geometric topology, the carrier phase observation value in the first positioning observation data and the reference phase observation value in the reference augmentation data are linearized into the observation equation to construct a double-difference observation equation between the first positioning observation data and the reference augmentation data. The observation noise and orbital error in the double-difference observation equation are regularized to obtain the standardized error constraint conditions of the double-difference observation equation. The standardized error constraints are incorporated into the double-difference observation equations to obtain the set of differential observation equation constraints between the first positioning observation data and the reference augmentation data.

[0010] In a preferred embodiment, determining the initial positioning coordinates of the transmission line monitoring point based on the second positioning observation data and the precise coordinates of the base station includes: Based on the satellite ephemeris data corresponding to the observation epoch in the second positioning observation data, determine the spatial position coordinates of the Beidou satellite at the time of signal transmission; Map the pseudorange observation values ​​in the second positioning observation data to the spatial location coordinates using distance parameters to construct the observation distance set between the power transmission line monitoring point and the Beidou satellite; Using the spatial location of the base station corresponding to the transmission line monitoring point in the precise coordinates of the base station as a reference reference point, spatial geometric constraints are applied to the observation distances in the observation distance set to obtain the set of spatial offsets of the transmission line monitoring point relative to the reference reference point; The spatial coordinates of the reference point are superimposed with the spatial offset set to obtain the initial positioning coordinates of the transmission line monitoring point.

[0011] In a preferred embodiment, the step of performing three-dimensional reconstruction of the design drawings and tower structural parameters of the transmission line to obtain a three-dimensional spatial data model of the transmission line includes: Geographic coordinates of the towers, route information, and span information between towers are extracted from the design drawings of the transmission line to generate a planar layout dataset of the transmission line. The tower type, tower height, crossarm length, and insulator string hanging point position information of the transmission line towers are extracted from the tower structure parameters to generate the tower structure feature dataset. The geographical coordinates of the poles in the planar layout dataset are associated and bound with the structural feature information of the corresponding poles in the structural feature dataset to obtain the geometric entity description set of the poles; Based on the route information and the span information, the geometric entity description set is spatially topologically arranged to obtain the linear spatial structure set of the transmission line; The linear spatial structure set is spatially superimposed with the topographic features in the design drawing data, and the coordinate system of the superimposed spatial data is normalized to construct a three-dimensional spatial data model of the transmission line.

[0012] In a preferred embodiment, the step of spatially correcting the initial positioning coordinates with the precise spatial coordinates and structural parameters of the tower in the three-dimensional spatial data model to obtain the target precise positioning coordinates of the transmission line monitoring point includes: The initial positioning coordinates are matched with the precise spatial coordinates of the tower in the three-dimensional spatial data model to determine the target tower to which the transmission line monitoring point belongs, and the spatial constraint features corresponding to the transmission line monitoring point are extracted from the structural parameters of the target tower. Based on the aforementioned spatial constraint characteristics, calculate the spatial deviation between the initial positioning coordinates and the precise spatial coordinates of the target tower; Based on the spatial deviation, the initial positioning coordinates are corrected to obtain the corrected positioning coordinates of the transmission line monitoring point; The corrected positioning coordinates are spatially fused with the feature point coordinates in the structural parameters of the target tower, and the corrected positioning coordinates are finely adjusted to obtain the target accurate positioning coordinates of the transmission line monitoring point.

[0013] In a preferred embodiment, the formula for calculating the spatial deviation is as follows: ; In the formula, This refers to the spatial deviation. Let be the spatial vector of the initial positioning coordinates. The spatial vector representing the precise spatial coordinates of the target tower. The weight matrix constructed for the spatial constraint features, It is the transpose of the vector.

[0014] To address the aforementioned problems, the present invention also provides a precise positioning device for transmission line monitoring points based on BeiDou positioning, the device comprising: The observation data quality screening module is used to acquire raw satellite observation data deployed at the transmission line monitoring point through the Beidou positioning terminal, and to screen the raw satellite observation data according to the carrier-to-noise ratio and multipath error in the raw satellite observation data to obtain the first positioning observation data of the transmission line monitoring point. The reference station collaborative filtering module is used to collect reference observation data and the precise coordinates of the reference station corresponding to the reference observation data through the preset Beidou reference stations along the transmission line, and to perform collaborative filtering on the first positioning observation data and the reference observation data to obtain the second positioning observation data of the monitoring point of the transmission line. The initial positioning calculation module is used to determine the initial positioning coordinates of the transmission line monitoring point based on the second positioning observation data and the precise coordinates of the base station. The three-dimensional space reconstruction module is used to reconstruct the design drawings and tower structure parameters of the transmission line in three dimensions to obtain a three-dimensional space data model of the transmission line. The spatial positioning correction module is used to spatially correct the initial positioning coordinates with the precise spatial coordinates and structural parameters of the tower in the three-dimensional spatial data model to obtain the target precise positioning coordinates of the transmission line monitoring point.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention collects raw satellite observation data through a BeiDou positioning terminal, performs quality screening based on carrier-to-noise ratio and multipath error, optimizes the signal quality index by combining the geographical environmental characteristics of the monitoring points, and eliminates multipath interference, thereby obtaining pure and reliable first positioning observation data, laying a solid foundation of high-quality data for positioning calculation. By relying on pre-set BeiDou reference stations along the transmission line to conduct collaborative filtering, reconstructing ionospheric and tropospheric delay parameters, and establishing a differential observation equation constraint set, it can accurately compensate for observation data errors, quickly calculate accurate initial positioning coordinates, optimize the positioning calculation process, and significantly improve the efficiency and data stability of the positioning calculation stage.

[0016] 2. This invention utilizes transmission line design drawings and tower structural parameters to perform 3D reconstruction, constructing a complete 3D spatial data model. This provides precise tower spatial coordinates and structural parameters for positioning correction. Through spatial matching of initial positioning coordinates with the 3D model, deviation calculation, and fine-tuning of coordinates, initial positioning deviations can be eliminated, resulting in accurate positioning coordinates for the monitoring point target and significantly improving positioning accuracy. The entire method forms a complete technical closed loop encompassing data screening, collaborative filtering, initial calculation, 3D reconstruction, and spatial correction. Each step synergistically enhances efficiency, comprehensively optimizing the positioning processing flow. From data processing to coordinate correction, the entire chain improves operational efficiency, making transmission line monitoring point positioning more accurate and efficient, fully meeting the dual requirements of intelligent line monitoring for positioning accuracy and efficiency. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a method for precise positioning of transmission line monitoring points based on BeiDou positioning, according to an embodiment of the present invention. Figure 2 This is a functional module diagram of a precise positioning device for transmission line monitoring points based on BeiDou positioning provided in an embodiment of the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides a method for precise positioning of transmission line monitoring points based on BeiDou positioning. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for precise positioning of transmission line monitoring points based on BeiDou positioning can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating a method for precise positioning of transmission line monitoring points based on BeiDou positioning, according to an embodiment of the present invention. In this embodiment, the method for precise positioning of transmission line monitoring points based on BeiDou positioning includes: A1. Obtain the original satellite observation data deployed at the transmission line monitoring point through the Beidou positioning terminal, and perform quality screening on the original satellite observation data according to the carrier-to-noise ratio and multipath error in the original satellite observation data to obtain the first positioning observation data of the transmission line monitoring point; In this embodiment of the invention, the step of acquiring raw satellite observation data deployed at transmission line monitoring points via a BeiDou positioning terminal, and performing quality screening on the raw satellite observation data based on the carrier-to-noise ratio and multipath error in the raw satellite observation data to obtain the first positioning observation data of the transmission line monitoring points includes: The Beidou positioning terminal collects raw satellite observation data streams deployed at power transmission line monitoring points in real time. The raw satellite observation data streams include satellite pseudorange, carrier phase, Doppler frequency shift, multipath error, and carrier-to-noise ratio. Tensor decomposition is performed on the geographical environmental characteristics of the transmission line monitoring points to obtain the observation environment weighting factors of the transmission line monitoring points; Based on the observation environment weighting factor, the carrier-to-noise ratio is subjected to environmental adaptive weighting to obtain the weighted signal quality index of the carrier-to-noise ratio. Nonlinear state inversion is performed on the spatiotemporal correlation characteristics of the multipath error to construct a dynamic threshold model for the multipath error. The multipath error is then compared with the dynamic threshold model to obtain the multipath effect elimination mark for the transmission line monitoring point. Based on the weighted signal quality index and the multipath effect elimination flag, low-quality observations in the original satellite observation data are eliminated to obtain the first positioning observation data of the transmission line monitoring point.

[0021] The formula for calculating the weighted signal quality index is as follows: ; In the formula, The weighted signal quality index is... The carrier-to-noise ratio is... The preset environmental modulation coefficient, The observation environment weighting factor is... For the natural constant An exponential function with base 0.

[0022] The Beidou positioning terminal deployed at the power transmission line monitoring point continuously receives satellite signals. The signal acquisition unit inside the terminal samples and analyzes the satellite signals at a fixed time interval of 1 second, maintaining the continuity of signal reception during sampling. During analysis, the carrier signal and code signal of the satellite signal are separated and processed to extract satellite pseudorange, carrier phase, Doppler frequency shift, multipath error and carrier-to-noise ratio data respectively. All extracted data are uniformly stored in binary data format. After storage, the data is transmitted to the data processing center in real time through the wireless transmission module to form the raw satellite observation data stream that can be used for subsequent processing, ensuring that each data item in the data stream corresponds to a unique monitoring point location and acquisition time.

[0023] The geographical environmental features of the area where the transmission line monitoring points are located, such as terrain undulations, vegetation obstruction, height above ground, intensity of surrounding electromagnetic interference, and angle of obstruction by transmission line towers, are arranged and combined according to spatial and temporal dimensions to form a multi-dimensional data structure. The spatial dimension includes three coordinate parameters: longitude, latitude, and altitude of the monitoring point. The temporal dimension is divided into time nodes with a 24-hour cycle. When extracting features layer by layer from this multi-dimensional data structure, the basic data of a single environmental feature is extracted first. Then, the data of different environmental features are integrated through feature fusion. Subsequently, redundant data is removed through dimensional compression, and the feature data that directly affects satellite observation is retained. Finally, the degree of influence of different environmental features on satellite observation is quantified and allocated. The quantified numerical result is the observation environment weight factor of the transmission line monitoring point. The value range of this weight factor is controlled between 0.1 and 1.0. The greater the degree of influence, the closer the weight factor value is to 1.0.

[0024] The carrier-to-noise ratio (CNR) is derived from the raw satellite observation data stream collected in real time by the BeiDou positioning terminal. This terminal is deployed on the top of the tower at the power transmission line monitoring point and continuously receives satellite signals through its built-in satellite signal receiving antenna. The signal analysis module inside the terminal samples and analyzes the received satellite signals, with the sampling frequency consistent with the signal acquisition unit. During the analysis process, the signal-to-noise ratio of the satellite signal is calculated, and the CNR data is extracted. This data, together with satellite pseudorange, carrier phase, Doppler shift, and multipath error data, constitutes the raw satellite observation data stream. The CNR data of each monitoring point corresponds one-to-one with the corresponding acquisition time and monitoring point location, ensuring data traceability.

[0025] The observation environment weighting factor originates from the tensor decomposition process of the geographical environmental features of the transmission line monitoring points. Specifically, the geographical environmental features of the monitoring points, such as topographic relief, vegetation obstruction, surrounding electromagnetic interference intensity, height above the ground, and tower obstruction angle, are formed into a multi-dimensional data structure according to spatial and temporal dimensions. The spatial dimension is based on the coordinates of the monitoring points, and the temporal dimension is divided into hourly data collection nodes. Then, the influence data of each environmental feature is separated through a layer-by-layer feature extraction method, and redundant information irrelevant to satellite observation is removed. Subsequently, dimensional compression is performed to simplify the multi-dimensional data into a single-dimensional influence coefficient. The degree of influence of different environmental features on satellite observation is quantified and allocated. The stability of satellite signal transmission is used as the criterion for quantification. The environmental feature with greater influence on signal stability is assigned a higher quantification value. The final quantification value is the observation environment weighting factor. This factor is dynamically adjusted as the environment of the monitoring points changes to ensure that it matches the actual observation environment.

[0026] The preset environmental modulation coefficients are derived from historical data statistics and practical application experience summaries of BeiDou positioning monitoring of transmission lines. Specifically, by collecting observation environmental weight factors, carrier-to-noise ratios (CNRs) and corresponding actual signal quality data of different transmission line monitoring points and different geographical environments over the past 5 years, the CNR variation patterns corresponding to different environmental weight factors are statistically analyzed, the influence of environmental weight factors on CNR modulation is analyzed, and coefficient values ​​that best match the actual signal quality after weighting are selected. These coefficient values ​​are determined as fixed coefficients that can accurately match the transmission line monitoring scenario and are used as preset environmental modulation coefficients. These coefficient values ​​are fixed and can be directly applied to subsequent weighted calculations.

[0027] The exponential function with the natural constant e as its base is a commonly used form of exponential expression in mathematics. Its expression is fixed and requires no additional parameters, so it can be directly applied to formula calculations. The core function of this exponential function is to smoothly adjust the modulation amplitude of the observation environment weight factor on the carrier-to-noise ratio, avoid abrupt changes in the modulation of the carrier-to-noise ratio by the weight factor, ensure that the trend of the weighted signal quality index changes in line with the actual trend of the satellite signal quality, and ensure the accuracy of subsequent data processing.

[0028] The observation environment weighting factor is multiplied with the carrier-to-noise ratio (CNR) value in the original satellite observation data stream. During the multiplication process, precise matching is performed according to the monitoring point number and data acquisition time to ensure that the CNR value of each monitoring point at each acquisition time point corresponds one-to-one with the observation environment weighting factor calculated under the corresponding environment, without mismatch or omission. The result of the multiplication is the weighted signal quality index of the CNR. The larger the index value, the better the satellite signal quality, which can be directly used to determine the subsequent low-quality observation values.

[0029] The formula's relevance to the preceding content is clear. Its core function is to fulfill the technical requirement of "adaptively weighting the carrier-to-noise ratio (CNR) based on the observation environment weighting factor to obtain a weighted signal quality index." The formula uses the CNR from the original satellite observation data stream as the basic data, combines it with the observation environment weighting factor from the transmission line monitoring point, adjusts the influence of the weighting factor on the CNR using a preset environmental modulation coefficient, and then smooths the modulation effect through an exponential function to avoid abrupt changes in the modulation of the CNR by the weighting factor. Finally, it calculates the weighted signal quality index, perfectly echoing the requirement of adaptively weighting the CNR in the preceding text. It integrates the influence of geographical environmental characteristics on satellite signal quality into the calculation of the signal quality index, ensuring that the obtained weighted signal quality index accurately reflects the actual quality of satellite signals under different observation environments. This provides a precise basis for subsequent elimination of low-quality observations, ensuring the targeted and accurate nature of subsequent data processing.

[0030] Based on the spatial distribution of transmission line monitoring points and the order of data acquisition, the variation characteristics of multipath error at different locations and times are extracted. The spatial distribution is sorted by the longitude and latitude coordinates of the monitoring points, and the data acquisition times are arranged chronologically. During the extraction process, the multipath error value for each monitoring point and each acquisition time is recorded. The continuous variation trend of multipath error over time and space is reverse-engineered and reconstructed. By tracing the variation trajectory of multipath error, the normal variation range of multipath error under the current monitoring environment is determined. Based on this normal variation range, acceptable upper and lower limits for multipath error are set. The upper and lower limits are determined according to the current environment. Historical multipath error data of the monitoring environment is determined to form a dynamic threshold model for multipath error that can be dynamically adjusted. This model updates the threshold range in real time as the monitoring environment changes. The multipath error values ​​in the original satellite observation data stream are compared with the upper and lower limits of the dynamic threshold model. When the multipath error value is between the upper and lower limits, it is marked as a normal observation value. When the multipath error value exceeds the upper limit or falls below the lower limit, it is marked as an outlier that needs to be removed. After marking all multipath error values, the multipath effect removal marks for the transmission line monitoring points are obtained. Each mark corresponds to a specific data item in the original satellite observation data stream.

[0031] The weighted signal quality index is compared with a preset signal quality judgment value. The preset signal quality judgment value is determined based on the actual needs of BeiDou positioning monitoring of transmission lines and remains fixed. Observation data with a weighted signal quality index greater than or equal to the preset value are judged as valid data, while observation data with a weighted signal quality index less than the preset value are judged as invalid data. At the same time, combined with the multipath effect elimination mark, the original satellite observation data corresponding to the outliers marked as needing to be eliminated are removed synchronously. During the removal process, it is ensured that only invalid data and outliers are deleted, while all valid and normal observation data are retained. The remaining observation data after removing invalid data and outliers is the first positioning observation data of the transmission line monitoring point. This data can be directly used for subsequent transmission line positioning monitoring and analysis.

[0032] The beneficial effects are that by combining the actual geographical environment of the transmission line monitoring points to weight and optimize the quality of satellite observation signals, and by using the spatiotemporal variation characteristics of multipath error to construct a dynamic elimination standard, low-quality satellite observation data and data interfered with by multipath effects can be accurately removed. This effectively avoids positioning data deviations caused by environmental interference, improves the reliability and stability of positioning data of Beidou positioning terminals in transmission line monitoring scenarios, provides accurate basic observation data support for transmission line safety monitoring, ensures the orderly development of transmission line positioning monitoring work, and reduces the difficulty of investigating potential safety hazards in transmission lines.

[0033] A2. Collect reference observation data and the precise coordinates of the reference stations corresponding to the reference observation data by pre-set Beidou reference stations along the transmission line, and perform collaborative filtering on the first positioning observation data and the reference observation data to obtain the second positioning observation data of the monitoring point of the transmission line. In this embodiment of the invention, the step of collecting reference observation data and the precise coordinates of the reference stations corresponding to the reference observation data through preset BeiDou reference stations along the transmission line, and performing collaborative filtering on the first positioning observation data and the reference observation data to obtain the second positioning observation data of the transmission line monitoring point includes: By using BeiDou reference stations pre-set along the transmission line, reference observation data is collected, and the ionospheric delay parameter and tropospheric wet delay parameter in the reference observation data are reconstructed by covariance to obtain the reference augmentation data of the reference observation data. Based on the geometric relationship of the satellite signal propagation path, the first positioning observation data and the reference augmentation data are geometrically registered to establish a set of differential observation equation constraints between the first positioning observation data and the reference augmentation data; Based on the constraint set of the differential observation equation, correlation analysis is performed on the common error terms in the first positioning observation data to obtain the error state estimate of the transmission line monitoring point. Based on the error state estimate, residual compensation is performed on the first positioning observation data to obtain the second positioning observation data of the transmission line monitoring point.

[0034] The step of geometrically registering the first positioning observation data with the reference augmentation data based on the geometric relationship of the satellite signal propagation path, and establishing a set of differential observation equation constraints between the first positioning observation data and the reference augmentation data, includes: Based on the spatiotemporal reference parameters in the first positioning observation data and the reference augmentation data, the satellite ephemeris in the first positioning observation data and the reference station ephemeris in the reference augmentation data are jointly registered to obtain the virtual geometric topology relationship between the transmission line monitoring point and the Beidou reference station. Based on the virtual geometric topology, the carrier phase observation values ​​in the first positioning observation data and the reference phase observation values ​​in the reference augmentation data are linearized using the observation equations to construct a double-difference observation equation between the first positioning observation data and the reference augmentation data. The observation noise and orbital error in the double-difference observation equation are regularized to obtain the standardized error constraint conditions of the double-difference observation equation. The standardized error constraints are incorporated into the double-difference observation equations to obtain the set of differential observation equation constraints between the first positioning observation data and the reference augmentation data.

[0035] BeiDou reference stations are deployed along the transmission line at fixed intervals, with the interval controlled within 50 kilometers, to ensure that the signal coverage of each reference station is interconnected. The BeiDou reference stations continuously receive navigation signals broadcast by BeiDou navigation satellites through their built-in signal receiving modules, and simultaneously collect reference observation data including location information, time information, ionospheric related data, and tropospheric related data. The ionospheric delay parameters and tropospheric wet delay parameters in the reference observation data are correlated and matched according to the spatial location and observation time of the parameters. The corresponding weight values ​​are determined based on the variation amplitude of each parameter at different observation times. The two types of delay parameters are then recombined and arranged according to the weight values ​​to complete the covariance reconstruction processing, forming reference enhancement data that can improve the accuracy of the reference observation data.

[0036] By utilizing the unified time and space reference parameters in the first positioning observation data and the reference augmentation data uploaded by the transmission line monitoring point, the satellite ephemeris information recorded in the first positioning observation data and the reference station ephemeris information recorded in the reference augmentation data are matched one by one according to the same satellite number and observation time. The position coordinates and trajectory in the ephemeris information are synchronously calibrated so that the two types of ephemeris information are under the same spatiotemporal reference system. The position association between the transmission line monitoring point and the Beidou reference station in three-dimensional space is determined by the calibrated ephemeris information, forming a virtual geometric topology relationship between the transmission line monitoring point and the Beidou reference station.

[0037] Based on the spatial position association form determined by the virtual geometric topology, the carrier phase observation values ​​in the first positioning observation data and the reference phase observation values ​​in the reference augmentation data are mapped according to the corresponding satellite signal propagation paths. The variation law of the phase observation values ​​is linearly transformed based on the spatial position relationship to eliminate the influence of nonlinear changes in the phase observation values. A mathematical expression form that can reflect the correlation between the two types of observation values ​​is established, the observation equation is linearized, and a double-difference observation equation between the first positioning observation data and the reference augmentation data is constructed.

[0038] The observation noise data and orbital error data contained in the double-difference observation equation are extracted. The observation noise data is continuously smoothed according to the chronological order of the observation time, and the window length of the smoothing process is set to ten consecutive observation times. The orbital error data is trend-fitted according to the variation law of the satellite orbit, and the fitting accuracy is controlled at the millimeter level. The random fluctuation of the error data is reduced by smoothing and trend fitting, and regularization is completed. The allowable fluctuation range of the error data in the double-difference observation equation is determined, and the standardized error constraint conditions of the double-difference observation equation are formed.

[0039] The allowable error fluctuation range determined in the standardized error constraint is combined with the double-difference observation equation to limit the calculation results of the double-difference observation equation to within the allowable error fluctuation range. Abnormal calculation results that exceed the allowable error fluctuation range are eliminated, and the equation calculation results that meet the error constraint requirements are retained, forming a set of differential observation equation constraints between the first positioning observation data and the benchmark augmentation data.

[0040] Based on the constraint set of the differential observation equation, the common error terms such as ionospheric delay error, tropospheric delay error, and orbital error in the first positioning observation data are compared and analyzed one by one. The comparison and analysis is based on the error constraint values ​​in the constraint set of the differential observation equation to determine the actual deviation value of each common error term. The actual deviation values ​​of all common error terms are comprehensively calculated, and the calculation result is used as the error state estimate of the transmission line monitoring point.

[0041] Using the error state estimate as the basis for compensation, the error values ​​at the corresponding positions in the first positioning observation data are reversed and corrected. The correction magnitude is consistent with the error state estimate, eliminating the influence of common errors in the first positioning observation data, completing the residual compensation process, and obtaining the second positioning observation data that can truly reflect the location information of the transmission line monitoring points.

[0042] The beneficial effects are as follows: by reconstructing the covariance of the delay parameters of the BeiDou reference station observation data, the accuracy of the reference data is improved; by using the spatiotemporal reference to achieve joint registration of ephemeris to construct virtual geometric topological relationships, and then by linearization, regularization and constraint set fusion to form accurate differential observation constraints, the common errors in the transmission line positioning observation data can be accurately separated and compensated, effectively reducing the interference of factors such as ionospheric delay, tropospheric delay and orbital error on the positioning results, and improving the accuracy and stability of the transmission line monitoring point positioning data. At the same time, the entire processing flow is based on clear numerical standards and spatial relationships, which can ensure the reproducibility and reliability of the transmission line positioning monitoring process and meet the accuracy requirements of long-term stable monitoring of transmission lines.

[0043] A3. Determine the initial positioning coordinates of the transmission line monitoring point based on the second positioning observation data and the precise coordinates of the base station; In this embodiment of the invention, determining the initial positioning coordinates of the transmission line monitoring point based on the second positioning observation data and the precise coordinates of the reference station includes: Based on the satellite ephemeris data corresponding to the observation epoch in the second positioning observation data, determine the spatial position coordinates of the Beidou satellite at the time of signal transmission; Map the pseudorange observation values ​​in the second positioning observation data to the spatial location coordinates using distance parameters to construct the observation distance set between the power transmission line monitoring point and the Beidou satellite; Using the spatial location of the base station corresponding to the transmission line monitoring point in the precise coordinates of the base station as a reference reference point, spatial geometric constraints are applied to the observation distances in the observation distance set to obtain the set of spatial offsets of the transmission line monitoring point relative to the reference reference point; The spatial coordinates of the reference point are superimposed with the spatial offset set to obtain the initial positioning coordinates of the transmission line monitoring point.

[0044] The satellite ephemeris data corresponding to each observation epoch in the second positioning observation data is extracted. Based on the satellite orbit information and signal transmission time information recorded in the satellite ephemeris data, the spatial position of each satellite is calculated according to the orbital operation law of Beidou satellite. The calculation process is based on the three-dimensional spatial coordinate system with the Earth's center of mass as the origin. The calculation accuracy is controlled at the centimeter level. Finally, the spatial position coordinates of Beidou satellite at the signal transmission time are determined.

[0045] The pseudorange observations in the second positioning observation data are matched one by one with the satellite number according to the corresponding observation epoch. A one-to-one correspondence is established between the matched pseudorange observations and the spatial position coordinates of the same satellite signal transmission time. Based on the actual propagation path characteristics of the satellite signal from the transmission position to the monitoring point, the pseudorange observations are converted into distance values ​​that can reflect the actual spatial length. All the converted distance values ​​are combined to form the observation distance set between the transmission line monitoring point and the Beidou satellite.

[0046] The spatial location of the base station within the same monitoring section as the transmission line monitoring point in the precise coordinates of the base station is selected as the reference reference point. The coordinate accuracy of this reference point is controlled at the millimeter level. The observation distances are concentrated so that each observation distance is constrained by the three-dimensional spatial location of the reference reference point. The spatial location is limited by the fixed spatial orientation along the transmission line and the layout range of the monitoring points. The limited range extends outward from the reference reference point without exceeding one hundred meters. The position deviation value corresponding to each observation distance is calculated through the spatial location limitation. All position deviation values ​​are combined to form the spatial offset set of the transmission line monitoring point relative to the reference reference point.

[0047] The three-dimensional spatial coordinates of the reference point are split one by one according to the spatial coordinate axis direction. The offset values ​​of the spatial offset set corresponding to the coordinate axis direction are superimposed and merged with the coordinate values ​​of the reference point corresponding to the coordinate axis. The merging process strictly follows the spatial coordinate superposition rules. After each set of coordinates and offsets is merged, a new set of spatial coordinates is formed. All the merged spatial coordinates together form the initial positioning coordinates of the transmission line monitoring point.

[0048] The beneficial effects are that the spatial position coordinates of the satellite at the time of launch can be accurately calculated by using satellite ephemeris data, and the observation distance set constructed by combining pseudorange observation values ​​can truly reflect the spatial distance relationship between the monitoring point and the satellite. Using high-precision base station coordinates as a reference point for spatial geometric constraints can effectively eliminate abnormal deviations in the distance data. The initial positioning coordinates can be obtained by directly superimposing coordinates and offsets, which can simplify the positioning calculation process and ensure the continuity and consistency of positioning results. This provides reliable initial position information for transmission line monitoring points and lays a stable data foundation for subsequent positioning accuracy optimization and line status monitoring.

[0049] A4. Perform three-dimensional reconstruction of the design drawings and tower structure parameters of the transmission line to obtain a three-dimensional spatial data model of the transmission line; In this embodiment of the invention, the step of performing three-dimensional reconstruction of the design drawing data and tower structure parameters of the transmission line to obtain a three-dimensional spatial data model of the transmission line includes: Geographic coordinates of the towers, route information, and span information between towers are extracted from the design drawings of the transmission line to generate a planar layout dataset of the transmission line. The tower type, tower height, crossarm length, and insulator string hanging point position information of the transmission line towers are extracted from the tower structure parameters to generate the tower structure feature dataset. The geographical coordinates of the poles in the planar layout dataset are associated and bound with the structural feature information of the corresponding poles in the structural feature dataset to obtain the geometric entity description set of the poles; Based on the route information and the span information, the geometric entity description set is spatially topologically arranged to obtain the linear spatial structure set of the transmission line; The linear spatial structure set is spatially superimposed with the topographic features in the design drawing data, and the coordinate system of the superimposed spatial data is normalized to construct a three-dimensional spatial data model of the transmission line.

[0050] The coordinate annotations and line planning content contained in the transmission line design drawings are read. The geographic coordinate information corresponding to each tower is extracted sequentially according to the tower number marked on the drawings. The geographic coordinate information is recorded using the 2000 National Geodetic Coordinate System, and the coordinate recording accuracy is retained to six decimal places. The overall extension direction and segment turning angle of the transmission line are extracted simultaneously to form the line direction information. The horizontal straight distance between two towers is calculated according to the adjacent tower numbers to obtain the span information. The above three types of information are organized and stored in the order of tower number to form the planar layout dataset of the transmission line.

[0051] The relevant parameters of the tower structure marked in the transmission line design drawings are read, and the tower type information corresponding to each tower is extracted one by one according to the tower number. The tower type information distinguishes between types such as straight towers, tension towers, and angle towers. The vertical height value from the ground to the top of the tower is extracted as the tower height information, and the tower height value is recorded with an accuracy of centimeters. The length value of the outward extension of the crossarms on both sides of the tower is extracted as the crossarm length information. The three-dimensional relative position information of the connection point between the insulator string and the tower is extracted as the insulator string hanging point position information. The above four types of information are summarized and stored according to the tower number to form the tower structural feature dataset.

[0052] Using a unified tower number as the matching basis, the geographical coordinate information corresponding to each numbered tower in the planar layout dataset is combined one-to-one with the tower type information, tower height information, crossarm length information, and insulator string hanging point location information corresponding to the same numbered tower in the structural feature dataset. This creates a fixed association between the spatial position of each tower and its own structural attributes. All the tower information after the association is completed is combined together to obtain the geometric entity description set of the tower.

[0053] Based on the route information in the planar layout dataset, the overall arrangement direction is determined, and the spatial interval between adjacent towers is determined according to the span information. The tower information in the geometric entity description set is arranged sequentially according to the line extension order, and the spatial interval between adjacent towers is maintained in accordance with the span value. After the arrangement is completed, a continuous extending spatial structure combination is formed, resulting in the linear spatial structure set of the transmission line.

[0054] The topographic elevation data, surface undulation features, river and road distribution, and other topographic features recorded in the design drawings are extracted. The linear spatial structure set is placed in the same spatial range as the topographic features and its position is overlapped. Data with coordinate system differences after overlay are uniformly converted. All data are uniformly adopted to the 2000 National Geodetic Coordinate System. After conversion, the spatial position deviation between different data is eliminated. The topographic data with unified coordinates is integrated with the spatial structure data of the transmission line to construct a three-dimensional spatial data model of the transmission line.

[0055] The beneficial effects are that by accurately extracting layout and structural information from design drawings to form a standardized dataset, it is possible to ensure that the source of basic information for transmission lines is unified and complete. By associating and binding tower locations with structural features, it is possible to achieve the fusion of physical and spatial attributes. By combining the line route and span to complete the topological arrangement, it is possible to truly restore the actual layout of the transmission line. The three-dimensional spatial data model constructed after superimposing it with topographic features and unifying the coordinate system can fully reflect the spatial relationship between the transmission line and the surrounding terrain, providing a real and reliable data carrier for subsequent line monitoring status analysis, while improving the consistency and reproducibility of data application.

[0056] A5. The initial positioning coordinates are spatially corrected by comparing them with the precise spatial coordinates and structural parameters of the tower in the three-dimensional spatial data model to obtain the target precise positioning coordinates of the transmission line monitoring point.

[0057] In this embodiment of the invention, the step of spatially correcting the initial positioning coordinates with the precise spatial coordinates and structural parameters of the tower in the three-dimensional spatial data model to obtain the target precise positioning coordinates of the transmission line monitoring point includes: The initial positioning coordinates are matched with the precise spatial coordinates of the tower in the three-dimensional spatial data model to determine the target tower to which the transmission line monitoring point belongs, and the spatial constraint features corresponding to the transmission line monitoring point are extracted from the structural parameters of the target tower. Based on the aforementioned spatial constraint characteristics, calculate the spatial deviation between the initial positioning coordinates and the precise spatial coordinates of the target tower; Based on the spatial deviation, the initial positioning coordinates are corrected to obtain the corrected positioning coordinates of the transmission line monitoring point; The corrected positioning coordinates are spatially fused with the feature point coordinates in the structural parameters of the target tower, and the corrected positioning coordinates are finely adjusted to obtain the target accurate positioning coordinates of the transmission line monitoring point.

[0058] The formula for calculating the spatial deviation is as follows: ; In the formula, This refers to the spatial deviation. Let be the spatial vector of the initial positioning coordinates. The spatial vector representing the precise spatial coordinates of the target tower. The weight matrix constructed for the spatial constraint features, It is the transpose of the vector.

[0059] The formula for calculating spatial deviation can be applied to scenarios such as quantification of coordinate deviation of transmission line monitoring points, verification of tower affiliation, correction of monitoring point positioning coordinates, and high-precision position calibration. The initial positioning coordinates are compared with the precise spatial coordinates of each tower in the three-dimensional spatial data model. The straight-line distance between the initial positioning coordinates and the precise spatial coordinates of each tower in three-dimensional space is calculated. The tower with the smallest distance value, less than 50 meters, is identified as the matching object. In this way, spatial proximity matching is completed and the target tower to which the transmission line monitoring point belongs is determined. The spatial constraint range and position limitation features that match the installation position of the monitoring point are extracted from the structural parameters corresponding to the target tower, and the spatial constraint features corresponding to the transmission line monitoring point are obtained.

[0060] The spatial vector serving as the initial positioning coordinates is obtained by vector transformation of the initial positioning coordinates obtained in previous steps. The initial positioning coordinates are formed by superimposing the spatial coordinates of the reference point with the set of spatial offsets. Arranging the three-dimensional values ​​of the initial positioning coordinates in the order of east-west, north-south, and vertical directions yields the corresponding spatial vector. .

[0061] The spatial vector representing the precise spatial coordinates of the target tower is obtained by vector transformation of the target tower's precise spatial coordinates. The target tower is determined by spatial proximity matching between its initial positioning coordinates and the tower's precise spatial coordinates in the 3D spatial data model. The precise spatial coordinates of the target tower come from the 3D spatial data model, which is constructed by associating and binding the planar layout dataset and structural feature dataset, spatially arranging the topology, overlaying it with terrain features, and unifying the coordinate system. Arranging the 3D values ​​of these precise spatial coordinates in the order of east-west, north-south, and vertical directions forms the spatial vector corresponding to the precise spatial coordinates of the target tower. .

[0062] The weight matrix, constructed using spatial constraint features, is derived from the extracted spatial constraint features. These features are extracted from the structural parameters of the target tower and correspond to the spatial constraint range and location limitations of the monitoring points on the transmission line. During construction, weights are assigned based on the constraint strength in different directions within the spatial constraint features. When a monitoring point is installed near an insulator string hanging point, the constraint strength in the vertical direction is higher, and a higher weight is assigned accordingly. The weight values ​​are determined based on the limitations of the monitoring point installation location in the tower structural parameters, ensuring that the weight matrix accurately reflects the influence of the spatial constraint features on the positional deviation in each direction, thereby constructing a weight matrix that meets the constraint requirements. .

[0063] This is the transpose of a vector, designed to meet the requirements of formula calculations. It converts the difference vector between the spatial vector of the initial positioning coordinates and the spatial vector of the precise spatial coordinates of the target tower into a row vector. The transpose process involves converting the column elements of the original difference vector into row elements in sequence, ensuring that this difference vector can be used with the weight matrix. Perform reasonable matrix multiplication operations to ensure the rationality of the entire formula calculation process and the accuracy of the results.

[0064] As a spatial deviation, it is derived from the comprehensive calculation of the initial positioning coordinates, the precise spatial coordinates of the target tower, and the spatial constraint characteristics through this formula. The calculation result directly corresponds to the degree of three-dimensional spatial deviation between the initial positioning coordinates and the precise spatial coordinates of the target tower, and is the core quantitative basis for subsequent correction of the initial positioning coordinates.

[0065] The relevance of this formula to the preceding content lies in the fact that the preceding steps explicitly require calculating the spatial deviation between the initial positioning coordinates and the precise spatial coordinates of the target tower based on spatial constraint characteristics. This formula is precisely the method to achieve this calculation requirement. By converting the initial positioning coordinates and the precise spatial coordinates of the target tower into spatial vectors, and incorporating the influence of spatial constraint characteristics using a weight matrix, the formula integrates the positional deviations in various directions within three-dimensional space through vector transposition, matrix multiplication, and square root operations. This accurately quantifies the spatial deviation between the initial positioning coordinates and the precise spatial coordinates of the target tower, providing an accurate and quantifiable basis for subsequent correction of the initial positioning coordinates based on the spatial deviation. It perfectly connects the preceding steps of calculating the spatial deviation with the subsequent steps of correcting the initial positioning coordinates, ensuring the continuity and accuracy of the entire transmission line monitoring point positioning process. At the same time, it ensures that the calculation process of the spatial deviation conforms to the spatial constraint requirements set in the preceding text, guaranteeing the adaptability of the calculation results to the actual installation location of the monitoring point.

[0066] Based on the three-dimensional spatial range and positional constraints defined by the spatial constraints, the positional differences between the initial positioning coordinates and the precise spatial coordinates of the target tower in the east-west, north-south, and vertical directions are calculated respectively. The positional differences in the three directions are then integrated according to the actual spatial distribution to form a spatial deviation that reflects the degree of positional deviation.

[0067] Based on the spatial deviation, the initial positioning coordinates are adjusted in the opposite direction to the precise spatial coordinates of the target tower in each of the three coordinate axes. The adjustment range is consistent with the value of the spatial deviation in the corresponding direction. After the adjustment is completed, the positional deviation between the initial positioning coordinates and the target tower is eliminated, and the corrected positioning coordinates of the transmission line monitoring point are obtained.

[0068] The corrected positioning coordinates are spatially fused with the coordinates of characteristic points such as insulator string hanging points, crossarm ends, and tower leg nodes in the target tower structural parameters. Based on the coordinates of the characteristic points, the corrected positioning coordinates are finely adjusted dimension by dimension according to the spatial relationship corresponding to the actual installation position of the monitoring point. The fine-tuning range is controlled within ten centimeters, so that the final coordinates completely match the actual structural position of the tower, and the target accurate positioning coordinates of the transmission line monitoring point are obtained.

[0069] The beneficial effects are that by matching the initial positioning coordinates with the precise coordinates of the tower, the tower to which the monitoring point belongs can be quickly identified. By combining spatial constraint characteristics to calculate the spatial deviation, the positioning error can be quantified. Targeted correction of the initial coordinates can effectively improve the reliability of the monitoring point coordinates. Furthermore, by finely adjusting the target positioning coordinates after fusion with the tower feature point coordinates, the position of the monitoring point can be highly consistent with the actual physical structure of the transmission line, greatly improving the accuracy and practicality of the positioning results and providing accurate and reliable location information for the safety monitoring of transmission lines.

[0070] like Figure 2 The diagram shown is a functional block diagram of a precise positioning device for transmission line monitoring points based on BeiDou positioning provided in an embodiment of the present invention.

[0071] The BeiDou-based precise positioning device 100 for transmission line monitoring points described in this invention can be installed in electronic devices. Depending on the functions implemented, the BeiDou-based precise positioning device 100 may include an observation data quality screening module 101, a base station collaborative filtering module 102, an initial positioning calculation module 103, a three-dimensional spatial reconstruction module 104, and a spatial positioning correction module 105. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0072] In this embodiment, the functions of each module / unit are as follows: The observation data quality screening module 101 is used to acquire the original satellite observation data deployed at the transmission line monitoring point through the Beidou positioning terminal, and to screen the original satellite observation data according to the carrier-to-noise ratio and multipath error in the original satellite observation data to obtain the first positioning observation data of the transmission line monitoring point. The reference station collaborative filtering module 102 is used to collect reference observation data and the precise coordinates of the reference station corresponding to the reference observation data through the Beidou reference stations preset along the transmission line, and to perform collaborative filtering on the first positioning observation data and the reference observation data to obtain the second positioning observation data of the transmission line monitoring point. The initial positioning calculation module 103 is used to determine the initial positioning coordinates of the transmission line monitoring point based on the second positioning observation data and the precise coordinates of the base station. The three-dimensional space reconstruction module 104 is used to perform three-dimensional reconstruction of the design drawing data and tower structure parameters of the transmission line to obtain a three-dimensional space data model of the transmission line. The spatial positioning correction module 105 is used to perform spatial positioning correction between the initial positioning coordinates and the precise spatial coordinates and structural parameters of the tower in the three-dimensional spatial data model, so as to obtain the target precise positioning coordinates of the transmission line monitoring point.

[0073] In the several embodiments provided by this invention, it should be understood that the disclosed methods and apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0074] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0075] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0076] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0077] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application device that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended 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.

Claims

1. A method for precise positioning of transmission line monitoring points based on BeiDou positioning, characterized in that, The method includes: A1. Obtain the original satellite observation data deployed at the transmission line monitoring point through the Beidou positioning terminal, and perform quality screening on the original satellite observation data according to the carrier-to-noise ratio and multipath error in the original satellite observation data to obtain the first positioning observation data of the transmission line monitoring point; A2. Collect reference observation data and the precise coordinates of the reference stations corresponding to the reference observation data by pre-set Beidou reference stations along the transmission line, and perform collaborative filtering on the first positioning observation data and the reference observation data to obtain the second positioning observation data of the monitoring point of the transmission line. A3. Determine the initial positioning coordinates of the transmission line monitoring point based on the second positioning observation data and the precise coordinates of the base station; A4. Perform three-dimensional reconstruction of the design drawings and tower structure parameters of the transmission line to obtain a three-dimensional spatial data model of the transmission line; A5. The initial positioning coordinates are spatially corrected by comparing them with the precise spatial coordinates and structural parameters of the tower in the three-dimensional spatial data model to obtain the target precise positioning coordinates of the transmission line monitoring point.

2. The method for precise positioning of transmission line monitoring points based on BeiDou positioning as described in claim 1, characterized in that, The process involves acquiring raw satellite observation data deployed at transmission line monitoring points via a BeiDou positioning terminal, and then performing quality screening on the raw satellite observation data based on the carrier-to-noise ratio and multipath error to obtain the first positioning observation data for the transmission line monitoring points, including: The Beidou positioning terminal collects raw satellite observation data streams deployed at power transmission line monitoring points in real time. The raw satellite observation data streams include satellite pseudorange, carrier phase, Doppler frequency shift, multipath error, and carrier-to-noise ratio. Tensor decomposition is performed on the geographical environmental characteristics of the transmission line monitoring points to obtain the observation environment weighting factors of the transmission line monitoring points; Based on the observation environment weighting factor, the carrier-to-noise ratio is subjected to environmental adaptive weighting to obtain the weighted signal quality index of the carrier-to-noise ratio. Nonlinear state inversion is performed on the spatiotemporal correlation characteristics of the multipath error to construct a dynamic threshold model for the multipath error. The multipath error is then compared with the dynamic threshold model to obtain the multipath effect elimination mark for the transmission line monitoring point. Based on the weighted signal quality index and the multipath effect elimination flag, low-quality observations in the original satellite observation data are eliminated to obtain the first positioning observation data of the transmission line monitoring point.

3. The method for precise positioning of transmission line monitoring points based on BeiDou positioning as described in claim 2, characterized in that, The formula for calculating the weighted signal quality index is as follows: ； In the formula, The weighted signal quality index is... The carrier-to-noise ratio is... The preset environmental modulation coefficient, The observation environment weighting factor is... For the natural constant An exponential function with base 0.

4. The method for precise positioning of transmission line monitoring points based on BeiDou positioning as described in claim 1, characterized in that, The process involves collecting reference observation data and the precise coordinates of the reference stations corresponding to the reference observation data from pre-set BeiDou reference stations along the transmission line, and then performing collaborative filtering on the first positioning observation data and the reference observation data to obtain the second positioning observation data for the transmission line monitoring points, including: By using BeiDou reference stations pre-set along the transmission line, reference observation data is collected, and the ionospheric delay parameter and tropospheric wet delay parameter in the reference observation data are reconstructed by covariance to obtain the reference augmentation data of the reference observation data. Based on the geometric relationship of the satellite signal propagation path, the first positioning observation data and the reference augmentation data are geometrically registered to establish a set of differential observation equation constraints between the first positioning observation data and the reference augmentation data; Based on the constraint set of the differential observation equation, correlation analysis is performed on the common error terms in the first positioning observation data to obtain the error state estimate of the transmission line monitoring point. Based on the error state estimate, residual compensation is performed on the first positioning observation data to obtain the second positioning observation data of the transmission line monitoring point.

5. The method for precise positioning of transmission line monitoring points based on BeiDou positioning as described in claim 4, characterized in that, The step of geometrically registering the first positioning observation data with the reference augmentation data based on the geometric relationship of the satellite signal propagation path, and establishing a set of differential observation equation constraints between the first positioning observation data and the reference augmentation data, includes: Based on the spatiotemporal reference parameters in the first positioning observation data and the reference augmentation data, the satellite ephemeris in the first positioning observation data and the reference station ephemeris in the reference augmentation data are jointly registered to obtain the virtual geometric topology relationship between the transmission line monitoring point and the Beidou reference station. Based on the virtual geometric topology, the carrier phase observation value in the first positioning observation data and the reference phase observation value in the reference augmentation data are linearized into the observation equation to construct a double-difference observation equation between the first positioning observation data and the reference augmentation data. The observation noise and orbital error in the double-difference observation equation are regularized to obtain the standardized error constraint conditions of the double-difference observation equation. The standardized error constraints are incorporated into the double-difference observation equations to obtain the set of differential observation equation constraints between the first positioning observation data and the reference augmentation data.

6. The method for precise positioning of transmission line monitoring points based on BeiDou positioning as described in claim 1, characterized in that, The step of determining the initial positioning coordinates of the transmission line monitoring point based on the second positioning observation data and the precise coordinates of the base station includes: Based on the satellite ephemeris data corresponding to the observation epoch in the second positioning observation data, determine the spatial position coordinates of the Beidou satellite at the time of signal transmission; Map the pseudorange observation values ​​in the second positioning observation data to the spatial location coordinates using distance parameters to construct the observation distance set between the power transmission line monitoring point and the Beidou satellite; Using the spatial location of the base station corresponding to the transmission line monitoring point in the precise coordinates of the base station as a reference reference point, spatial geometric constraints are applied to the observation distances in the observation distance set to obtain the set of spatial offsets of the transmission line monitoring point relative to the reference reference point; The spatial coordinates of the reference point are superimposed with the spatial offset set to obtain the initial positioning coordinates of the transmission line monitoring point.

7. The method for precise positioning of transmission line monitoring points based on BeiDou positioning as described in claim 1, characterized in that, The process of reconstructing the three-dimensional design drawings and tower structural parameters of the transmission line to obtain a three-dimensional spatial data model of the transmission line includes: Geographic coordinates of the towers, route information, and span information between towers are extracted from the design drawings of the transmission line to generate a planar layout dataset of the transmission line. The tower type, tower height, crossarm length, and insulator string hanging point position information of the transmission line towers are extracted from the tower structure parameters to generate the tower structure feature dataset. The geographical coordinates of the poles in the planar layout dataset are associated and bound with the structural feature information of the corresponding poles in the structural feature dataset to obtain the geometric entity description set of the poles; Based on the route information and the span information, the geometric entity description set is spatially topologically arranged to obtain the linear spatial structure set of the transmission line; The linear spatial structure set is spatially superimposed with the topographic features in the design drawing data, and the coordinate system of the superimposed spatial data is normalized to construct a three-dimensional spatial data model of the transmission line.

8. The method for precise positioning of transmission line monitoring points based on BeiDou positioning as described in claim 1, characterized in that, The step of spatially correcting the initial positioning coordinates with the precise spatial coordinates and structural parameters of the tower in the three-dimensional spatial data model to obtain the target precise positioning coordinates of the transmission line monitoring point includes: The initial positioning coordinates are matched with the precise spatial coordinates of the tower in the three-dimensional spatial data model to determine the target tower to which the transmission line monitoring point belongs, and the spatial constraint features corresponding to the transmission line monitoring point are extracted from the structural parameters of the target tower. Based on the aforementioned spatial constraint characteristics, calculate the spatial deviation between the initial positioning coordinates and the precise spatial coordinates of the target tower; Based on the spatial deviation, the initial positioning coordinates are corrected to obtain the corrected positioning coordinates of the transmission line monitoring point; The corrected positioning coordinates are spatially fused with the feature point coordinates in the structural parameters of the target tower, and the corrected positioning coordinates are finely adjusted to obtain the target accurate positioning coordinates of the transmission line monitoring point.

9. The method for precise positioning of transmission line monitoring points based on BeiDou positioning as described in claim 8, characterized in that, The formula for calculating the spatial deviation is as follows: ； In the formula, This refers to the spatial deviation. Let be the spatial vector of the initial positioning coordinates. The spatial vector representing the precise spatial coordinates of the target tower. The weight matrix constructed for the spatial constraint features, It is the transpose of the vector.

10. A precise positioning device for transmission line monitoring points based on BeiDou positioning, characterized in that, The device for implementing the precise positioning method for transmission line monitoring points based on BeiDou positioning as described in claim 1 includes: The observation data quality screening module is used to acquire raw satellite observation data deployed at the transmission line monitoring point through the Beidou positioning terminal, and to screen the raw satellite observation data according to the carrier-to-noise ratio and multipath error in the raw satellite observation data to obtain the first positioning observation data of the transmission line monitoring point. The reference station collaborative filtering module is used to collect reference observation data and the precise coordinates of the reference station corresponding to the reference observation data through the preset Beidou reference stations along the transmission line, and to perform collaborative filtering on the first positioning observation data and the reference observation data to obtain the second positioning observation data of the monitoring point of the transmission line. The initial positioning calculation module is used to determine the initial positioning coordinates of the transmission line monitoring point based on the second positioning observation data and the precise coordinates of the base station. The three-dimensional space reconstruction module is used to reconstruct the design drawings and tower structure parameters of the transmission line in three dimensions to obtain a three-dimensional space data model of the transmission line. The spatial positioning correction module is used to spatially correct the initial positioning coordinates with the precise spatial coordinates and structural parameters of the tower in the three-dimensional spatial data model to obtain the target precise positioning coordinates of the transmission line monitoring point.

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