Power transmission line observation environment error calibration method, system, equipment and medium

Abstract

The invention relates to the technical field of satellite navigation detection, in particular to a power transmission line observation environment error calibration method, system and device and a medium, and the method comprises the steps: collecting the original monitoring data of each satellite navigation system for a power transmission line; calculating an ionosphere-free combined residual error according to the original monitoring data of each satellite navigation system; establishing a target grid according to the position of the observation station antenna and the satellite observation angle, projecting the ionosphere-free combination residual error to the target grid according to the satellite observation angle of the observation station antenna, and establishing an ionosphere-free combination model according to a projection result; and calibrating the observation environment error of the power transmission line by using the ionosphere-free combination model. Therefore, the problems of limited applicability, insufficient representation capability of observation environment errors, relatively low error modeling accuracy and the like during processing of multi-system observation data in related technologies are solved.

Description

Technical Field

[0001] This application relates to the field of satellite navigation detection technology, and in particular to a method, system, equipment and medium for calibrating the error of the observation environment of power transmission lines. Background Technology

[0002] Transmission line towers are mostly located in areas prone to geological disasters, requiring high-precision monitoring of tower foundation deformation using the BeiDou satellite navigation system. However, due to the influence of the tower's metal structure and the complex surrounding environment, monitoring signals are prone to multipath effects, which are difficult to eliminate using conventional differential methods. Therefore, research on multipath error suppression methods in transmission line monitoring is of great significance.

[0003] Related techniques typically employ sidereal-day filtering or semi-celestial grid models to model and suppress multipath errors. However, under conditions of mixed observations of multiple systems, the applicability of these methods is limited when processing observation data from multiple systems in a unified manner, their ability to characterize observational environment errors is insufficient, and the accuracy of error modeling is low. Summary of the Invention

[0004] This application provides a method, system, equipment, and medium for calibrating the environmental error of transmission line observations, in order to solve the problems of limited applicability of related technologies when processing multi-system observation data, insufficient ability to characterize environmental errors, and low accuracy of error modeling.

[0005] The first aspect of this application provides a method for calibrating the observation environment error of a transmission line, comprising the following steps: collecting raw monitoring data of the transmission line from each satellite navigation system; calculating the ionospheric-free combination residual based on the raw monitoring data of each satellite navigation system; establishing a target grid based on the position of the station antenna and the satellite observation angle; projecting the ionospheric-free combination residual onto the target grid based on the satellite observation angle of the station antenna; establishing an ionospheric-free combination model based on the projection result; and calibrating the observation environment error of the transmission line using the ionospheric-free combination model.

[0006] Optionally, in one embodiment of this application, calculating the ionosphere-free combined residual based on the original monitoring data of each satellite navigation system includes: extracting the first frequency point observation value and the second frequency point observation value from the original monitoring data; linearly combining the first frequency point observation value and the second frequency point observation value according to the frequency square ratio to calculate the ionosphere-free combined observation value; calculating the model observation value corresponding to the ionosphere-free combined observation value; and calculating the ionosphere-free combined residual based on the model observation value and the reference observation value.

[0007] Optionally, in one embodiment of this application, the expression for the ionosphere-free composite residual is:

[0008] in, For ionosphere-free composite residuals, This refers to the observation environment errors without ionospheric combinations (including non-white noise components such as multipath and protocol model residual system errors). The noise measured is primarily white noise.

[0009] Optionally, in one embodiment of this application, establishing a target grid based on the position of the station antenna and the satellite observation angle includes: constructing a target space based on the position of the station antenna; dividing the target space into angles based on the satellite observation angle of the station antenna; discretizing the angle division results of the target space to establish at least one grid cell; and establishing a target grid based on the grid cell and a pre-set grid resolution.

[0010] Optionally, in one embodiment of this application, establishing an ionosphere-free combined model based on the projection results includes: establishing a set of parameters to be estimated for the grid cells of the target grid; obtaining smoothing constraints and predetermined weights of the smoothing constraints between adjacent grid cells; solving the set of parameters to be estimated based on the ionosphere-free combined residuals, smoothing constraints, and predetermined weights; and generating an ionosphere-free combined model based on the solution results of the set of parameters to be estimated.

[0011] Optionally, in one embodiment of this application, the observation environment error of the transmission line is calibrated using an ionosphere-free combined model, including: extracting a first frequency model and a second frequency model of the ionosphere-free combined model; discretizing the first frequency model and the second frequency model; generating an observation environment error lookup table based on the discretized model; and calibrating the observation environment error of the transmission line based on the observation environment error lookup table.

[0012] Optionally, in one embodiment of this application, the expression for the first frequency point model is:

[0013] in, The observation environment error for the first frequency observation. For the ionosphere-free combination model, The ionosphere-free combination factor. The ionosphere-free combination factor is, i.e. , , The ratio of the first frequency point frequency to the second frequency point frequency is... ; The expression for the second frequency point model is:

[0014] in, This represents the observation environment error for the second frequency point observation.

[0015] A second aspect of this application provides a system for calibrating the observation environment error of a transmission line, comprising: an acquisition module for acquiring raw monitoring data of the transmission line from each satellite navigation system; a calculation module for calculating the ionospheric-free combination residual based on the raw monitoring data of each satellite navigation system; an establishment module for establishing a target grid based on the position of the station antenna and the satellite observation angle, projecting the ionospheric-free combination residual onto the target grid based on the satellite observation angle of the station antenna, and establishing an ionospheric-free combination model based on the projection result; and a calibration module for calibrating the observation environment error of the transmission line using the ionospheric-free combination model.

[0016] Optionally, in one embodiment of this application, the calculation module is further configured to extract the first frequency point observation value and the second frequency point observation value from the original monitoring data; calculate the ionosphere-free combined observation value by linearly combining the first frequency point observation value and the second frequency point observation value according to the frequency square ratio; calculate the model observation value corresponding to the ionosphere-free combined observation value; and calculate the ionosphere-free combined residual based on the model observation value and the reference observation value.

[0017] Optionally, in one embodiment of this application, the expression for the ionosphere-free composite residual is:

[0018] in, For ionosphere-free composite residuals, This refers to the observation environment errors without ionospheric combinations (including non-white noise components such as multipath and protocol model residual system errors). The noise measured is primarily white noise.

[0019] Optionally, in one embodiment of this application, the establishment module is further configured to construct a target space based on the position of the station antenna; divide the target space into angles based on the satellite observation angle of the station antenna; discretize the angle division results of the target space to establish at least one grid cell; and establish a target grid based on the grid cell and a pre-set grid resolution.

[0020] Optionally, in one embodiment of this application, the establishment module is further configured to establish a set of parameters to be estimated for the grid cells of the target grid; obtain the smoothing constraints and predetermined weights of the smoothing constraints between adjacent grid cells; solve the set of parameters to be estimated based on the ionosphere-free combination residual, smoothing constraints and predetermined weights; and generate an ionosphere-free combination model based on the solution results of the set of parameters to be estimated.

[0021] Optionally, in one embodiment of this application, the calibration module is further used to extract the first frequency model and the second frequency model of the ionosphere-free combined model; to discretize the first frequency model and the second frequency model; to generate an observation environment error lookup table based on the discretized model; and to calibrate the observation environment error of the transmission line based on the observation environment error lookup table.

[0022] Optionally, in one embodiment of this application, the expression for the first frequency point model is:

[0023] in, The observation environment error for the first frequency observation. For the ionosphere-free combination model, The ionosphere-free combination factor. The ionosphere-free combination factor is, i.e. , , The ratio of the first frequency point frequency to the second frequency point frequency is... ; The expression for the second frequency point model is:

[0024] in, This represents the observation environment error for the second frequency point observation.

[0025] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the transmission line observation environment error calibration system method as described in the above embodiments.

[0026] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the transmission line observation environment error calibration method as described in the above embodiments.

[0027] Therefore, this application has the following beneficial effects: First, raw monitoring data of the transmission line from each satellite navigation system is collected, acquiring multi-source observation information from multiple systems to achieve full acquisition and complementary fusion of observation data, thereby improving data integrity. Second, ionospheric-free combined residuals are calculated based on the raw monitoring data of each satellite navigation system. By eliminating the influence of ionospheric delay, the accuracy and consistency of the observation data are improved, providing high-quality input data for the subsequent establishment of an ionospheric-free combined model. Then, a target grid is established based on the position of the station antennas and the satellite observation angles. The ionospheric-free combined residuals are projected onto the target grid according to the satellite observation angles of the station antennas, and an ionospheric-free combined model is established based on the projection results. Multi-system observations enhance grid coverage, improve grid fill rate, shorten modeling time, and improve model stability and accuracy. Finally, the ionospheric-free combined model is used to calibrate the observation environment errors of the transmission line, achieving effective compensation for environmental errors such as multipath errors, thereby improving the accuracy and reliability of transmission line monitoring results. This solves the problems of limited applicability of related technologies when processing multi-system observation data, insufficient characterization ability of observation environment errors, and low accuracy of error modeling.

[0028] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0029] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of the transmission line observation environment error calibration method according to an embodiment of this application; Figure 2 This is a flowchart illustrating the specific steps of the transmission line observation environment error calibration method according to an embodiment of this application; Figure 3 This is an example diagram of a transmission line observation environment error calibration system according to an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0030] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0031] The following description, with reference to the accompanying drawings, outlines a method, system, device, and medium for calibrating the error of a transmission line observation environment according to embodiments of this application. Addressing the issues mentioned in the background art, such as poor applicability under multi-system mixed observation conditions, low grid fill rate due to limitations in the number of visible satellites in a single system, leading to long modeling times and insufficient model accuracy, this application provides a method for calibrating the error of a transmission line observation environment. In this method, firstly, raw monitoring data of the transmission line from each satellite navigation system is collected, obtaining multi-source observation information from multiple systems to achieve full acquisition and complementary fusion of observation data, thereby improving data integrity. Secondly, the ionospheric-free combined residual is calculated based on the raw monitoring data of each satellite navigation system, eliminating the influence of ionospheric delay to improve the accuracy of the observation. The accuracy and consistency of the measured data provide high-quality input data for the subsequent establishment of an ionospheric-free combined model. Then, a target grid is established based on the location of the station antennas and the satellite observation angles. The ionospheric-free combined residuals are projected onto the target grid according to the satellite observation angles of the station antennas, and an ionospheric-free combined model is established based on the projection results. Multi-system observations enhance grid coverage, improve grid fill rate, shorten modeling time, and improve model stability and accuracy. Finally, the ionospheric-free combined model is used to calibrate the observation environment errors of transmission lines, achieving effective compensation for environmental errors such as multipath errors, thereby improving the accuracy and reliability of transmission line monitoring results. This solves the problems of limited applicability of related technologies when processing multi-system observation data, insufficient ability to characterize observation environment errors, and low accuracy of error modeling.

[0032] Specifically, Figure 1 This is a flowchart illustrating a method for calibrating the environmental error of a power transmission line observation provided in an embodiment of this application.

[0033] like Figure 1 As shown, the method for calibrating the observation environment error of this transmission line includes the following steps: In step S101, raw monitoring data of the transmission line from each satellite navigation system is collected.

[0034] In this application, the raw monitoring data refers to the uncorrected environmental error-free observation data generated by various satellite navigation systems received by the transmission line monitoring station.

[0035] Understandably, by collecting raw monitoring data of transmission lines from each satellite navigation system, multi-source observation information from monitoring stations under different satellite navigation systems can be obtained. Since different satellite navigation systems have different satellite distribution characteristics and signal frequencies, unified collection of observation data from multiple satellite navigation systems can effectively increase the number of observable satellites and improve the continuity and stability of the observation data.

[0036] This application's embodiments collect multi-system dual-frequency observation data (including pseudorange and carrier phase observations) from transmission line monitoring stations over continuous periods, such as BDS (BeiDou Navigation Satellite System), GPS (Global Positioning System), and Galileo (Galileo Navigation Satellite System), identifying common frequency points where frequencies overlap between systems. In this application, pseudorange represents the distance measurement from the satellite signal to the receiver; carrier phase observations represent the phase change of the arriving satellite carrier wavefront as measured by the receiver, used to more accurately determine the distance between the satellite and the receiver.

[0037] Specifically, assuming the monitoring station has been operating continuously and the environment is relatively stable (e.g., continuous observation for 3-7 days) and has the capability to receive multi-system dual-frequency observation data, this embodiment focuses on data acquisition for a BeiDou monitoring station on a power transmission tower located at a geological hazard site. During data acquisition, this embodiment collects raw observation data from multiple systems at the monitoring station, including pseudorange and carrier phase observations. Subsequently, common frequency signals with frequency overlap between systems are identified and uniformly categorized.

[0038] In step S102, the ionosphere-free combination residual is calculated based on the original monitoring data of each satellite navigation system.

[0039] In this application, the ionospheric-free composite residual represents the observation residual obtained after eliminating the first-order delay error of the ionosphere. This residual mainly includes multipath errors caused by the surrounding environment of the station and residual errors that were not completely eliminated by the precise positioning model.

[0040] Understandably, by calculating the ionospheric-free combined residuals based on the raw monitoring data of each satellite navigation system, the frequency relationship between dual-frequency observations can be used to construct the ionospheric-free combined observations, thereby effectively eliminating the impact of the first-order ionospheric delay error on satellite observation data. After eliminating the main sources of ionospheric error, the resulting residuals primarily reflect the multipath effect caused by the surrounding environment of the station and a small amount of residual error in the positioning model, enabling the observation residuals to more accurately characterize the impact of the observation environment on satellite signal propagation. Simultaneously, by calculating the ionospheric-free combined residuals separately for observation data from different satellite navigation systems, observation environment error information for multiple systems can be obtained, providing a reliable data foundation for subsequently constructing observation environment error models.

[0041] In one embodiment of this application, calculating the ionosphere-free combined residual based on the original monitoring data of each satellite navigation system includes: extracting the first frequency point observation value and the second frequency point observation value from the original monitoring data; linearly combining the first frequency point observation value and the second frequency point observation value according to the frequency square ratio to calculate the ionosphere-free combined observation value; calculating the model observation value corresponding to the ionosphere-free combined observation value; and calculating the ionosphere-free combined residual based on the model observation value and the reference observation value.

[0042] Wherein, the first frequency observation value is the satellite observation data obtained by the satellite navigation receiver on the first frequency signal, which in this application represents the carrier phase observation value or pseudorange observation value of the first frequency received by the monitoring station; the second frequency observation value is the satellite observation data obtained by the satellite navigation receiver on the second frequency signal, which in this application represents the carrier phase observation value or pseudorange observation value of the second frequency received by the monitoring station; the ionospheric-free combined observation value is the observation obtained by linearly combining the first frequency observation value and the second frequency observation value according to the frequency square ratio, which in this application represents the combined observation data used to eliminate the first-order delay error of the ionosphere; the reference observation value is based on precise single-point positioning. In this application, the theoretical observation values ​​calculated by the observation model represent the theoretical values ​​of the ionospheric-free combined observations calculated under known satellite orbits, satellite clock errors, and related model parameters. The model observation values ​​are the observations formed by the actual observation data received by the monitoring station. In this application, they represent the actual ionospheric-free combined observation values ​​obtained by combining the observation values ​​at the first frequency point and the observation values ​​at the second frequency point. The ionospheric-free combined residual is the difference between the model observation values ​​and the reference observation values. In this application, it represents the observation residual obtained after eliminating the first-order delay error of the ionosphere. The observation residual mainly includes the multipath error caused by the observation environment and the residual error that the precise positioning model has not completely eliminated.

[0043] It is understandable that by extracting the first and second frequency observations from the original monitoring data and linearly combining them according to the frequency square ratio to calculate the ionospheric-free combined observations, the influence of the first-order ionospheric delay error during satellite signal propagation on the observation data can be effectively eliminated. Based on this, by calculating the model observations corresponding to the ionospheric-free combined observations and comparing them with the reference observations to obtain the ionospheric-free combined residuals, the error components related to the satellite signal propagation environment in the observation data can be extracted. This allows the residuals to primarily reflect the multipath effect caused by the surrounding environment of the station and a small amount of residual positioning model error. Through the above processing, a reliable data foundation can be provided for subsequent modeling and calibration of observation environment errors.

[0044] First, in this embodiment of the application, the raw monitoring data is divided into two main frequency bands: The first frequency observation value is frequency point 1 (denoted as...). ): Includes GPS L1, Galileo E1, BDS-3 B1C (all with a center frequency of 1575.42 MHz); the second frequency point observation frequency is 2 (denoted as...). ): Includes GPS L5, GalileoE5a, and BDS-3 B2a (all with a center frequency of 1176.45 MHz).

[0045] In this application, the classified signals have similar multipath reflection characteristics under the same physical observation environment. Therefore, the differences between systems can be ignored for joint processing, providing basic data for subsequent calculation of ionospheric combined residuals and calibration of observation environment errors.

[0046] Secondly, this application's embodiments employ post-hoc precise ephemeris and clock error products to perform static precise single-point positioning processing on the raw monitoring data. The observation values ​​at the first and second frequencies are linearly combined according to the square of the frequency. Taking the phase observation equation as an example, it is expressed as follows:

[0047] in, These are phase observations of the ionosphere-free combination. This is the observation value at the first frequency point. This is the observation value at the first frequency point. For the carrier phase observation at frequency point 1, The carrier phase observation measurement is for frequency point 2.

[0048] By fixing the precise coordinates of the station and correcting for prior tropospheric delay, relativistic effects, phase entanglement, and antenna phase center deviation, the ionospheric-free combined residual is obtained.

[0049]

[0050] in, For ionosphere-free composite residuals, The geometric distance is calculated based on the positions of the satellite and the receiver. The protocol corrections include relativistic effects, phase entanglement, and tidal corrections. The speed of light in a vacuum. This is the receiver clock bias estimate. For tropospheric projection functions, This is the estimated tropospheric delay at the zenith of the monitoring station. The carrier phase wavelength at frequency point 1, These are estimated values ​​for the combined ambiguity parameters without an ionosphere.

[0051] In one embodiment of this application, the expression for the ionosphere-free composite residual is:

[0052] in, For ionosphere-free composite residuals, This refers to the observation environment errors without ionospheric combinations (including non-white noise components such as multipath and protocol model residual system errors). The noise measured is primarily white noise.

[0053] It is understandable that by establishing an expression for the ionospheric-free combined residuals, the sources of error in the ionospheric-free combined residuals can be clearly defined, making the residuals mainly composed of observation environment errors and measurement noise. This allows the systematic errors related to the observation environment to be separated from the observation residuals, providing a theoretical basis for the subsequent extraction and modeling of observation environment errors.

[0054] The ionosphere-free composite residuals in this application mainly include:

[0055] in, For ionosphere-free composite residuals, This refers to the observation environment errors without ionospheric combinations (including non-white noise components such as multipath and protocol model residual system errors). The noise measured is primarily white noise.

[0056] The multipath error at the first and second frequency points and Linear combination:

[0057] in, The expression is:

[0058] The expression is: .

[0059] In step S103, a target grid is established based on the position of the station antenna and the satellite observation angle. The ionospheric-free composite residual is projected onto the target grid based on the satellite observation angle of the station antenna. An ionospheric-free composite model is established based on the projection results.

[0060] In this application, the target grid is a spatially discretized grid established with the station antenna as the center. In this application, it represents the spatial structure of dividing the satellite observation angle into discrete grid cells to store the observation environment error. Projection is the process of mapping the ionospheric combination residual to the corresponding grid cell of the target grid according to the satellite observation angle. In this application, it represents the establishment of a correspondence between the observation residual and the spatial grid cell. The ionospheric combination model is an environmental error model generated based on the target grid cell parameters and smoothing constraints. In this application, it represents a mathematical model used to characterize the distribution of the ionospheric combination observation environment error under different satellite observation angles.

[0061] Understandably, by mapping the ionospheric-free composite residuals onto a target grid centered on the station antenna and modeling it, scattered, obstructed, or noise-affected observation data can be uniformly organized into a continuous and structured spatial model, thereby improving the spatial coverage and accuracy of observation environment errors. Even when the number of satellites visible is limited or some lines of sight are obstructed, reliable error information can still be obtained. At the same time, by utilizing the grid structure and the smoothing constraints of adjacent grids, the influence of noise on the model can be effectively suppressed, making the error model more stable and continuous.

[0062] In one embodiment of this application, establishing a target grid based on the position of the station antenna and the satellite observation angle includes: constructing a target space based on the position of the station antenna; dividing the target space into angles based on the satellite observation angle of the station antenna; discretizing the angle division results of the target space to establish at least one grid cell; and establishing a target grid based on the grid cell and a pre-set grid resolution.

[0063] In this application, the target space represents the spatial region reachable by satellite signals and is used for grid division and observation error mapping. Angle division is the process of dividing the target space according to elevation angle and azimuth angle, and in this application, it represents the operation steps of discretizing the observable space of the station antenna into several grid cells. The grid cell is the basic unit in the target grid and in this application, it represents the smallest angle region in the target space divided according to elevation angle and azimuth angle, and is used to store observation environment error information in the corresponding direction.

[0064] It is understandable that by establishing a target grid based on the location of the station antenna and the satellite observation angle, and projecting the ionospherically unaffected composite residuals onto the target grid, a systematic and structured representation of environmental errors within the space surrounding the monitoring station can be achieved. The embodiments of this application can uniformly map the observation residuals of different satellite systems to the same spatial reference frame, effectively increasing the amount of usable data within the grid cells.

[0065] This embodiment of the application establishes a target grid based on the position of the station antenna and the satellite observation angle, specifically including the following steps: First, a target space is constructed based on the location of the station antenna. In this application, the target space is a hemispherical region within the coverage area of ​​the station antenna, used to represent all possible satellite observation directions.

[0066] Secondly, the target space is divided angularly based on the satellite observation angles of the station antennas. Specifically, the elevation angle E is divided into latitude intervals, and the azimuth angle A is divided into longitude intervals, thus dividing the celestial sphere into several grid units.

[0067] Next, the obtained angular intervals are discretized to establish a set of grid cells. In this application, each grid cell corresponds to a direction interval on the hemisphere, and the grid resolution is determined by a pre-set angular interval d×d (e.g., 2°×2°).

[0068] Finally, the target grid is established using the set of grid cells and a preset resolution. To enhance data density, this embodiment treats signals from different satellite systems with the same center frequency as signals from the same source, and maps their ionospheric-free combination residuals to the same grid cell according to the satellite's elevation angle E and azimuth angle A, thereby completing the fusion of grids at common frequencies of multiple systems and improving the coverage and reliability of the grid.

[0069] In one embodiment of this application, establishing an ionosphere-free combined model based on the projection results includes: establishing a set of parameters to be estimated for the grid cells of the target grid; obtaining smoothing constraints and predetermined weights of the smoothing constraints between adjacent grid cells; solving the set of parameters to be estimated based on the ionosphere-free combined residuals, smoothing constraints and predetermined weights; and generating an ionosphere-free combined model based on the solution results of the set of parameters to be estimated.

[0070] Among them, the smoothing constraint is a constraint condition used to limit the variation of errors between adjacent grid cells. In this application, it represents a mathematical constraint to suppress noise and improve the continuity of the model. The set of parameters to be estimated is the set of environmental error values ​​of grid cells that need to be solved in the target grid. In this application, it is used to describe the observation environmental error of each grid cell.

[0071] It is understandable that by solving for the set of parameters to be estimated to generate an ionospheric-free combined model, the observation environment errors in all directions around the station can be accurately characterized as a continuous grid model, realizing a spatial distribution description of multipath and other systematic errors. The embodiments of this application can fully fit the actual observation errors using projection residuals, improving model accuracy and reliability. By introducing smoothing constraints, the influence of local noise and data gaps is suppressed, resulting in a ionospheric-free combined model with high spatial continuity and usability.

[0072] In one embodiment of this application, the residuals of ionosphere-free combined observations in precise positioning are mapped onto grid points to establish an observation environment error model, specifically including the following steps: First, define any line of sight in the target space. Environmental error As a fitting target. This represents the observation environment error in that direction and is a reference value required for subsequent grid fitting.

[0073] Subsequently, the target space is discretized into a target grid, and parameters to be estimated are set for each grid cell. This is used to describe the observation environment error along the direction of the grid cell. The ionospheric-free combined residuals of the extracted raw monitoring data from each satellite navigation system are then used. The observation elevation angle E and azimuth angle A of the satellite are projected onto the previously established target grid, so that each grid cell can obtain the corresponding observation data.

[0074] Next, smoothing constraints and predetermined weights are introduced between grid cells to overcome potential data gaps in some areas and suppress observation noise. Specifically, the parameters to be estimated in adjacent grid cells satisfy the following constraints:

[0075]

[0076] The constraint equations are assigned predetermined weights to limit the range of parameter variation, for example, the variation per degree is no more than 5cm. The specific constraint form and weights can be supplemented and adjusted according to the actual application.

[0077] Then, based on the projected ionosphere-free composite residuals, smoothing constraints, and predetermined weights, the set of parameters to be estimated for the grid cells is solved. The least squares method or other appropriate solution methods can be used to fit the entire grid to obtain a complete set of grid parameters.

[0078] Finally, an ionosphere-free composite model is generated based on the set of parameters to be estimated from the solved grid cells. In this embodiment, The observation environment error in each direction of the entire hemispherical space is represented by a discrete grid, where the solution parameters for each grid cell are... Used to describe the error in the direction of this unit.

[0079] For any line of sight Environmental error Available The grid point parameters are calculated. In this embodiment, Through the four neighboring grid points of the given grid cell and their estimated parameters. The weighted result can be expressed using bilinear interpolation or other appropriate interpolation methods. Taking bilinear interpolation as an example, the formula is as follows:

[0080] in, For the parameters to be estimated for the grid points, These are the interpolation coefficients. Direction of sight The node index of the grid cell.

[0081] thus, It provides a discrete parameter representation of the entire target grid, where the parameters to be estimated for each grid node are... Used to describe the observation environment error in the corresponding direction. For any line-of-sight direction. The environmental error in that direction can be calculated (using methods including but not limited to interpolation) based on the parameters of the neighboring grid points of the grid cell in which it is located. This enables the estimation of environmental errors in the observation of the target grid in any direction within the target space.

[0082] In step S104, the observation environment error of the transmission line is calibrated using the ionosphere-free combined model.

[0083] Understandably, by accurately calibrating the observation environment error of transmission line monitoring stations using an ionospheric-free combined model, multipath and systematic environmental errors related to satellite perspective can be effectively eliminated. The environmental error model obtained through calibration can be directly used to correct the original observation values, significantly reducing the fluctuation of the calculated time series.

[0084] In one embodiment of this application, the observation environment error of a transmission line is calibrated using an ionosphere-free combined model, including: extracting a first frequency model and a second frequency model from the ionosphere-free combined model; discretizing the first frequency model and the second frequency model; generating an observation environment error lookup table based on the discretized model; and calibrating the observation environment error of the transmission line based on the observation environment error lookup table.

[0085] The first frequency model is the observation environment error model of the first frequency point, which is inversely separated from the ionosphere-free combined model. In this application, it represents the multipath and residual model error distribution corresponding to the first frequency point. The second frequency model is the observation environment error model of the second frequency point, which is inversely separated from the ionosphere-free combined model. In this application, it represents the multipath and residual model error distribution corresponding to the second frequency point. The first and second frequency models are based on the assumption of the proportional relationship between the wavelength of the dual-frequency signal and the multipath effect. Using the linear combination coefficient matrix, the ionosphere-free combined environmental error obtained by modeling is inversely separated into independent environmental error models of each frequency point of the dual frequencies. The observation environment error lookup table is a data table established based on the first and second frequency models after discretization. It is used to record the environmental error values ​​under different satellite elevation angles and azimuth angles. In this application, it represents an environmental error mapping table that can be directly used to correct the observation values ​​of transmission lines in real time.

[0086] It is understood that the embodiments of this application can accurately separate and calibrate the spatial directional environmental error obtained by dual-frequency ionosphere-free combined residual modeling into an independent observation environmental error model for each frequency point. After generating an observation environmental error lookup table through discretization processing, the original observation values ​​can be quickly and accurately corrected during real-time monitoring, thereby significantly reducing the impact of multipath effects and residual model errors on the monitoring and positioning results of transmission lines.

[0087] Although this application's embodiments yield an ionosphere-free combined model, actual monitoring and calculations often require corrections for each frequency point. Based on the physical reflection mechanism, it is assumed that the phase multipath error is related to the signal wavelength. There exists a proportional relationship (or they are approximately equal), i.e., a scaling factor is introduced. :

[0088] Generally acceptable (Assuming the error is proportional to the number of weeks). Substituting this relationship into the formula for the ionosphere-free combined model:

[0089] Therefore, the environmental error model for the first frequency point can be calculated, which is the model for the first frequency point:

[0090] This leads to the environmental error model at the second frequency point, which is the second frequency point model:

[0091] Thus, this embodiment of the application completes the calibration of the environmental error for transmission line observation. Specifically, the environmental error model obtained above at the first frequency point is... Second frequency point environmental error model The target grid is discretized, and the environmental error values ​​corresponding to each grid node are indexed and stored according to the elevation angle E and azimuth angle A, thereby constructing an observation environmental error lookup table. In actual real-time calculation, the corresponding error correction value can be queried according to the satellite's observation elevation angle and azimuth angle to achieve rapid compensation for observation environmental errors.

[0092] In one embodiment of this application, the expression for the first frequency point model is:

[0093] in, The observation environment error for the first frequency observation. For the ionosphere-free combination model, The ionosphere-free combination factor. The ionosphere-free combination factor is, i.e. , , The ratio of the first frequency point frequency to the second frequency point frequency is... ; The expression for the second frequency point model is:

[0094] in, This represents the observation environment error for the second frequency point observation.

[0095] It is understood that the embodiments of this application precisely decompose the ionospheric-free combined model into independent observation environment errors for each frequency point, enabling the multipath and environmental errors at different frequencies to be quantified and corrected individually. The first frequency point model is solved directly from the combined model, and the second frequency point model is solved using a scaling factor. By associating with the first frequency point model, a systematic correlation of dual-frequency errors is achieved. This embodiment ensures rapid and accurate correction of the original observations at any frequency point during subsequent real-time monitoring, effectively reducing the impact of directional correlation errors and measurement noise, thereby improving the accuracy of transmission line geological disaster displacement monitoring.

[0096] Furthermore, in the embodiments of this application, after generating the observation environment error lookup table, during the subsequent real-time monitoring phase, for satellite observations at any epoch, the first frequency point model is retrieved based on the satellite's elevation angle and azimuth angle. Second frequency point model The model value.

[0097]

[0098]

[0099] Using the corrected observations and The calculation is performed (e.g., using real-time dynamic positioning technology or precise single-point positioning). By eliminating direction-related systematic environmental errors, the time series fluctuations of monitoring and positioning results caused by related errors will be significantly reduced, thus enabling more sensitive identification of millimeter-level geological disaster displacements of transmission towers.

[0100] In summary, the transmission line observation environment error calibration method of this application embodiment is as follows: Figure 2 As shown: In step S201, pseudorange and phase observations from the BeiDou / Global Navigation Satellite System are acquired. Specifically, satellite observation data for a continuous observation period is acquired using a satellite navigation receiver installed at the transmission line monitoring point. The observation data includes pseudorange and carrier phase observations from satellite navigation systems such as BDS, GPS, and Galileo, which are used to characterize the distance and phase information during satellite signal propagation and provide basic data for subsequent observation data processing.

[0101] In step S202, observations are extracted from common frequencies of BDS, GPS, and Galileo. Specifically, frequency point identification is performed on the pseudorange and phase observations obtained in step S201. Common frequency observation signals with the same or similar center frequencies are selected from different satellite navigation systems, and the pseudorange and phase observations corresponding to the common frequencies are uniformly extracted and organized for subsequent joint processing.

[0102] In step S203, precise single-point positioning of the ionospheric combination is performed using precise orbit and clock bias products. Specifically, the satellite precise orbit and satellite clock bias products for the corresponding observation period are acquired, and the observation values ​​of the first and second frequency points in the original monitoring data are linearly combined according to the square of the frequency. The ionospheric combination observation values ​​are then calculated using the precise single-point positioning method.

[0103] In step S204, the relevant processing for precise single-point localization of the ionospheric composite model continues. This step is a further continuation of step S203. Based on the preliminary localization solution, the model observations corresponding to the ionospheric composite observations are calculated, and the ionospheric composite residuals are calculated based on the model observations and the reference observations.

[0104] In step S205, an ionospheric-free combined model is established. Specifically, based on the observation data obtained in step S204, an ionospheric-free combined model related to the satellite observation direction is established to describe the error characteristics of the observation environment under different observation directions.

[0105] In step S206, the model coefficients are solved. Specifically, based on the established ionosphere-free combined model, the coefficients in the model are solved using observation data to obtain model parameters that characterize the variation characteristics of observation environment errors.

[0106] In step S207, a first frequency point model and a second frequency point model are constructed. Specifically, based on the model coefficients obtained in step S206 and combined with the proportional relationship between signals at different frequency points, a first frequency point model and a second frequency point model are constructed respectively, thereby obtaining the observation environment error model corresponding to different frequency points.

[0107] In step S208, the observation environment error is corrected to obtain the corrected real-time observation value. Specifically, during the actual observation process, the observation environment error correction value for the corresponding frequency point is obtained according to the first frequency point model and the second frequency point model, and the correction value is used to correct the real-time pseudorange and phase observation values ​​to obtain the corrected real-time observation value.

[0108] In summary, the transmission line observation environment error calibration method of this application starts from the original pseudorange and phase observation values ​​of the satellite navigation system, and through steps such as common frequency point extraction, precise single-point positioning processing without ionospheric combination, model establishment, and observation environment error correction, it realizes the calibration of transmission line observation environment errors and obtains corrected real-time observation values. It has the following advantages and beneficial effects: (1) High data utilization: By integrating the common frequency data of BDS, GPS and Galileo (such as 1575.42MHz), the defect of uneven distribution of single system data in the half-sky grid caused by the obstruction of transmission towers is overcome, and the observation time required for model establishment is significantly shortened.

[0109] (2) High calibration accuracy: The traditional pseudorange-carrier combination (which is greatly affected by pseudorange noise) is abandoned to calculate the pseudorange observation environment error. Instead, the high-precision single-point positioning non-ionospheric combination residual is directly used for modeling, which effectively removes ionospheric delay and satellite end error, ensuring that the extracted station environment error is pure.

[0110] (3) Strong model applicability: This application proposes a method for inversely recovering a single-frequency model from a non-ionospheric combined model, so that the calibration results can be directly applied to the original observation values, which is applicable to monitoring terminals of various non-combined or non-difference solution modes.

[0111] According to the transmission line observation environment error calibration method proposed in this application, firstly, the original monitoring data of the transmission line from each satellite navigation system is collected, and multi-source observation information is obtained from multiple systems to achieve full acquisition and complementary fusion of observation data, thereby improving data integrity; secondly, the ionospheric-free combination residual is calculated based on the original monitoring data of each satellite navigation system. By eliminating the influence of ionospheric delay, the accuracy and consistency of the observation data are improved, providing high-quality input data for the subsequent establishment of the ionospheric-free combination model; then, a target grid is established based on the position of the station antenna and the satellite observation angle. The ionospheric-free combination residual is projected onto the target grid according to the satellite observation angle of the station antenna, and the ionospheric-free combination model is established based on the projection result. By enhancing grid coverage through multi-system observation, the grid filling rate is improved, the modeling time is shortened, and the stability and accuracy of the model are improved; finally, the ionospheric-free combination model is used to calibrate the observation environment error of the transmission line, achieving effective compensation for environmental errors such as multipath, thereby improving the accuracy and reliability of the transmission line monitoring results. This solves the problems of limited applicability of related technologies when processing multi-system observation data, insufficient ability to characterize observation environment errors, and low accuracy of error modeling.

[0112] Next, with reference to the accompanying drawings, the system for calibrating the error of the power line observation environment according to the embodiments of this application is described.

[0113] Figure 3 This is a block diagram of the line observation environment error calibration system according to an embodiment of this application.

[0114] like Figure 3 As shown, the transmission line observation environment error calibration system 10 includes: an acquisition module 100, a calculation module 200, an establishment module 300, and a calibration module 400.

[0115] The system includes: an acquisition module 100 for acquiring raw monitoring data of the transmission line from each satellite navigation system; a calculation module 200 for calculating the ionospheric-free combination residual based on the raw monitoring data of each satellite navigation system; a modeling module 300 for establishing a target grid based on the position of the station antenna and the satellite observation angle, projecting the ionospheric-free combination residual onto the target grid based on the satellite observation angle of the station antenna, and establishing an ionospheric-free combination model based on the projection results; and a calibration module 400 for calibrating the observation environment error of the transmission line using the ionospheric-free combination model.

[0116] In one embodiment of this application, the calculation module 200 is further configured to extract the first frequency point observation value and the second frequency point observation value from the original monitoring data; to calculate the ionosphere-free combined observation value by linearly combining the first frequency point observation value and the second frequency point observation value according to the frequency square ratio; to calculate the model observation value corresponding to the ionosphere-free combined observation value; and to calculate the ionosphere-free combined residual based on the model observation value and the reference observation value.

[0117] In one embodiment of this application, the expression for the ionosphere-free composite residual is:

[0118] in, For ionosphere-free composite residuals, This refers to the observation environment errors without ionospheric combinations (including non-white noise components such as multipath and protocol model residual system errors). The noise measured is primarily white noise.

[0119] In one embodiment of this application, the establishment module 300 is further configured to construct a target space based on the position of the station antenna; divide the target space into angles based on the satellite observation angle of the station antenna; discretize the angle division results of the target space to establish at least one grid cell; and establish a target grid based on the grid cell and a pre-set grid resolution.

[0120] In one embodiment of this application, the establishment module 300 is further configured to establish a set of parameters to be estimated for the grid cells of the target grid; obtain the smoothing constraints and predetermined weights of the smoothing constraints between adjacent grid cells; solve the set of parameters to be estimated based on the ionosphere-free combination residual, smoothing constraints and predetermined weights; and generate an ionosphere-free combination model based on the solution results of the set of parameters to be estimated.

[0121] In one embodiment of this application, the calibration module 400 is further configured to extract a first frequency model and a second frequency model of the ionosphere-free combined model; discretize the first frequency model and the second frequency model; generate an observation environment error lookup table based on the discretized model; and calibrate the observation environment error of the transmission line based on the observation environment error lookup table.

[0122] In one embodiment of this application, the expression for the first frequency point model is:

[0123] in, The observation environment error for the first frequency observation. For the ionosphere-free combination model, The ionosphere-free combination factor. The ionosphere-free combination factor is, i.e. , , The ratio of the first frequency point frequency to the second frequency point frequency is... ; The expression for the second frequency point model is:

[0124] in, This represents the observation environment error for the second frequency point observation.

[0125] It should be noted that the foregoing explanation of the embodiment of the transmission line observation environment error calibration method also applies to the transmission line observation environment error calibration system of this embodiment, and will not be repeated here.

[0126] According to the transmission line observation environment error calibration system proposed in this application, firstly, the system collects raw monitoring data of the transmission line from each satellite navigation system, acquiring multi-source observation information from multiple systems to achieve full acquisition and complementary fusion of observation data, thereby improving data integrity. Secondly, the system calculates the ionospheric-free combined residual based on the raw monitoring data of each satellite navigation system, improving the accuracy and consistency of the observation data by eliminating the influence of ionospheric delay, and providing high-quality input data for the subsequent establishment of the ionospheric-free combined model. Then, a target grid is established based on the position of the station antenna and the satellite observation angle. The ionospheric-free combined residual is projected onto the target grid based on the satellite observation angle of the station antenna, and an ionospheric-free combined model is established based on the projection results. By enhancing grid coverage through multi-system observation, the grid filling rate is improved, the modeling time is shortened, and the model stability and accuracy are enhanced. Finally, the ionospheric-free combined model is used to calibrate the observation environment error of the transmission line, achieving effective compensation for environmental errors such as multipath, thereby improving the accuracy and reliability of the transmission line monitoring results. This solves the problems of limited applicability of related technologies when processing multi-system observation data, insufficient ability to characterize observation environment errors, and low accuracy of error modeling.

[0127] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0128] When the processor 402 executes the program, it implements the circuit observation environment error calibration method provided in the above embodiments.

[0129] Furthermore, electronic devices also include: Communication interface 403 is used for communication between memory 401 and processor 402.

[0130] The memory 401 is used to store computer programs that can run on the processor 402.

[0131] The memory 401 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0132] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0133] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0134] Processor 402 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0135] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for calibrating the error of the power line observation environment.

[0136] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0137] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0138] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0139] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0140] Those skilled in the art will understand that all or part of the steps of the methods implementing the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0141] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for calibrating the environmental error of transmission line observation, characterized in that, Includes the following steps: Collect raw monitoring data of each satellite navigation system for the power transmission lines; Calculate the ionosphere-free combined residual based on the original monitoring data of each satellite navigation system; A target grid is established based on the position of the station antenna and the satellite observation angle. The ionosphere-free composite residual is projected onto the target grid based on the satellite observation angle of the station antenna. An ionosphere-free composite model is established based on the projection result. The observation environment error of the transmission line is calibrated using the ionosphere-free combined model.

2. The method for calibrating the error of the transmission line observation environment according to claim 1, characterized in that, The calculation of the ionosphere-free combined residual based on the original monitoring data of each satellite navigation system includes: Extract the observation values ​​of the first frequency point and the second frequency point from the original monitoring data; The non-ionospheric combined observation value is calculated by linearly combining the first frequency point observation value and the second frequency point observation value according to the frequency square ratio. Calculate the model observations corresponding to the ionosphere-free combination observations, and calculate the ionosphere-free combination residuals based on the model observations and reference observations.

3. The method for calibrating the error of the transmission line observation environment according to claim 2, characterized in that, The expression for the ionosphere-free composite residual is: in, For ionosphere-free composite residuals, For observational environment errors without ionospheric assemblies, For measuring noise.

4. The method for calibrating the error of the transmission line observation environment according to claim 1, characterized in that, The process of establishing a target grid based on the position of the station antenna and the satellite observation angle includes: Construct the target space based on the location of the station antenna; The target space is divided into angles based on the satellite observation angle of the station antenna; The angular division results of the target space are discretized to establish at least one grid cell, and the target grid is established based on the grid cell and the preset grid resolution.

5. The method for calibrating the error of the transmission line observation environment according to claim 1, characterized in that, The step of establishing an ionosphere-free composite model based on the projection results includes: Establish the set of parameters to be estimated for the grid cells of the target grid; Obtain the smoothing constraints between adjacent grid cells and the predetermined weights of the smoothing constraints; Based on the ionosphere-free combined residual, the smoothing constraint, and the predetermined weight, the set of parameters to be estimated is solved, and the ionosphere-free combined model is generated based on the solution results of the set of parameters to be estimated.

6. The method for calibrating the error of the transmission line observation environment according to claim 1, characterized in that, The calibration of the observation environment error of the transmission line using the ionosphere-free combined model includes: Extract the first frequency point model and the second frequency point model of the ionosphere-free combined model; Discretize the first frequency point model and the second frequency point model; An observation environment error lookup table is generated based on the discretized model, and the observation environment error of the transmission line is calibrated based on the observation environment error lookup table.

7. The method for calibrating the error of the transmission line observation environment according to claim 6, characterized in that, The expression for the first frequency point model is: in, The observation environment error for the first frequency observation is... For an ionosphere-free composite model, The ionosphere-free combination factor. The ionosphere-free combination factor. This is the frequency ratio of the first frequency point to the second frequency point. The expression for the second frequency point model is: in, This represents the observation environment error for the second frequency point observation.

8. A system for calibrating the environmental error of a transmission line observation, characterized in that, include: The data acquisition module is used to collect raw monitoring data of each satellite navigation system on the power transmission line; The calculation module is used to calculate the ionosphere-free combined residual based on the original monitoring data of each satellite navigation system; A module is established to create a target grid based on the position of the station antenna and the satellite observation angle, project the ionosphere-free composite residual onto the target grid based on the satellite observation angle of the station antenna, and establish an ionosphere-free composite model based on the projection result; The calibration module is used to calibrate the observation environment error of the transmission line using the ionosphere-free combined model.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the transmission line observation environment error calibration method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, it implements the steps of the transmission line observation environment error calibration method as described in any one of claims 1-7.

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