A line topology assisted verification method based on intelligent inspection application

Abstract

The application discloses a kind of line topological auxiliary verification methods based on intelligent inspection application, comprising: obtaining the spatial pose data of tower reference point collected by unmanned aerial vehicle at preset hovering point;Based on the spatial pose data, the reference point of the tower is determined in the intelligent edge terminal, and the relative position information between the reference point and the preset hovering point is obtained;The spatial coordinate data of the reference point corresponding to each of the two adjacent base towers is obtained, and based on the corrected pose coordinate of the two adjacent base towers, the horizontal span and the height difference are calculated, and the shape coefficient related to the wire characteristics is matched from the preset parameter library to solve the real electrical length between the two adjacent base towers.The application solves the problem of low verification accuracy and high algorithm dependence of line length caused by terrain elevation and wire sag in the prior art, and realizes high-precision, low-algorithm-cost line topological auxiliary verification.

Description

Technical Field

[0001] This invention belongs to the field of power distribution automation and intelligent inspection technology, specifically involving an auxiliary method for verifying the topology parameters of power distribution network lines, which is particularly suitable for application scenarios that use drones in conjunction with edge computing terminals to accurately measure line spans and electrical lengths. Background Technology

[0002] In 10kV distribution networks, accurate line topology records are a crucial data foundation for daily operation and maintenance management, line loss analysis, and fault location. Among these, the actual electrical length of the line between adjacent towers (i.e., the actual arc length of the conductor) is a core topology parameter that directly affects the accuracy of traveling wave fault location, the calculation of protection settings, and the accuracy of dynamic capacity expansion assessments.

[0003] Currently, the industry mainly relies on the following methods to verify the span and conductor length of power lines: (a) Theoretical estimation based on design drawings and ledgers Early practices involved directly using the design span from the line design phase as the actual operating span, or having maintenance personnel calculate the horizontal projection distance on a two-dimensional map based on the tower's latitude and longitude coordinates. This method only considered the X / Y axis coordinates in the horizontal plane, completely ignoring the impact of elevation differences caused by terrain undulations, and failing to account for the sag effect caused by the conductor's own weight. In plains areas, the error of this method was within an acceptable range; however, when applied to complex terrains such as mountains and hills, due to the significant elevation differences between adjacent towers, there was a large deviation between the two-dimensional projected distance and the actual three-dimensional electrical length, seriously affecting the usability of the ledger data.

[0004] (II) 3D Modeling Method Based on General UAV Aerial Survey and AI Vision

[0005] In recent years, with the rapid development of drone technology and computer vision algorithms, the industry has begun to use commercially available general-purpose drones equipped with visible light cameras to take aerial photos of the route from multiple angles. After acquiring massive amounts of image data, the data is transmitted back to the cloud or backend server. The three-dimensional point cloud model of the route corridor is then reconstructed using structure of motion restoration (SfM) or multi-view stereo vision (MVS) algorithms, and the length of the conductor is measured from the data.

[0006] However, this technical approach has revealed the following prominent problems in practical engineering applications: Elevation distortion caused by 2D simplification: Most existing aerial survey post-processing software approximates the traverse as a horizontal straight line by default when calculating span, only outputting the horizontal projected distance between two points, rather than considering the actual spatial distance of the Z-axis elevation difference. Even when the point cloud model has 3D coordinates, the measurement results still differ from the actual electrical length due to the lack of physical modeling of the traverse catenary morphology.

[0007] Scale drift and perspective distortion: When general-purpose UAVs perform multi-waypoint inspections, the hovering position, pitch angle, and yaw angle of each flight over a tower are random, resulting in a lack of a unified scale benchmark for image data collected from different waypoints. During post-processing reconstruction, this random error introduces scale drift, distorting the absolute scale of the 3D model and directly affecting the absolute accuracy of line length measurements.

[0008] Heavy reliance on computing power, incompatible with edge computing: AI vision-based 3D reconstruction methods involve numerous iterative optimization operations such as image feature extraction, feature matching, and bundle adjustment, consuming extremely high computational resources and requiring support from high-performance servers or cloud computing power. Since inspection sites are often located in remote mountainous areas with unstable 4G / 5G signal coverage, the upload bandwidth for massive amounts of image data is limited, making real-time online processing difficult. Furthermore, if the computing tasks are entirely deployed on intelligent edge terminals (such as inspection tablets), the computing power of existing edge devices is far from sufficient to meet the real-time operation requirements of such algorithms.

[0009] Image recognition reliability is insufficient in complex environments: Transmission line corridors in mountainous areas are often densely vegetated with cluttered background features, making the pixel features of conductors easily confused with background elements such as tree branches, mountain shadows, and clouds. Conductor extraction algorithms based on convolutional neural networks show a significant drop in accuracy in such scenarios, frequently misidentifying tree branches as conductors or missing conductors altogether, leading to 3D reconstruction failures or measurement results that deviate severely from the true values.

[0010] (III) Direct Measurement Scheme Based on Sensors Installed on Towers

[0011] In a few scenarios, some solutions involve installing differential GPS receivers or laser rangefinders on the towers to directly obtain the relative positions between adjacent towers and the conductor sag. While this type of solution offers high accuracy, it requires dedicated hardware for each tower, resulting in high modification costs, complex construction, and heavy subsequent maintenance workload, making it unsuitable for widespread application in large-scale power distribution networks.

[0012] Therefore, there is an urgent need in this field for a line topology-assisted verification method that can take into account high accuracy, low computing power consumption, strong environmental adaptability, and no need to modify existing tower hardware, in order to solve the problem of accurate verification of the ledger of power distribution networks, especially mountain lines. Summary of the Invention

[0013] The purpose of this invention is to provide a line topology-assisted verification method based on intelligent inspection applications, so as to solve the problems of low accuracy, difficulty in achieving low-latency processing on intelligent edge terminals and poor adaptability mentioned in the background art.

[0014] To achieve the above objectives, the present invention provides the following technical solution: A method for line topology-assisted verification based on intelligent inspection applications includes the following steps: S1: Acquire the spatial pose data of the tower reference point collected by the UAV at the preset hovering point; S2: Based on the spatial pose data, determine the reference point of the tower in the intelligent edge terminal, and obtain the relative position information between the reference point and the preset hovering point; S3: Obtain the spatial coordinate data of the actual hovering points of two adjacent towers; when any of the actual hovering points deviates from the preset standard hovering point, compensate and correct the spatial coordinate data of the actual hovering point according to the deviation amount to obtain the corrected pose coordinates of the corresponding reference point. S4: If there are no obstacles, the preset standard hovering point is used as the actual hovering point, and the preset standard relative position vector is used as the actual relative position vector; S5: Substitute the horizontal span, the height difference, and the shape coefficient into the real line length model constructed based on the catenary equation to solve for the real electrical length of the line between the two adjacent towers.

[0015] As a preferred technical solution, the spatial pose data includes depth map data. Step S2, which determines the reference point of the tower based on the spatial pose data, specifically includes: In the depth matrix of the depth map, find the minimum depth value of each column to lock the vertical center line of the tower, determine the horizontal position and front-back distance of the reference point, and complete the alignment. A one-dimensional scan is performed from top to bottom along the vertical center generatrix in the depth matrix. When the crossarm position is scanned, the horizontally widened region in the horizontal direction is determined. The midpoint of the intersection of the horizontally widened region and the vertical center line is taken as the reference point.

[0016] As a preferred technical solution, before step S1, the following steps are also included: Based on the pre-stored GIS ledger information, an inspection task package containing a preset standard hovering point is generated for each tower; wherein, the preset standard hovering point is defined as a uniform relative position with a preset distance, preset azimuth angle and preset pitch angle relative to the reference point.

[0017] As a preferred technical solution, step S2, obtaining the relative position information between the reference point and the preset hovering point, specifically includes: Based on the spatial pose data, determine whether there are obstacles around the preset hovering point; If there are obstacles, a suboptimal hovering point is determined within the unobstructed safe area as the actual hovering point, and the actual relative position vector of the actual hovering point relative to the reference point is recorded. If there are no obstacles, the preset standard hovering point is used as the actual hovering point, and the corresponding standard relative position vector is recorded.

[0018] As a preferred technical solution, determining a suboptimal hovering point within an unobstructed safe area specifically includes: Based on the reference point, a breadth-first search is performed within the safe area of ​​the spatial point cloud to determine a safe window as the suboptimal hovering point.

[0019] As a preferred technical solution, the compensation and correction of the spatial coordinate data in step S3 specifically includes: When it is determined that the actual hovering point of any base tower deviates from the standard hovering point, the difference between the actual relative position vector of the actual hovering point relative to the reference point and the preset standard relative position vector is calculated to obtain the offset vector; Subtract the offset vector from the spatial coordinates of the actual hovering point to obtain the corrected pose coordinates of the reference point.

[0020] As a preferred technical solution, the calculation of horizontal span and height difference in step S4 specifically includes: Extract the corrected pose coordinates corresponding to two adjacent towers, and record them as the first coordinate and the second coordinate, respectively; Calculate the difference between the X-axis coordinate and the difference between the Y-axis coordinate of the first coordinate and the second coordinate in the horizontal direction, respectively, and calculate the horizontal span based on the difference between the X-axis coordinate and the Y-axis coordinate. The height difference is obtained by calculating the difference between the Z-axis coordinates of the first coordinate and the second coordinate in the vertical direction.

[0021] As a preferred technical solution, the shape factor related to the conductor characteristics is a preset shape factor for the catenary, which is obtained through the following method: Confirm the current conductor model, and match and retrieve the standard unit weight and construction horizontal tension corresponding to the conductor model from the preset parameter library; Divide the construction horizontal tension by the standard unit weight to obtain the preset coefficient of the catenary shape.

[0022] As a preferred technical solution, the calculation formula corresponding to the real line length model is as follows:

[0023] Where L is the actual electrical length of the line, and Δh is the elevation difference. The horizontal span, a is a preset coefficient for the shape of the catenary.

[0024] As a preferred technical solution, it also includes: Compare the actual electrical length of the line with the original length data in the pre-stored line ledger; If the comparison results exceed the preset threshold, a topology verification and correction report containing the updated line length and tower coordinates will be generated.

[0025] Compared with the prior art, the beneficial effects of the present invention are: First, by offloading depth map processing and catenary analysis calculations to the intelligent edge terminal, local offline closed-loop processing of line topology verification is achieved, avoiding the communication bandwidth dependence caused by the backhaul of massive image data to the cloud, and ensuring the continuous availability of inspection services in network blind spot scenarios.

[0026] Second, by using the relative position vector of the UAV relative to the tower reference point as a unified spatial reference for data acquisition, the scale drift and viewpoint distortion errors caused by the randomness of the hovering attitude at multiple waypoints are eliminated, the reliance of traditional 3D reconstruction schemes on high-precision image feature matching algorithms is avoided, and the spatial consistency between multi-source heterogeneous data is improved.

[0027] Third, by performing obstacle detection in the depth map space and adaptively searching for a safe window as a suboptimal hovering point, and combining this with the inverse compensation of the offset vector to restore the standardized pose coordinates, the problem of ineffective data collection when the preset hovering point is occluded in complex terrain is solved, ensuring the environmental adaptability and data integrity of the method in complex scenarios such as mountainous and forested areas.

[0028] Fourth, by substituting the horizontal span and elevation difference between adjacent towers into the real line length model constructed based on the catenary equation, quantitative compensation for the nonlinear deviations introduced by conductor self-weight sag and terrain difference is realized. This improves the length underestimation defect caused by the traditional two-dimensional projection method which only calculates the horizontal distance, and enhances the verification accuracy of the line electrical length ledger. Attached Figure Description

[0029] Figure 1 This is a flowchart of a line topology-assisted verification method based on intelligent inspection application according to the present invention; Figure 2This is a schematic diagram of a topology example of a power distribution line in a mountainous area, as shown in the embodiment. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] Please see Figure 1 and Figure 2 This invention provides a method for line topology-assisted verification based on intelligent inspection applications.

[0032] In this embodiment, the method is executed by a smart edge terminal, specifically an inspection tablet computer, with the drone serving only as a data collection node. The method includes steps S1 to S5.

[0033] Step S1: Obtain the spatial pose data of the tower reference point collected by the UAV at the preset hovering point.

[0034] Before step S1 is executed, a task pre-generation step is also included: based on the pre-stored GIS ledger information, an inspection task package containing preset standard hovering points is generated for each tower. The preset standard hovering point is defined as a uniform relative position with a preset distance, preset azimuth angle, and preset elevation angle relative to a reference point. The GIS ledger is downloaded to the intelligent edge terminal in advance when a network connection is available, so that even in a communication dead zone during the actual line inspection, the intelligent edge terminal can still call the existing ledger data and task package to perform inspection operations.

[0035] After the drone flies to the vicinity of the target tower, it hovers near a preset hovering point under the control of the intelligent edge terminal. If the drone has a built-in pose correction function, it will automatically fly to the preset hovering point; if the drone's performance does not support automatic pose correction, manual control guidance will be generated on the simulated view of the intelligent edge terminal, and the ground operator will manually control the drone to get as close as possible to the preset hovering point. After hovering in the target area, the drone only acquires a three-dimensional depth map of the current field of view and transmits it back to the intelligent edge terminal, which is the aforementioned spatial pose data.

[0036] Step S2: Based on spatial pose data, determine the reference point of the tower in the intelligent edge terminal, and obtain the relative position information between the reference point and the preset hovering point.

[0037] After receiving a single-frame depth map, the intelligent edge terminal performs a reference point anchoring operation within the same set of point cloud processing algorithms. This includes the following sub-steps: In the depth matrix of the depth map, find the minimum depth value in each column to lock the vertical center line of the tower. This line determines the horizontal position and distance of the reference point in space, thus completing the alignment.

[0038] A one-dimensional scan is performed from top to bottom in the depth matrix along the vertical center generatrix. When the crossarm position is scanned, a horizontal rectangular area, i.e., a horizontally widening area, will be scanned in the horizontal direction.

[0039] The midpoint of the intersection of this horizontally widened region and the vertical center line is taken as the final reference point P2, with coordinates as follows: .

[0040] Once the reference point is determined, the intelligent edge terminal projects the virtual coordinates of a preset standard hovering point into the 3D matrix of the depth map based on this reference point. The preset standard hovering point is defined as a uniform relative position hovering at a distance D from the reference point, with an azimuth angle of α and a pitch angle of β, denoted as the standard relative position vector.

[0041] Simultaneously, the system detects whether there are obstacle point clouds around the preset standard hovering point based on the depth map. If no obstacle is found, the preset standard hovering point is used as the actual hovering point, and the corresponding standard relative position vector is recorded.

[0042] If obstacles exist, a suboptimal hovering point is determined within the unobstructed safe area as the actual hovering point. Specifically, in the depth matrix of the depth map, a breadth-first search is performed outward from the reference point P2 to find the nearest safe window, i.e., the unobstructed area. After finding this safe window, its true 3D coordinates are extracted. This is used as the suboptimal hovering point. Then, the actual relative position vector of this actual hovering point relative to the reference point is calculated. And record the vector.

[0043] Step S3: Obtain the spatial coordinate data of the reference points corresponding to the two adjacent towers. When any spatial coordinate data has a deviation caused by the actual hovering point deviating from the standard hovering point, compensate and correct the spatial coordinate data to obtain the corrected pose coordinates.

[0044] The intelligent edge terminal extracts high-precision spatial data transmitted from two levels of towers. When the actual hovering point of a certain tower falls within a safe window found due to obstruction, its actual hovering point deviates from the preset standard hovering point, resulting in an offset. At this point, the actual relative position vector is calculated. Relative position vector relative to the preset standard The difference is used to obtain the offset vector. :

[0045] Then, the offset vector is subtracted from the RTK coordinates of the actual hovering point to obtain the corrected pose coordinates of the reference point, i.e.

[0046] By using this inverse spatial translation compensation, the displacement deviation caused by physical occlusion is corrected at the geometric level, and the theoretically standardized pose coordinates are restored.

[0047] Step S4: Based on the corrected pose coordinates of two adjacent towers, calculate the horizontal span and height difference, and obtain the shape coefficient related to the conductor characteristics from the preset parameter library.

[0048] Extract the corrected pose coordinates of two adjacent towers, and denote them as the first coordinate and the second coordinate, respectively. For example, the corrected coordinates of tower A are... The corrected coordinates of tower B are: .

[0049] Calculate the difference between the first and second coordinates along the horizontal X-axis. Difference with Y-axis coordinate Calculate the horizontal span based on Δx and Δy. Simultaneously, calculate the difference between the first and second coordinates along the Z-axis in the vertical direction. Thus, the elevation difference Δh is obtained.

[0050] The shape factor related to the conductor characteristics is the catenary shape preset factor a. After the system confirms the current conductor model, it matches and retrieves the standard unit weight q and construction horizontal tension T0 corresponding to that conductor model from the conductor parameter preset library, and then... The pre-set coefficient 'a' for the catenary shape is calculated.

[0051] Step S5: Substitute the horizontal span, height difference, and shape factor into the real line length model constructed based on the catenary equation to solve for the real electrical length of the line between two adjacent towers.

[0052] Substitute the horizontal span l, elevation difference Δh, and catenary shape preset coefficient a obtained in step S4 into the actual line length calculation formula:

[0053] Where L is the actual electrical length of the line, and Δh is the elevation difference. The horizontal span, The true electrical length L (i.e., curve arc length) is obtained by solving for the pre-set coefficients of the catenary shape, which compensate for elevation distortion and conductor sag. The processor of the intelligent edge terminal can complete this calculation at a speed of microseconds.

[0054] The following is a specific embodiment used to illustrate the technical solution of the present invention: Taking the topology verification of the power distribution line in the mountainous area as shown in the figure as an example, it is known that there is a significant elevation difference in this section, and the recorded conductor type is steel-cored aluminum stranded wire LGJ-120.

[0055] Confirm reference point: The line length of the two-stage towers shown in the figure needs to be measured. Issue prior constraint instructions: The hovering point must be located 5 meters directly to the side of the junction of the tower and crossarm, with a downward angle of 15 degrees (i.e., ...). (The data itself has no special meaning and is only used as an example.)

[0056] Node E Sampling: The drone flies to the drone hovering point on the low-altitude tower E, and the tablet records the absolute RTK coordinates of the drone at this time. .

[0057] Data collection at node F: When the drone flies to the vicinity of high-altitude tower F and finds that the original drone hovering point is obstructed, and a safe gap is found nearby, the tablet records the RTK coordinates of the drone at this time when it is hovering in the safe gap. as well as Then proceed to the originally planned drone hovering point. Restoration:

[0058] Background calculation: The system calculates based on... , Extract the horizontal physical clearance of the X / Y axes from the coordinate values. And the precise elevation difference along the Z-axis Substitute the above data into the actual line length calculation formula.

[0059] The processor can quickly (theoretically within microseconds) calculate the actual electrical length after compensating for elevation distortion (e.g., 61.8 meters). After the inspection task is completed, the tablet generates a topology verification and correction report including the verified line length and tower coordinates.

[0060] Before verification: The system had no actual span, and the line length was calculated based on a fuzzy span of 60 meters. After verification, it was updated to 61.8 meters. Although a 3% difference is not significant for a single-level tower, for distribution network lines that are long enough to reach hundreds of towers, the accumulated error can be quite large. This method can effectively reduce the error.

[0061] For example, such as Figure 2 As shown, the actual line length between towers #15 and #16 of a 10kV distribution line in a mountainous area needs to be measured: Assume that the catenary in this section has a pre-defined shape factor.

[0062] Coordinates of the reference point for tower #15 (node ​​A): , ,

[0063] Coordinates of the reference point for tower #16 (node ​​B): , , ; Find the horizontal distance :

[0064] Find the elevation difference :

[0065] Will , , Substitute into the solution formula:

[0066] Combining the square root of the sum of squares of the elevation differences:

[0067] After the calculation is completed, the intelligent edge terminal compares the actual electrical length of the line with the original length data in the pre-stored line ledger (see Table 1 below). If the comparison result exceeds a preset threshold, a topology verification and correction report containing the updated line length and tower coordinates is generated. In this embodiment, the original ledger in the system is recorded at a fuzzy interval of 100m (ignoring elevation difference and sag), and after verification, it is updated to 103.24m. This result is the actual physical electrical length that compensates for elevation distortion and conductor self-weight sag.

[0068] Table 1 Comparison of actual electrical length of the lines with the original length in the pre-stored line ledger.

[0069] After the inspection task is completed, the intelligent edge terminal generates a complete topology verification and correction report, which includes the verified line length and tower coordinates for maintenance personnel to refer to.

[0070] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for line topology-assisted verification based on intelligent inspection applications, characterized in that, Includes the following steps: S1: Acquire the spatial pose data of the tower reference point collected by the UAV at the preset hovering point; S2: Based on the spatial pose data, determine the reference point of the tower in the intelligent edge terminal, and obtain the relative position information between the reference point and the preset hovering point; S3: Obtain the spatial coordinate data of the actual hovering points of two adjacent towers; when any of the actual hovering points deviates from the preset standard hovering point, compensate and correct the spatial coordinate data of the actual hovering point according to the deviation amount to obtain the corrected pose coordinates of the corresponding reference point. S4: Based on the corrected pose coordinates of two adjacent towers, calculate the horizontal span and height difference, and obtain the shape coefficient related to the conductor characteristics from the preset parameter library; S5: Substitute the horizontal span, the height difference, and the shape coefficient into the real line length model constructed based on the catenary equation to solve for the real electrical length of the line between the two adjacent towers.

2. The method according to claim 1, characterized in that, The spatial pose data includes depth map data. Step S2, which determines the reference point of the tower based on the spatial pose data, specifically includes: In the depth matrix of the depth map, find the minimum depth value of each column to lock the vertical center line of the tower, determine the horizontal position and front-back distance of the reference point, and complete the alignment. A one-dimensional scan is performed from top to bottom along the vertical center generatrix in the depth matrix. When the crossarm position is scanned, the horizontally widened region in the horizontal direction is determined. The midpoint of the intersection of the horizontally widened region and the vertical center line is taken as the reference point.

3. The method according to claim 1, characterized in that, Before step S1, the following is also included: Based on the pre-stored GIS ledger information, an inspection task package containing a preset standard hovering point is generated for each tower; wherein, the preset standard hovering point is defined as a uniform relative position with a preset distance, preset azimuth angle and preset pitch angle relative to the reference point.

4. The method according to claim 1, characterized in that, Step S2, which involves obtaining the relative position information between the reference point and the preset hovering point, specifically includes: Based on the spatial pose data, determine whether there are obstacles around the preset hovering point; If there are obstacles, a suboptimal hovering point is determined within the unobstructed safe area as the actual hovering point, and the actual relative position vector of the actual hovering point relative to the reference point is recorded. If there are no obstacles, the preset standard hovering point is used as the actual hovering point, and the preset standard relative position vector is used as the actual relative position vector.

5. The method according to claim 4, characterized in that, The process of determining a suboptimal hovering point within an unobstructed safe area specifically includes: Based on the reference point, a breadth-first search is performed within the safe area of ​​the spatial point cloud to determine a safe window as the suboptimal hovering point.

6. The method according to claim 1, characterized in that, The compensation and correction of the spatial coordinate data described in step S3 specifically includes: When it is determined that the actual hovering point of any base tower deviates from the standard hovering point, the difference between the actual relative position vector of the actual hovering point relative to the reference point and the preset standard relative position vector is calculated to obtain the offset vector; Subtract the offset vector from the spatial coordinates of the actual hovering point to obtain the corrected pose coordinates of the reference point.

7. The method according to claim 1, characterized in that, The calculation of horizontal span and elevation difference in step S4 specifically includes: Extract the corrected pose coordinates corresponding to two adjacent towers, and record them as the first coordinate and the second coordinate, respectively; Calculate the difference between the X-axis coordinate and the difference between the Y-axis coordinate of the first coordinate and the second coordinate in the horizontal direction, respectively, and calculate the horizontal span based on the difference between the X-axis coordinate and the Y-axis coordinate. The height difference is obtained by calculating the difference between the Z-axis coordinates of the first coordinate and the second coordinate in the vertical direction.

8. The method according to claim 1, characterized in that, The shape factor related to the conductor characteristics is a preset shape factor for the catenary, which is obtained through the following method: Confirm the current conductor model, and match and retrieve the standard unit weight and construction horizontal tension corresponding to the conductor model from the preset parameter library; Divide the construction horizontal tension by the standard unit weight to obtain the preset coefficient of the catenary shape.

9. The method according to claim 1, characterized in that, The calculation formula corresponding to the real line length model is: Where L is the actual electrical length of the line, and Δh is the elevation difference. The horizontal span, a is a preset coefficient for the shape of the catenary.

10. The method according to claim 1, characterized in that, Also includes: Compare the actual electrical length of the line with the original length data in the pre-stored line ledger; If the comparison results exceed the preset threshold, a topology verification and correction report containing the updated line length and tower coordinates will be generated.

Images