A method, apparatus, and equipment for optimizing tower locations of power transmission lines.

Abstract

This invention provides a method, apparatus, and equipment for optimizing tower locations in transmission lines, comprising: acquiring forward-looking panchromatic images, backward-looking panchromatic images, backward-looking multispectral images, and laser elevation points of a preliminary tower location area; establishing a digital surface elevation model of the preliminary tower location area based on the forward-looking and backward-looking panchromatic images; filtering pixels in the digital surface model based on the backward-looking multispectral images to obtain a first local bare ground elevation model; correcting the first local bare ground elevation model based on the laser elevation points to obtain a second local bare ground elevation model; filtering points in the second local bare ground elevation model according to preset conditions to obtain multiple candidate points; determining the evaluation score of the candidate points according to a preset evaluation model; and determining the optimized tower location information based on the evaluation score of each candidate point. This solves problems commonly encountered in actual construction, such as steep slopes, locations at trench edges, and large excavation volumes in the preliminary tower location selection.

Description

Technical Field

[0001] This invention relates to the field of power equipment technology, and in particular to a method, apparatus and equipment for optimizing tower locations of transmission lines. Background Technology

[0002] When constructing power transmission lines in complex terrain areas such as mountains, hills, and gullies, the initial phase typically involves overall route selection and preliminary tower site selection. However, these preliminary tower site selections are largely based on macroscopic paths, crossing relationships, and corridor conditions, often failing to accurately reflect the micro-topographical features within a few tens of meters around the tower site. In actual construction, while the preliminary tower site selection may be reasonable overall, local issues such as steep slopes, location on gully edges, large excavation volumes, construction difficulties, and extensive vegetation clearing often arise. This necessitates repeated on-site surveys, site adjustments, and modifications, increasing the design cycle and construction costs. For example, two tower sites on the same line, a few meters apart, may both macroscopically meet span requirements and corridor avoidance requirements. However, one might be located on a gentle slope shoulder, while the other might be adjacent to a gully edge or slope break. The former's tower foundation platform is easier to manage, while the latter may require larger excavations, support treatment, and could even lead to slope stability issues. Therefore, power transmission line design requires not only macroscopic route selection but also local micro-topographical analysis and correction for the preliminary tower site selection.

[0003] If we still rely on existing methods such as ordinary DEM (digital surface model), two-dimensional remote sensing images, or human experience to conduct local micro-topography analysis and correction, it will be difficult to accurately depict the real bare land terrain around the tower site. If we rely heavily on field surveys or UAV surveys, although the accuracy is high, the efficiency is low and the scope is limited, which is not suitable for conducting batch evaluations of multiple tower sites in the early stage of transmission line design. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method, device and equipment for optimizing the tower location of transmission lines. By analyzing and correcting the local micro-topography, the actual bare land terrain around the tower location is accurately depicted, the initial tower location is optimized, and the problems that often occur in actual construction, such as the initial tower location being reasonable in the overall line but having local problems such as large slope, being located at the edge of a ditch, large excavation volume, construction difficulty, and large amount of vegetation clearing, are often encountered.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: A method for optimizing tower locations for power transmission lines, comprising: Acquire the frontal panchromatic image, rearal panchromatic image, rearal multispectral image, and laser elevation point of the initially selected tower site area; A digital surface elevation model of the initially selected tower location area is established based on the forward and rear panchromatic images. The pixels in the digital surface model are filtered based on the rear-view multispectral image to obtain a first local bare ground elevation model. The first local bare ground elevation model is corrected based on the laser elevation points to obtain the second local bare ground elevation model; Based on preset conditions, points in the second local bare land elevation model are filtered to obtain multiple candidate points; The evaluation score of the candidate point is determined according to the preset evaluation model; The optimized tower location information is determined based on the evaluation score of each candidate point.

[0006] Optionally, acquire the forward panchromatic image, rearward panchromatic image, rearward multispectral image, and laser elevation point of the initially selected tower location area, including: Acquire the original frontal panchromatic image, original rearal panchromatic image, original rearal multispectral image, and original laser elevation point of the initially selected tower site area; The original forward panchromatic image, the original rearward panchromatic image, and the original rearward multispectral image are corrected to obtain the forward panchromatic image, the rearward panchromatic image, and the rearward multispectral image under the same coordinate system and plane projection. The original laser elevation points are modified by removing outliers to obtain the laser elevation points.

[0007] Optionally, a digital surface elevation model of the initially selected tower location area is established based on the forward panchromatic image and the rearward panchromatic image, including: The front-view panchromatic image and the rear-view panchromatic image are subjected to epipolar resampling and stereo matching to obtain a disparity map; The image is interpolated to obtain a digital surface elevation model of the initially selected tower location area.

[0008] Optionally, the pixels in the digital surface model are filtered based on the rear-view multispectral image to obtain a first local bare ground elevation model, including: The normalized vegetation index was determined based on the rear-view multispectral imagery. Identify the non-surface protrusions in the digital surface model; Based on the normalized vegetation index and non-surface protrusion areas, determine the set of bare land candidate pixels and the set of occluded pixels in the digital surface model; The elevation information of the occluded pixel set is restored by performing surface elevation restoration on the elevation information of the bare land candidate pixel set to obtain the adjusted elevation information of the occluded pixel set. The first local bare land elevation model is determined based on the elevation information of the bare land candidate pixel set and the elevation information of the adjusted occluded pixel set.

[0009] Optionally, the first local bare land elevation model is corrected based on the laser elevation points to obtain a second local bare land elevation model, including: Based on the elevation information of each pixel in the laser elevation point and the first local bare ground elevation model, determine the elevation error and weight of each pixel; Based on the elevation error and weight, determine the elevation modification amount for each pixel; The elevation information of each pixel is adjusted according to the elevation modification amount to obtain the second local bare ground elevation model.

[0010] Optionally, points in the second local bare land elevation model are filtered according to preset conditions to obtain multiple candidate points, including: The second local bare ground elevation model is discretized according to the preset grid to obtain discrete points to be screened; The discrete points are filtered based on preset constraints such as slope, valley edge safety distance, restricted construction zone, and span variation to obtain multiple candidate points.

[0011] Optionally, the evaluation score of the candidate point is determined according to a preset evaluation model, including: Based on the preset earthwork cost model for the tower base platform, determine the earthwork cost of the candidate points for the tower base platform; Based on the pre-set slope stability risk cost model, determine the slope stability risk cost of the candidate points; Based on the preset surface roughness cost model, the surface roughness cost of the candidate points is determined. Based on the preset construction access cost model, determine the construction access cost of candidate points; Based on the preset vegetation clearing cost model, determine the vegetation clearing cost of candidate points; The evaluation score of the candidate point is determined based on the earthwork cost of the tower base platform, the slope stability risk cost, the surface roughness cost, the construction access cost, and the vegetation clearing cost.

[0012] Optionally, the optimized tower location information is determined based on the evaluation score of each candidate point, including: according to Determine the optimized tower location information; in, These are the final optimized tower location coordinates. Indicates candidate points, Find the candidate point that yields the minimum evaluation score.

[0013] A tower location optimization device for transmission lines, comprising: The acquisition module is used to acquire the foreground panchromatic image, the rearground panchromatic image, the rearground multispectral image, and the laser elevation point of the initially selected tower site area; The processing module is used to establish a digital surface elevation model of the initially selected tower location area based on the forward-looking panchromatic image and the rear-looking panchromatic image; to filter the pixels in the digital surface model based on the rear-looking multispectral image to obtain a first local bare ground elevation model; to correct the first local bare ground elevation model based on the laser elevation points to obtain a second local bare ground elevation model; to filter the points in the second local bare ground elevation model according to preset conditions to obtain multiple candidate points; to determine the evaluation score of the candidate points according to a preset evaluation model; and to determine the optimized tower location information based on the evaluation score of each candidate point.

[0014] The above-described solution of the present invention has at least the following beneficial effects: The above-described solution of the present invention acquires forward panchromatic images, backward panchromatic images, backward multispectral images, and laser elevation points of the initially selected tower site area; establishes a digital surface elevation model of the initially selected tower site area based on the forward and backward panchromatic images; filters the pixels in the digital surface model based on the backward multispectral images to obtain a first local bare land elevation model; and corrects the first local bare land elevation model based on the laser elevation points to obtain a second local bare land elevation model. This achieves local micro-topographic analysis and correction of the initially selected tower site area, precisely depicting the real bare land terrain around the tower site. Furthermore, the present invention filters points in the second local bare land elevation model according to preset conditions to obtain multiple candidate points; determines the evaluation score of the candidate points according to a preset evaluation model; and determines the optimized tower site information based on the evaluation score of each candidate point. Each pixel within the initial tower site area was screened, which solved problems that often occurred in actual construction, such as the initial tower site being reasonable in the overall line but having local large slopes, being located at the edge of a ditch, having a large amount of excavation, having construction difficulties, and having a large amount of vegetation clearing. Attached Figure Description

[0015] Figure 1 This is a schematic flowchart of an embodiment of the tower location optimization method for transmission lines of the present invention; Figure 2 This is a schematic diagram of the structure of the tower location optimization device for power transmission lines according to the present invention. Detailed Implementation

[0016] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0017] like Figure 1As shown, an embodiment of the present invention proposes a method for optimizing the tower location of a transmission line, comprising: Step 11: Obtain the forward panchromatic image, rear panchromatic image, rear multispectral image, and laser elevation point of the initially selected tower location area; Step 12: Establish a digital surface elevation model of the initially selected tower location area based on the forward panchromatic image and the rear panchromatic image; Step 13: Based on the rear-view multispectral image, the pixels in the digital surface model are filtered to obtain the first local bare ground elevation model; Step 14: Correct the first local bare ground elevation model based on the laser elevation points to obtain the second local bare ground elevation model; Step 15: Filter the points in the second local bare land elevation model according to preset conditions to obtain multiple candidate points; Step 16: Determine the evaluation score of the candidate point according to the preset evaluation model; Step 17: Determine the optimized tower location information based on the evaluation score of each candidate point.

[0018] This embodiment addresses the scenario where macroscopic alignment of the entire transmission line has been completed and preliminary tower locations have been determined. It focuses on refining and optimizing the surrounding area of ​​a single preliminary tower location. Specifically, it involves acquiring Gaofen-7 forward panchromatic, backward panchromatic, and backward multispectral images, along with laser elevation points, covering the area of ​​the preliminary tower location. Then, it performs local 3D reconstruction using the forward and backward stereo images to generate a local DSM (Digital Surface Model, representing the elevation of everything on the ground surface, i.e., the ground level + treetops + building tops). Next, it combines multispectral information to identify vegetation-covered areas and non-surface protrusions, and uses laser elevation points to correct local elevation deviations, ultimately restoring a local bare ground DEM (Digital Elevation Model) suitable for tower foundation construction analysis. The Model, or Digital Elevation Model, also known as the Second Local Bare Ground Elevation Model, only calculates the elevation of the bare ground, removing trees and houses, and only reflects the topographic relief (mountains, valleys, slopes). Then, a micro-movement search area is established with the initially selected tower location as the center. The candidate micro-movement points in the search area are screened by hard constraints and evaluated by multi-factor quantification. Finally, the candidate point with the best score is selected as the optimized tower location through a comprehensive objective function, and the relevant analysis results are finally output.

[0019] In some optional implementations, step 11 involves acquiring the foreground panchromatic image, the background panchromatic image, the background multispectral image, and the laser elevation point of the initially selected tower location area, including: Step 111: Obtain the original forward panchromatic image, original rear panchromatic image, original rear multispectral image, and original laser elevation point of the initially selected tower location area; Step 112: Correct the original forward panchromatic image, the original rearward panchromatic image, and the original rearward multispectral image to obtain the forward panchromatic image, the rearward panchromatic image, and the rearward multispectral image under the same coordinate system and plane projection. Step 113: Remove outliers from the original laser elevation points to obtain the laser elevation points.

[0020] In this embodiment, for step 111, the original forward-looking panchromatic image, the original backward-looking panchromatic image, the original backward-looking multispectral image, and the original laser elevation point are remote sensing data of Gaofen-7 forward-looking panchromatic image, Gaofen-7 backward-looking panchromatic image, Gaofen-7 backward-looking multispectral image, and Gaofen-7 laser elevation point data of the same geographical area.

[0021] In this embodiment, for step 112, the original front-view panchromatic image, the original rear-view panchromatic image, and the original rear-view multispectral image are subjected to radiometric correction, denoising and stripe suppression, geometric correction, unified projection, and spatial registration. Then, local cropping is performed according to the initially selected tower location, and all images are unified to the same coordinate system and plane projection to obtain the front-view panchromatic image, the rear-view panchromatic image, and the rear-view multispectral image under the same coordinate system and plane projection, so as to facilitate subsequent stereo matching and raster overlay analysis.

[0022] In this embodiment, before step 113, duplicate points and missing records in the original laser elevation points can be deleted, and low-confidence points can be removed based on the quality identifier of each laser point. Then, for step 113, abnormal elevation points are identified based on the neighborhood statistics method: For any laser point Calculate the local average elevation within its neighborhood. with standard deviation If satisfied This is then identified as an outlier. Among them, A value of 2.5-3.0 can be used. The average elevation within the neighborhood can also be set. Replace the original abnormal point's elevation.

[0023] Since the original laser elevation points have their own geographic coordinates (latitude and longitude / geocentric coordinates), a spatial index with the same coordinate system and projection as the image in step 112 can be established for the retained laser points, ensuring that each laser point corresponds to a unique location on the ground and a certain geographic area on the image.

[0024] In some alternative implementations, step 12, establishing a digital surface elevation model of the initially selected tower location area based on the forward-looking panchromatic image and the rear-looking panchromatic image, includes: Step 121: Perform epipolar resampling and stereo matching processing on the forward-looking panchromatic image and the rear-looking panchromatic image to obtain a disparity map; Step 122: The image is interpolated to obtain a digital surface elevation model of the initially selected tower location area.

[0025] In this embodiment, for step 121, the centerline point of the initially selected tower location area is used. A local modeling region with a radius of 100m-150m is established around the center, covering both the area where the tower base platform is located and the surrounding terrain transition zone. Then, the foreground and background panchromatic images within this local modeling region are processed as follows: 1. Perform epipolar resampling based on the RPC model to establish the mapping relationship between image pixel coordinates and ground geodetic coordinates: Ground geodetic coordinates: (X, Y, Z) (X is the plane coordinates in the east direction, Y is the plane coordinates in the north direction, and Z is the ground elevation); Image pixel coordinates: (s, l) (s column number, l row number); Forward expression of RPC model (ground → image): ; in, , Indicates the numerator and denominator of the column direction polynomial. Indicates the numerator and denominator of a row direction polynomial.

[0026] 2. In a stereo image pair, corresponding ground points must fall on the corresponding epipolar lines of the left and right images. The RPC model is used to convert the original non-epidural images into epipolar images, ensuring that corresponding points only have a one-dimensional offset in the row / column direction, significantly simplifying subsequent stereo matching. (1) Determine the left image (Forward-looking panchromatic image), the coordinates of any pixel are ( , ); Right image (Forward-looking panchromatic image), the coordinates of any pixel are ( , ); (2) Determine the epipolar constraint equations based on the RPC model , , ; (3) Combining forward and inverse calculations of the RPC model, the local modeling area (radius 100~150m, center point) is modeled. Inner elevation interval Traverse and solve for the corresponding epipolar lines of the left and right images: take the left image pixels ( , The ground point set is obtained by inverse calculation (image → ground) using the RPC model. Substitute the ground point set into the right image RPC model for forward calculation to obtain the right image pixel trajectory, which is the corresponding epipolar line of the right image.

[0027] (4) When the original image is resampled into a regular epipolar image, bilinear interpolation (the mainstream interpolation method for remote sensing images, which balances accuracy and efficiency) is used to calculate the pixel gray values ​​of the epipolar image: ; in, This indicates the coordinates of the target pixels in the epipolar image. Indicates epipolar image The final grayscale value, Indicates the surrounding area in the original image The gray values ​​of the four adjacent original pixels at the mapped location. This indicates the horizontal offset of the target point from the original cell. , This indicates the vertical offset of the target point from the original pixel. .

[0028] After resampling, the forward and backward panchromatic images are converted into standard epipolar stereo image pairs: corresponding points only have column parallax, and their row coordinates are completely consistent.

[0029] 3. For a standard epipolar stereo image pair, define the left epipolar image (forward-looking) pixel: Right epipolar image (rear view) corresponding pixels: According to the parallax formula Determine the pixel The disparity value at that location, where, Represents a cell The disparity value at each location, the disparity map ultimately stores the disparity value of each cell. , Indicates the coordinates of the left epipolar image column. This indicates the coordinates of the corresponding points in the right epipolar image. Represents row coordinates (epidermal image row alignment, corresponding points) (Completely equal).

[0030] Then, corresponding points are identified based on the window grayscale correlation, and the matching cost formula is as follows: ; in, This represents the matching correlation coefficient within window W when the disparity is d, with a value range of []. [1,1]; the closer the value is to 1, the higher the matching similarity. Indicates the grayscale of the left nuclear line image. This indicates the grayscale of the right nuclear line image. This represents the matching window (square neighborhood). Represents relative coordinates within the window. This represents the average grayscale value within the left image window W. This represents the average grayscale value of the window corresponding to the right image, where N is the total number of pixels in the window.

[0031] Traverse the preset parallax range The disparity corresponding to the largest correlation coefficient is taken as the optimal disparity for that pixel: ,in, Represent the final optimal disparity of pixel (u,v) and write it into the disparity map. This indicates the upper and lower limits of the parallax search, which are preset based on the baseline of the Gaofen-7 stereo image and the topography of the survey area. Let d represent the independent variable that maximizes function C. According to... Determine the disparity map.

[0032] For step 122, if the disparity map contains invalid matching points (no corresponding points, low matching confidence), neighborhood mean interpolation can be used as an example: According to... Determine the disparity value of the invalid pixel (u,v) after interpolation, where, This indicates the M normal disparity values ​​within the effective neighborhood of the pixel. This indicates the number of valid neighboring pixels participating in the interpolation.

[0033] After interpolating invalid regions of the disparity map, the disparity map is inversely calculated into a 3D point cloud using simultaneous equations via RPC. The point cloud is then gridded to generate a Digital Surface Elevation Model (DSM), which can be represented as:

[0034] in, Representing coordinates The elevation value of the digital surface model at that location; This represents a set of three-dimensional point clouds obtained by inverse calculation from front- and rear-view stereo images; This indicates a processing function that performs gridding, interpolation, and elevation assignment on a 3D point cloud to generate a DSM.

[0035] Semi-global matching algorithms SGM or SGBM can be used, and the DSM output resolution is 1m.

[0036] In some alternative implementations, step 13, which involves filtering the pixels in the digital surface model based on the rear-view multispectral image to obtain a first local bare ground elevation model, includes: Step 131: Determine the normalized vegetation index based on the rear-view multispectral image; Step 132: Determine the non-surface protrusion areas in the digital surface model; Step 133: Based on the normalized vegetation index and non-surface protrusion areas, determine the set of bare land candidate pixels and the set of occluded pixels in the digital surface model; Step 134: Based on the elevation information of the bare land candidate pixel set, perform surface elevation restoration on the elevation information of the occluded pixel set to obtain the adjusted elevation information of the occluded pixel set. Step 135: Determine the first local bare land elevation model based on the elevation information of the bare land candidate pixel set and the elevation information of the adjusted occluded pixel set.

[0037] In this embodiment, for step 131, the Normalized Difference Vegetation Index (NDVI) for each pixel is calculated using the back-look multispectral image: ,in, This is the reflectance value in the near-infrared band. This is the reflectance value in the red light band.

[0038] If a certain pixel satisfy Then it can be marked as a candidate pixel for vegetation cover, preferably, 0.35.

[0039] For step 132, filter the pixels of the digital surface elevation model obtained in step 12: A preset elevation threshold is determined based on the latitude and longitude of the tower site area and the elevation value of each candidate bare land cell in the tower site area: ; in, , in, This represents the average elevation of the bare ground pixels in the tower location area. This represents the standard deviation of the elevation of bare ground pixels in the tower location area. This indicates the baseline elevation difference of the bare land in the tower location area. This indicates the baseline elevation difference of the bare land in the tower location area. This represents the fluctuation amplification factor (empirical threshold factor), with a typical value. For flat terrain, use 1.0; for slightly undulating terrain, use 1.0. , Indicates the longitude of the center point of the tower. Indicates the latitude of the center point of the tower. This represents the longitude correction factor. Indicates the latitude correction factor. This represents the global reference elevation constant (such as the local geodetic datum elevation, sea level correction value). This indicates the final elevation determination threshold.

[0040] Pixels with elevation values ​​greater than a preset elevation threshold, pixels with relatively regular boundaries or obvious protrusions in planar morphology, and pixels whose spectral features do not belong to vegetation are identified as non-surface protrusion areas (which may include buildings, rocks, etc. in addition to vegetation).

[0041] For each pixel of the digital surface elevation model obtained in step 133 and step 12, it is classified according to the following logic: if it is not a candidate pixel for vegetation cover and does not belong to a non-surface protrusion area, it is regarded as a candidate pixel for bare land; if it is a candidate pixel for vegetation cover or belongs to a non-surface protrusion area, it is regarded as an occluded pixel. Therefore, we can obtain the set of candidate bare pixels B and the set of occluded pixels O.

[0042] For step 134, since vegetation, buildings, rocks, and other obstructions cause the corresponding pixel elevations to be much higher than the actual ground level, they cannot represent the original surface topography. These pixels are classified as occlusion set O, and using them directly would lead to terrain misjudgment. However, bare land areas have no tall obstructions, and their DSM elevations are approximately equal to the actual ground elevations, making them reliable surface elevation reference samples for the entire area. Therefore, for the occlusion pixel set O, the surface elevation is restored using the surrounding bare land candidate pixel set B. Specifically, the following methods are used: local slope fitting is used for small patches, TIN interpolation is used for large patches, and Kriging interpolation is used when the terrain changes gently, to obtain the adjusted elevation information of the occlusion pixel set.

[0043] Specifically, local slope fitting: 1. Select sample points. Use the coordinates of the occluded pixels to be recovered ( , Centered on , candidate bare land pixels B within the neighborhood are extracted, resulting in a set of known coordinates ( , and true elevation The sample points.

[0044] 2. Construct a system of least squares equations. Substitute all bare land samples into the plane equations. The optimal coefficients a, b, and c are solved using the least squares method to minimize the sum of squared elevation residuals from the sample points to the fitting plane.

[0045] 3. Take the partial derivatives of a, b, and c and set the derivatives to 0. Solve the system of linear equations to obtain a unique set of a, b, and c.

[0046] 4. Set the coordinates of the occluded pixel ( , Substituting into the fitting equation, we obtain the restored ground elevation of that point: .

[0047] Specifically, TIN irregular triangular mesh interpolation: Due to the large area of ​​the shading region and the complex terrain, a single plane cannot fit the local terrain. A continuous irregular triangular network can be constructed using the surrounding discrete bare land sample points B. The terrain inside each triangle is regarded as a linear slope, and the elevation of the shading point is calculated by interpolation of the triangular facets.

[0048] 1. Select interpolation samples. Extract all valid bare ground pixels B around the occluded patch as discrete elevation control points.

[0049] 2. Construct a TIN triangulation network. Use Delaunay triangulation to connect all control points into a non-overlapping triangular mesh (ensuring optimal triangulation network shape and avoiding elongated triangles).

[0050] 3. Locate the triangular facet to which the target point belongs. Determine the occluded pixels to be restored. Which triangle in TIN does it fall inside?

[0051] 4. Elevation calculation using linear interpolation of triangular faces. Let the three vertices of the triangle containing the target point be... , , The elevation of the point is calculated using area-weighted / bilinear interpolation. The position weight of the point within the triangle is assigned to the three vertices, and the elevation is obtained by weighted summation. The above steps are repeated for all occluded pixels within the large patch to complete the elevation restoration of the entire area.

[0052] Specifically, Kriging interpolation: 1. Determine the neighborhood samples. Centered on the pixel to be recovered, search for bare land sample points B within a certain radius, which will be used as interpolation samples.

[0053] 2. Calculate the variogram (core). Analyze the relationship between the distance between sample points and the elevation difference, and fit the variogram. Description: The greater the distance h between two points, the greater the variation in elevation difference.

[0054] 3. Construct the Kriging equation system. Based on spatial correlation, solve for the weight coefficients corresponding to each surrounding sample point. It satisfies: unbiased estimation + minimum variance.

[0055] 4. Calculate the target elevation using weighted average. Kriging interpolation formula: ; Elevation of surrounding bare land samples. The weights obtained from the solution are weighted and summed to obtain the restored elevation of the occluded pixels.

[0056] 5. Pixel-by-pixel traversal. Complete the elevation recovery of pixels obscured by flat terrain throughout the entire area.

[0057] For step 135, a first local bare land elevation model is determined based on the elevation information of the bare land candidate pixel set and the elevation information of the adjusted occluded pixel set.

[0058] In some alternative implementations, step 14 involves correcting the first local bare land elevation model based on the laser elevation points to obtain a second local bare land elevation model, including: Step 141: Determine the elevation error and weight of each pixel based on the laser elevation point and the elevation information of each pixel in the first local bare ground elevation model; Step 142: Determine the elevation modification amount for each pixel based on the elevation error and weight. Step 143: Adjust the elevation information of each pixel according to the elevation modification amount to obtain the second local bare ground elevation model.

[0059] In this embodiment, since the first local bare ground elevation model may still be affected by stereo matching errors, occlusion recovery errors, etc., this embodiment further uses laser elevation points for local correction.

[0060] For step 141, for the pixel p to be corrected, search for the set of laser elevation points Lp within the preset radius rc, and calculate the elevation error at each control point: ,in, This represents the elevation error at the i-th laser elevation point; This represents the measured elevation value of the i-th laser elevation point; This represents the elevation value at the location of the i-th laser point in the first local bare ground elevation model.

[0061] Then, the error is propagated according to the inverse distance weighting, where the weights are: ,in, This represents the influence weight of the i-th laser elevation point on the pixel p to be corrected; This represents the distance between the pixel p to be corrected and the i-th laser elevation point; Its function is to prevent the denominator from being zero; it is a minimal constant.

[0062] For step 142, according to Determine the elevation modification amount for each pixel, where, This represents the elevation correction amount for the pixel p to be corrected; n represents the number of laser elevation points around the pixel p that participate in the correction. This represents the weight of the i-th laser elevation point; This represents the elevation error at the i-th laser elevation point.

[0063] For step 143, the second local bare ground elevation model is as follows: ; in, This represents the elevation value of the second local bare ground elevation model, i.e., the corrected bare ground DEM at pixel p. This represents the elevation value at pixel p in the second local bare ground elevation model; This indicates the elevation correction amount corresponding to that pixel. Preferably, the laser point search radius rc = 30m. Take 0.5-1.

[0064] In some alternative implementations, in step 15, points in the second local bare land elevation model are filtered according to preset conditions to obtain multiple candidate points, including: Step 151: Discretize the second local bare ground elevation model according to the preset grid to obtain the discrete points to be screened; Step 152: Based on preset slope constraints, valley edge safety distance constraints, restricted construction zone constraints, and span variation constraints, the discrete points are screened to obtain multiple candidate points.

[0065] In this embodiment, for step 151, a local micro-shift search area is established with the center P0 of the initially selected tower location area as the center. The search radius is 20-60m. This range can avoid local unfavorable terrain and will not cause the tower location to shift too much, thus affecting the overall design of the original line.

[0066] Generate discrete points to be filtered within the search area using a 1m grid: .

[0067] For step 152, each candidate point is subjected to hard constraint judgment, and those that do not meet the constraint conditions are directly eliminated: Among them, slope constraints: according to Determine the slope of the discrete points to be screened, where, This represents the slope at the discrete point p to be screened; This represents the elevation value of the discrete point p to be screened in the second local bare ground elevation model; x and y are plane coordinates. and These represent the rates of change of elevation in the x and y directions, respectively.

[0068] If the average slope within the range of the discrete point p to be screened or its base platform is greater than the threshold... Then eliminate the preferred ones. .

[0069] Among them, the safety distance constraint at the edge of the gully is: Using the curvature, confluence trend, and slope break line extraction results of the discrete point p to be screened, the edges of gullies, the upper edges of steep slopes, and unstable slope break zones are identified. If the distance from the discrete point p to be screened to the above-mentioned dangerous boundaries is less than the minimum safe distance... If so, it will be removed, and the judgment condition is: Preferred =8m. Among them, This represents the distance from the j-th discrete point p to the edge of a gully, the upper edge of a steep sill, or an unstable slope break, or other dangerous boundaries. This represents the minimum safe distance threshold.

[0070] Among them, the restricted construction zone constraint is: if the discrete point p to be screened is a set of occluded pixels, that is, located within buildings, water areas, roads, protected areas or other restricted construction zones, it will be removed.

[0071] Among them, the span variation constraint is as follows: let the adjacent tower positions be respectively and Then the discrete points to be screened The corresponding new front gear is The next gear is ,in, Represents the discrete points to be filtered. The corresponding previous gear length; Represents the discrete points to be filtered. The corresponding next gear length; Indicates the previous base position; Indicates the j-th candidate micro-shifting tower site; This function represents the distance between two points.

[0072] like or If so, then discard. Among them, Indicates the previous gear distance in the original design; η represents the next gear length in the original design; η represents the maximum allowable gear length change rate threshold.

[0073] After applying the above constraints, discrete points that do not meet the conditions are eliminated, resulting in multiple candidate points.

[0074] In some alternative implementations, step 16, determining the evaluation score of the candidate point according to a preset evaluation model, includes: Step 161: Determine the earthwork cost of the candidate tower base platform based on the preset tower base platform earthwork cost model. Step 162: Determine the slope stability risk cost of candidate points based on the preset slope stability risk cost model. Step 163: Determine the surface roughness cost of candidate points based on the preset surface roughness cost model. Step 164: Determine the construction access cost of candidate points based on the preset construction access cost model; Step 165: Determine the vegetation clearing cost of candidate points based on the preset vegetation clearing cost model. Step 166: Determine the evaluation score of the candidate point based on the earthwork cost of the tower base platform, the slope stability risk cost, the surface roughness cost, the construction access cost, and the vegetation clearing cost.

[0075] In this embodiment, for step 161, the coordinates of the candidate points are taken as the origin, and... Establish a tower base platform area for the platform's side length. , ,according to The earthwork cost of the tower base platform is determined by selecting candidate points (a smaller cost indicates a smaller amount of earthwork required for the tower base platform). Indicate candidate points The cost of earthwork for the tower base platform Represents the area of ​​a single raster cell; This represents the absolute value of the elevation difference between the pixel elevation and the platform's average elevation. , Indicate candidate points The average elevation of the corresponding tower base platform area; Indicate candidate points The corresponding tower base platform area; This represents the raster cells within the platform area; n represents the number of cells within the platform area. This represents the elevation value of pixel q in the second local bare ground elevation model.

[0076] If it is necessary to distinguish between excavation and filling, then according to Candidate points for determining candidate points The total cost of earthwork; in, Indicates the volume of excavation. ; in, The amount to be filled in on the form. ; in, Indicate candidate points Excavation volume corresponding to the platform area; Indicate candidate points The corresponding tower base platform area; Represents the raster cells within the platform area; Represents a cell Elevation value; Indicates the average elevation or design leveling elevation of the platform area; Represents the area of ​​a single raster cell; Indicate candidate points The amount of fill for the corresponding platform area.

[0077] For step 162, according to Determine the slope stability risk cost of candidate points; in, Indicate candidate points The cost of slope stability risks; This represents the average slope of the analysis area surrounding the candidate point; This indicates the maximum slope of the analysis area surrounding the candidate point; Indicates the curvature of the cross section; Indicates local elevation differences; , , These are the weight coefficients of each evaluation factor.

[0078] For step 163, according to Determine the surface roughness cost of candidate points; in, Indicate candidate points The cost of surrounding surface roughness; Indicate candidate points The surrounding analytical neighborhood; To analyze the number of pixels in the neighborhood; This represents the elevation value of pixel q; Analyzing the average elevation within the neighborhood reveals that the greater the roughness, the more fragmented the surface, making mechanical deployment and construction organization more difficult.

[0079] For step 164, first determine the candidate points. The cost of each single grid cell in the surrounding analysis neighborhood is calculated because the path is composed of multiple consecutive grid cells q connected in series, from the existing road to the candidate point. There will be multiple different paths, each with its own cost. By traversing all feasible paths, the path with the lowest cumulative cost is selected as the final construction access cost for that candidate point. , representing the construction access cost for selecting candidate points; in, Indicate candidate points The cost of construction and access; This indicates that there is an existing road or a point of access; Represents the path cost of multiple different paths. min indicates selecting the minimum cumulative passage cost.

[0080] Among them, according to Determine the cost of a single path; where, Indicates the weighting factor. The following factors represent, in order: slope factor (the slope value of the ground at grid q, usually standardized (0~1 or 0~10)), surface relief / roughness factor (the surface relief and terrain roughness of the grid q area), obstacle factor (the influence value of obstacles existing in grid q), and vegetation / ground cover factor (the influence value of vegetation cover and vegetation type of grid q).

[0081] For step 165, the high vegetation cover ratio within the candidate point platform area and its outer buffer zone is statistically analyzed. Determine the vegetation clearing cost for candidate sites (the smaller the value, the less work is required for preliminary clearing and vegetation treatment). in, Indicate candidate points The cost of vegetation clearing; This indicates the area of ​​high vegetation cover within the candidate point platform area and the outer buffer zone; This represents the total area of ​​the candidate point platform region and the outer buffer zone.

[0082] For step 166, according to The various evaluation indicators (i.e., earthwork cost of the tower base platform, slope stability risk cost, surface roughness cost, construction access cost, and vegetation clearing cost) are normalized. in, This represents the normalized value of the k-th evaluation indicator; Indicate candidate points The kth original evaluation index; This represents the minimum value of the k-th indicator among all candidate points; This represents the maximum value of the k-th indicator among all candidate points; k represents the evaluation indicator number, such as earthwork cost, slope risk cost, and roughness cost.

[0083] Then construct the comprehensive objective function: ; in, Indicate candidate points Evaluation score; Indicates the weight of each evaluation indicator; , , , , This represents the normalized costs of earthwork, slope stability risk, surface roughness, construction access, and vegetation clearing for the tower base platform.

[0084] Through steps 161 to 166, the evaluation score of each candidate point is obtained.

[0085] In some alternative implementations, step 17 involves determining the optimized tower location information based on the evaluation score of each candidate point, including: according to Determine the optimized tower location information; in, These are the final optimized tower location coordinates. Indicates candidate points, Find the candidate point that yields the minimum evaluation score.

[0086] In this embodiment, optimized tower location coordinates can be output based on the results of the above steps. The following parameters were considered: offset distance and direction relative to the initial tower location; comparison of earthwork volume of the platform before and after optimization; comparison of slope risk indicators before and after optimization; comparison of construction access costs before and after optimization; comprehensive suitability heat map of candidate area; and three-dimensional terrain profile of optimized tower location.

[0087] In one application scenario, taking the Xth initially selected tower location of a 220kV mountain transmission line as an example: 1. Acquire Gaofen-7 forward panchromatic image, backward panchromatic image, backward multispectral image, and laser elevation point covering a 500m radius around Tower X; 2. Perform radiometric correction, registration, and unified projection on the images; 3. Establish a modeling area with a radius of 120m centered on tower location X; 4. Using front and rear panchromatic images, perform epipolar resampling and stereo matching to generate a 1m resolution DSM (Digital Surface Elevation Model). 5. Calculate NDVI based on back-view multispectral imagery, and mark areas with NDVI ≥ 0.35 as vegetation-covered areas; 6. Identify regular high-protrusion areas in the DSM and use them as masks for non-surface protrusion areas; 7. Use TIN interpolation and local slope fitting to restore the initial bare land DEM (first local bare land elevation model) in the masked area. 8. Use laser elevation points within a radius of 30m to correct the initial bare ground DEM and obtain a local bare ground DEM (second local bare ground elevation model). 9. Establish a micro-movement search area with a radius of 40m centered on tower location X, and generate candidate points at 1m intervals; 10. Eliminate candidate points with a slope greater than 25°, less than 8m from the edge of the gully, or a span variation rate greater than 5%; 11. Calculate the earthwork volume, slope risk, roughness, construction access cost, and vegetation clearing cost for the retained candidate points; 12. Select the optimal candidate point as the optimized tower location by using the comprehensive objective function. ; 13. Output the optimized tower location coordinates and comparison results.

[0088] Ultimately, the optimized tower location was offset by 16m from the initially selected location, which significantly reduced the amount of earthwork on the platform, lowered the cost of the construction access path, and simultaneously improved the slope risk indicators.

[0089] The embodiments of this invention combine Gaofen-7 stereo imagery, multispectral imagery, and laser elevation point reconstruction to restore local bare ground DEMs suitable for tower foundation analysis, thereby more realistically reflecting the actual terrain undulations after clearing and improving the reliability of tower location analysis. Local micro-shift optimization within tens of meters of the initially selected tower location ensures the final tower location is closer to the actual constructible position, reducing the amount of tower foundation excavation and slope treatment. Through remote sensing data and a unified evaluation model, standardized and batch-based local analysis and optimization of multiple initially selected tower locations can be performed, reducing the reliance on extensive manual experience for point adjustments and repetitive surveys. The unified model incorporates transmission engineering-specific constraints and indicators such as span variation rate, tower foundation platform earthwork, slope risk, and construction access costs, thus better reflecting actual power design practices. By quickly identifying superior tower locations in the early design phase, subsequent repeated point adjustments, on-site corrections, and foundation rework can be reduced, shortening the design cycle and lowering upfront costs.

[0090] like Figure 2 As shown, an embodiment of the present invention proposes a tower location optimization device 200 for transmission lines, comprising: Acquisition module 201 is used to acquire the fore-view panchromatic image, rear-view panchromatic image, rear-view multispectral image and laser elevation point of the initially selected tower location area; The processing module 202 is used to establish a digital surface elevation model of the initially selected tower location area based on the forward-looking panchromatic image and the rear-looking panchromatic image; to filter the pixels in the digital surface model based on the rear-looking multispectral image to obtain a first local bare ground elevation model; to correct the first local bare ground elevation model based on the laser elevation points to obtain a second local bare ground elevation model; to filter the points in the second local bare ground elevation model according to preset conditions to obtain multiple candidate points; to determine the evaluation score of the candidate points according to a preset evaluation model; and to determine the optimized tower location information based on the evaluation score of each candidate point.

[0091] Optionally, acquire the forward panchromatic image, rearward panchromatic image, rearward multispectral image, and laser elevation point of the initially selected tower location area, including: Acquire the original frontal panchromatic image, original rearal panchromatic image, original rearal multispectral image, and original laser elevation point of the initially selected tower site area; The original forward panchromatic image, the original rearward panchromatic image, and the original rearward multispectral image are corrected to obtain the forward panchromatic image, the rearward panchromatic image, and the rearward multispectral image under the same coordinate system and plane projection. The original laser elevation points are modified by removing outliers to obtain the laser elevation points.

[0092] Optionally, a digital surface elevation model of the initially selected tower location area is established based on the forward panchromatic image and the rearward panchromatic image, including: The front-view panchromatic image and the rear-view panchromatic image are subjected to epipolar resampling and stereo matching to obtain a disparity map; The image is interpolated to obtain a digital surface elevation model of the initially selected tower location area.

[0093] Optionally, the pixels in the digital surface model are filtered based on the rear-view multispectral image to obtain a first local bare ground elevation model, including: The normalized vegetation index was determined based on the rear-view multispectral imagery. Identify the non-surface protrusions in the digital surface model; Based on the normalized vegetation index and non-surface protrusion areas, determine the set of bare land candidate pixels and the set of occluded pixels in the digital surface model; The elevation information of the occluded pixel set is restored by performing surface elevation restoration on the elevation information of the bare land candidate pixel set to obtain the adjusted elevation information of the occluded pixel set. The first local bare land elevation model is determined based on the elevation information of the bare land candidate pixel set and the elevation information of the adjusted occluded pixel set.

[0094] Optionally, the first local bare land elevation model is corrected based on the laser elevation points to obtain a second local bare land elevation model, including: Based on the elevation information of each pixel in the laser elevation point and the first local bare ground elevation model, determine the elevation error and weight of each pixel; Based on the elevation error and weight, determine the elevation modification amount for each pixel; The elevation information of each pixel is adjusted according to the elevation modification amount to obtain the second local bare ground elevation model.

[0095] Optionally, points in the second local bare land elevation model are filtered according to preset conditions to obtain multiple candidate points, including: The second local bare ground elevation model is discretized according to the preset grid to obtain discrete points to be screened; The discrete points are filtered based on preset constraints such as slope, valley edge safety distance, restricted construction zone, and span variation to obtain multiple candidate points.

[0096] Optionally, the evaluation score of the candidate point is determined according to a preset evaluation model, including: Based on the preset earthwork cost model for the tower base platform, determine the earthwork cost of the candidate points for the tower base platform; Based on the pre-set slope stability risk cost model, determine the slope stability risk cost of the candidate points; Based on the preset surface roughness cost model, the surface roughness cost of the candidate points is determined. Based on the preset construction access cost model, determine the construction access cost of candidate points; Based on the preset vegetation clearing cost model, determine the vegetation clearing cost of candidate points; The evaluation score of the candidate point is determined based on the earthwork cost of the tower base platform, the slope stability risk cost, the surface roughness cost, the construction access cost, and the vegetation clearing cost.

[0097] Optionally, the optimized tower location information is determined based on the evaluation score of each candidate point, including: according to Determine the optimized tower location information; in, These are the final optimized tower location coordinates. Indicates candidate points, Find the candidate point that yields the minimum evaluation score.

[0098] All implementations in the above method embodiments are applicable to the embodiments of this device and can achieve the same technical effect.

[0099] The present invention also provides a computing device, comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors perform the method described above.

[0100] All implementations in the above method embodiments are applicable to the embodiments of this computing device and can achieve the same technical effect.

[0101] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing tower locations for transmission lines, characterized in that, include: Acquire the frontal panchromatic image, rearal panchromatic image, rearal multispectral image, and laser elevation point of the initially selected tower site area; A digital surface elevation model of the initially selected tower location area is established based on the forward and rear panchromatic images. The pixels in the digital surface model are filtered based on the rear-view multispectral image to obtain a first local bare ground elevation model. The first local bare ground elevation model is corrected based on the laser elevation points to obtain the second local bare ground elevation model; Based on preset conditions, points in the second local bare land elevation model are filtered to obtain multiple candidate points; The evaluation score of the candidate point is determined according to the preset evaluation model; The optimized tower location information is determined based on the evaluation score of each candidate point.

2. The method for optimizing tower locations of transmission lines according to claim 1, characterized in that, Acquire the foreground panchromatic image, background panchromatic image, background multispectral image, and laser elevation points of the initially selected tower site area, including: Acquire the original frontal panchromatic image, original rearal panchromatic image, original rearal multispectral image, and original laser elevation point of the initially selected tower site area; The original forward panchromatic image, the original rearward panchromatic image, and the original rearward multispectral image are corrected to obtain the forward panchromatic image, the rearward panchromatic image, and the rearward multispectral image under the same coordinate system and plane projection. The original laser elevation points are modified by removing outliers to obtain the laser elevation points.

3. The method for optimizing tower locations of transmission lines according to claim 1, characterized in that, A digital surface elevation model of the initially selected tower location area is established based on the aforementioned forward and backward panchromatic images, including: The front-view panchromatic image and the rear-view panchromatic image are subjected to epipolar resampling and stereo matching to obtain a disparity map; The image is interpolated to obtain a digital surface elevation model of the initially selected tower location area.

4. The method for optimizing tower locations of transmission lines according to claim 1, characterized in that, Based on the rear-view multispectral image, pixels in the digital surface model are filtered to obtain a first local bare ground elevation model, including: The normalized vegetation index was determined based on the rear-view multispectral imagery. Identify the non-surface protrusions in the digital surface model; Based on the normalized vegetation index and non-surface protrusion areas, determine the set of bare land candidate pixels and the set of occluded pixels in the digital surface model; The elevation information of the occluded pixel set is restored by performing surface elevation restoration on the elevation information of the bare land candidate pixel set to obtain the adjusted elevation information of the occluded pixel set. The first local bare land elevation model is determined based on the elevation information of the bare land candidate pixel set and the elevation information of the adjusted occluded pixel set.

5. The method for optimizing tower locations of transmission lines according to claim 1, characterized in that, The first local bare land elevation model is corrected based on the laser elevation points to obtain a second local bare land elevation model, including: Based on the elevation information of each pixel in the laser elevation point and the first local bare ground elevation model, determine the elevation error and weight of each pixel; Based on the elevation error and weight, determine the elevation modification amount for each pixel; The elevation information of each pixel is adjusted according to the elevation modification amount to obtain the second local bare ground elevation model.

6. The method for optimizing tower locations of transmission lines according to claim 1, characterized in that, Points in the second local bare land elevation model are filtered according to preset conditions to obtain multiple candidate points, including: The second local bare ground elevation model is discretized according to the preset grid to obtain discrete points to be screened; The discrete points are filtered based on preset constraints such as slope, valley edge safety distance, restricted construction zone, and span variation to obtain multiple candidate points.

7. The method for optimizing tower locations of transmission lines according to claim 1, characterized in that, The evaluation score of the candidate point is determined according to a preset evaluation model, including: Based on the preset earthwork cost model for the tower base platform, determine the earthwork cost of the candidate points for the tower base platform; Based on the pre-set slope stability risk cost model, determine the slope stability risk cost of the candidate points; Based on the preset surface roughness cost model, the surface roughness cost of the candidate points is determined. Based on the preset construction access cost model, determine the construction access cost of candidate points; Based on the preset vegetation clearing cost model, determine the vegetation clearing cost of candidate points; The evaluation score of the candidate point is determined based on the earthwork cost of the tower base platform, the slope stability risk cost, the surface roughness cost, the construction access cost, and the vegetation clearing cost.

8. The method for optimizing tower locations of transmission lines according to claim 1, characterized in that, The optimized tower location information is determined based on the evaluation score of each candidate point, including: according to Determine the optimized tower location information; in, These are the final optimized tower location coordinates. Indicates candidate points, Find the candidate point that yields the minimum evaluation score.

9. A tower location optimization device for transmission lines, characterized in that, include: The acquisition module is used to acquire the foreground panchromatic image, the rearground panchromatic image, the rearground multispectral image, and the laser elevation point of the initially selected tower site area; The processing module is used to establish a digital surface elevation model of the initially selected tower location area based on the forward-looking panchromatic image and the rear-looking panchromatic image; to filter the pixels in the digital surface model based on the rear-looking multispectral image to obtain a first local bare ground elevation model; to correct the first local bare ground elevation model based on the laser elevation points to obtain a second local bare ground elevation model; to filter the points in the second local bare ground elevation model according to preset conditions to obtain multiple candidate points; to determine the evaluation score of the candidate points according to a preset evaluation model; and to determine the optimized tower location information based on the evaluation score of each candidate point.

10. A computing device, comprising: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 8.

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