A 3D point cloud-based method for detecting tree obstacles in power transmission channels

Abstract

The present invention provides a method for detecting tree obstacles in power transmission channels based on 3D point clouds, which belongs to the technical field of tree obstacle detection in power transmission channels. The technical problem to be solved is: to provide an improvement of the method for detecting tree obstacles in power transmission channels based on 3D point clouds. The technical solution adopted to solve the technical problem is: based on the elevation histogram filtering method of the normalized digital surface model, the interference point clouds of the ground, buildings and trees existing in the original point cloud data of the power transmission channel are filtered, the transmission line point cloud data are preliminarily extracted and preprocessed, the transmission line point cloud is extracted from the preprocessed point cloud data, tree obstacle detection is performed on the transmission line, specifically the distance from the tree obstacles around the transmission line to the transmission line is measured, and the extracted transmission line is three-dimensionally reconstructed using the catenary equation: according to the determined obstacle points, the pole tower points are removed from the obstacle point cloud at this time. The present invention is applied to the detection of tree obstacle hazards in power transmission channels.

Description

Technical Field

[0001] The present invention provides a method for detecting tree obstacle hidden dangers in power transmission channels based on 3D point clouds, belonging to the technical field of detecting tree obstacle hidden dangers in power transmission channels. Background Art

[0002] As the scale of power grid construction continues to expand, the requirements for efficiency and reliability in transmission corridor inspection and maintenance are also increasing, which brings more challenges to transmission corridor inspection and maintenance. Currently, tree barrier detection is a crucial part of transmission corridor inspection. Tree barrier hazards in transmission corridors mainly refer to trees that are closer than the grid safety standard distance from the transmission line. Due to the limitations of the line's terrain environment and equipment performance, traditional manual inspections are inefficient and of poor quality, making it difficult to promptly detect tree barrier hazards in transmission corridors.

[0003] To address this issue, drones equipped with LiDAR can quickly acquire three-dimensional point cloud information about transmission corridors. Algorithmic processing of this point cloud information enables efficient and high-quality tree barrier detection along transmission corridors. Currently, various solutions have been proposed for tree barrier detection along transmission corridors based on 3D point cloud processing technology. Most of these methods extract transmission lines using Hough transforms and combine them with spatial index structures for tree barrier detection. However, these methods lack robust applicability and fail to address the low efficiency of detection algorithms due to the large volume of raw point cloud data along transmission corridors. Furthermore, the accuracy of current algorithms for detecting tree barrier hazards along transmission corridors is often limited by the accuracy of transmission line extraction. High-precision extraction of transmission lines in densely populated areas is impossible, resulting in unsatisfactory tree barrier detection results. Summary of the Invention

[0004] In order to overcome the deficiencies in the prior art, the present invention aims to solve the following technical problem: to provide an improved method for detecting tree obstacles in power transmission channels based on 3D point clouds.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for detecting tree obstacles in power transmission channels based on 3D point clouds, comprising the following detection steps:

[0006] Step 1: Based on the elevation histogram filtering method of the normalized digital surface model, the interference point clouds of the ground, buildings, and trees in the original point cloud data of the transmission channel are filtered out, and the transmission line point cloud data is preliminarily extracted and preprocessed:

[0007] Step 1.1: Use the normalized digital surface model to process the transmission channel point cloud data to achieve elevation normalization of the point cloud data and eliminate the impact of terrain height differences on the ability to initially extract the transmission line point cloud from the elevation histogram;

[0008] Step 1.2: Use the elevation histogram to process the normalized raw point cloud data, remove the interference point clouds of the ground, buildings, and trees, traverse the point cloud data at this time to obtain the point cloud data elevation distribution map, set the threshold to filter the background points, and realize the preliminary extraction of the transmission line point cloud data;

[0009] Step 2: Based on the transmission line extraction method of the random sampling consistency RANSAC algorithm, the transmission line point cloud data after data preprocessing is extracted:

[0010] Step 2.1: Record the point cloud data processed in step 1 as data S, and project S onto the XOY plane;

[0011] Step 2.2: The algorithm starts to iterate, randomly selecting the data point with the minimum sample size from the projected data, and randomly initializing two data points as inliers in each iteration;

[0012] Step 2.3: According to the selected internal point, the formula Calculate the parameters of the fitted straight line and calculate the Euclidean distance s from the remaining data points to the straight line. The calculation formula is:

[0013] ;

[0014] Set a threshold, consider data points with distances less than the threshold as inliers, and data points with distances greater than the threshold as outliers, and calculate the total distance L of all inliers. k , the calculation formula is:

[0015] ;

[0016] The algorithm performs fitting k times in this step. The value of k is determined according to the line conditions. After the fitting is completed, the L value among the K times is selected. k The smallest result is taken as the result of this iteration;

[0017] Step 2.4: Set the number of algorithm iterations m according to the number of transmission lines in the actual line, iterate step 2.3 and remove the straight lines obtained in each iteration, and finally extract the point cloud of m transmission lines in this plane;

[0018] Step 2.5: Project the data S to the XOZ plane and repeat steps 2.2 to 2.4. Use the RANSAC algorithm to perform line detection in the XOZ plane to extract the transmission line point cloud. The straight line fitted in the XOZ plane corresponds to the arc missing when extracting the transmission line in the XOY plane. Combine the transmission line point clouds extracted from the XOY and XOZ planes to obtain a complete transmission line point cloud.

[0019] Step 3: Detect tree barriers around the transmission lines. Specifically, measure the distance from the tree barriers around the transmission lines to the transmission lines. Use the catenary equation to perform 3D reconstruction of the transmission lines extracted in step 2:

[0020] The catenary equation used specifically describes the basic relationship between conductor sag and stress, specific load, and span, and the expression is:

[0021] ;

[0022] Where, is the stress at the lowest point of the conductor, g is the acceleration of gravity, y is the sag of the conductor, x is the arc length span, cosh is the hyperbolic cosine function, C1 and C2 are integration constants, whose values are determined by the position of the coordinate origin and the initial conditions;

[0023] According to the collected data type, the catenary formula is expanded according to the Taylor series as follows: ;

[0024] Convert the Taylor expansion expression into a polynomial equation in three-dimensional space, the expression is:

[0025] ;

[0026] In the formula, p and q are the parameters of the projection line of the transmission line on the two-dimensional plane. Finally, the least squares method is used to fit and solve the polynomial model coefficients;

[0027] Step 4: After the transmission line extraction is completed, a spatial index structure is established to search for points other than the transmission line point cloud. If the distance between a point and any transmission line is less than the safety threshold, it is judged as an obstacle point, and the tower points are removed from the obstacle point cloud at this time:

[0028] Step 4.1: Use the non-transmission line point cloud as data to establish a spatial index structure, traverse and calculate the Euclidean distance between all points in the non-transmission line point cloud and the transmission line, and mark all point clouds with distances less than the safety threshold as obstacle point clouds;

[0029] Step 4.2: Use the adaptive elevation quantity statistics method to locate the tower point, and calculate the point cloud elevation density factor ,based on Draw a statistical histogram, locate the tower position according to the extreme points in the graph, and remove the tower obstacle point cloud;

[0030] Step 5: Based on the transmission channel point cloud data after the tower obstacle point cloud is removed, detect tree obstacle hazards around the transmission channel.

[0031] The specific method for processing the transmission channel point cloud data in step 1.1 is:

[0032] Step 1.1.1: Traverse the point cloud data and obtain the coordinates p of each point i and the corresponding elevation value z i , calculate the average elevation value μ of all points in the point cloud data, the calculation formula is:

[0033] ;

[0034] Step 1.1.2: Calculate the normalized elevation value N of each point i , the calculation formula is:

[0035] ;

[0036] Among them, p iz Represents point p i The Z-axis coordinate value of

[0037] σ represents the standard deviation of the elevation values of all points in the point cloud data, and the calculation formula is:

[0038] ;

[0039] Step 1.1.3: Traverse the elevation value of each point in the point cloud data and subtract the normalized elevation value N i , get the normalized elevation value, and realize the elevation normalization of the original point cloud data.

[0040] The specific method for locating the tower position and removing the tower obstacle point cloud in step 4.2 is:

[0041] Step 4.2.1: Traverse the obstacle point cloud along the horizontal X axis to calculate the elevation factor , the calculation formula is:

[0042] ;

[0043] Among them, m i It represents the number of point clouds with the same X-axis coordinate as the i-th point and a Y-axis coordinate value greater than 10. H represents the height of the tower in this line;

[0044] Step 4.2.2: Calculate the height factor Draw a statistical histogram by locating the The tower point in the obstacle point cloud is determined by the position of the maximum point of . After removing the tower point, the remaining point cloud is the tree obstacle in the transmission channel.

[0045] The beneficial effects of the present invention over the existing technology are as follows: In response to the shortcomings of the existing technology, the present invention proposes a transmission channel tree barrier detection method based on 3D point cloud. The method integrates point cloud data preprocessing, transmission line point cloud extraction and modeling, and tree barrier detection, and can provide technical support for the application of 3D point cloud processing technology in the detection of tree barrier hazards in transmission channels. Compared with the currently used transmission channel tree barrier detection method, the detection method proposed by the present invention designs a normalized digital surface model combined with the elevation histogram data preprocessing method, which can greatly reduce the amount of original point cloud data and the number of noise points. It can also realize the initial extraction of transmission lines, which can effectively improve the overall algorithm performance. At the same time, the present invention adopts the RANSAC algorithm to extract transmission line point clouds at XOY and XOZ respectively, and uses the catenary model to fit the transmission lines. The experimental results show that this method can obtain higher-precision transmission line point clouds. Compared with other detection methods, it can effectively improve the accuracy of subsequent algorithm tree obstacle detection. In addition, the present invention adds a normalized digital surface model algorithm to data preprocessing, and the RANSAC algorithm itself has strong robustness, which makes the present invention applicable to transmission line tree obstacle detection in various terrain environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The present invention will be further described below with reference to the accompanying drawings:

[0047] Figure 1 This is a flowchart of the steps for detecting tree obstacles in power transmission channels according to the present invention;

[0048] Figure 2 This is a state diagram of the power transmission channel tower point cloud of the present invention;

[0049] Figure 3 This is a schematic diagram of the state of the original point cloud of the power transmission channel of the present invention;

[0050] Figure 4 This is a schematic diagram of the state of the transmission line point cloud after data preprocessing of the present invention;

[0051] Figure 5 This is a modeling effect diagram of the power transmission line of the present invention;

[0052] Figure 6 Schematic diagram of the detection of tree obstacle hazards in power transmission channels according to the present invention. DETAILED DESCRIPTION

[0053] The present invention proposes a method for detecting tree obstacles in transmission channels based on 3D point clouds. Data preprocessing is achieved through the normalized digital surface model and elevation histogram, transmission line point cloud extraction is performed using the RANSAC algorithm, three-dimensional modeling of transmission lines is performed using a catenary model, and tree obstacle detection and tower point filtering are performed using a kd-tree combined with an adaptive elevation quantity statistics algorithm. This method can effectively detect tree obstacles in transmission channels, and the detection efficiency, accuracy, and applicability of the present invention can all reach a high level.

[0054] like Figure 1 As shown, the present invention first uses a normalized digital surface model combined with an elevation histogram algorithm to preprocess the original point cloud of the transmission channel, reduces the density of the background point cloud in the original point cloud of the transmission channel, and realizes differential downsampling of the point cloud data; then uses the RANSAC algorithm to extract the transmission line from the preprocessed point cloud data and models the transmission line based on the catenary equation; finally, a spatial index structure is established to detect the obstacle point cloud of the transmission channel and an adaptive elevation quantity statistical algorithm is designed to eliminate the tower point cloud to realize tree obstacle detection.

[0055] To achieve the above purpose, the detection method adopted by the present invention mainly includes:

[0056] Step 1: Point cloud data preprocessing. This paper designs an elevation histogram filtering method based on a normalized digital surface model to filter out the large number of interfering point clouds such as ground, buildings, and trees in the original point cloud data of the transmission channel. This allows for the initial extraction of the transmission line point cloud, thereby improving the accuracy and speed of subsequent transmission line extraction algorithms.

[0057] Step 1.1: The normalized digital surface model is used to process the transmission channel point cloud data in the case of large terrain undulations. This algorithm is used to normalize the point cloud data elevation and eliminate the impact of terrain height differences on the ability to initially extract the transmission line point cloud from the elevation histogram. The specific steps include:

[0058] Step 1.1.1: Traverse the point cloud data and obtain the coordinates p of each point i and the corresponding elevation value z i , calculate the average elevation value μ of all points in the point cloud data, the calculation formula is:

[0059] ;

[0060] Step 1.1.2: Calculate the normalized elevation value N of each point i , the calculation formula is:

[0061] ;

[0062] Among them, p iz Represents point p i The Z-axis coordinate value of

[0063] σ represents the standard deviation of the elevation values of all points in the point cloud data, and the calculation formula is:

[0064] ;

[0065] Step 1.1.3: Traverse the elevation value (Z coordinate axis value) of each point in the point cloud data and subtract the normalized elevation value N i The normalized elevation value can be obtained, realizing the elevation normalization of the original point cloud data.

[0066] Step 1.2: The elevation histogram is used to process the normalized raw point cloud data to remove interfering point clouds such as the ground, buildings, and trees, enabling the initial extraction of the power line point cloud. By traversing the resulting point cloud data, an elevation distribution map of the point cloud data is obtained. Since power lines typically have an overhead height of 10-15 meters and the elevation differences between them are relatively small after normalization, initial extraction of the power lines can be achieved by setting an appropriate threshold to filter out background points.

[0067] Step 2: Transmission line point cloud extraction. This invention designs a transmission line extraction method based on the RANSAC (Random Sample Consensus) algorithm, and performs transmission line point cloud extraction on the preprocessed point cloud data. The point cloud data preprocessed in step 1 contains not only the transmission line point cloud, but also the point cloud of trees and towers near the transmission line. Therefore, this method extracts transmission line point clouds from two perspectives. The specific steps include:

[0068] Step 2.1: Record the point cloud data processed in step 1 as data S, and project S onto the XOY plane;

[0069] Step 2.2: The algorithm begins to iterate, randomly selecting the minimum sample size data points from the projected data. The transmission lines can be viewed as multiple parallel lines after projection, so only two data points need to be randomly initialized in each iteration. These data points are called inliers.

[0070] Step 2.3: According to the selected internal point, the formula Calculate the parameters of the fitted straight line this time, and for the remaining data points, calculate their Euclidean distance s to the straight line. The calculation formula is:

[0071] ;

[0072] Set a threshold, consider data points with a distance less than the threshold as inliers, and data points with a distance greater than the threshold as outliers, and calculate the total distance L of all inliers k , the calculation formula is:

[0073] ;

[0074] The algorithm performs fitting k times in this step. The value of k is determined according to the line conditions. After the fitting is completed, the L value among the K times is selected. k The smallest result is taken as the result of this iteration;

[0075] Step 2.4: Set the number of algorithm iterations m according to the number of transmission lines in the actual line, iterate step 2.3 and remove the straight lines obtained in each iteration to prevent repeated extraction. Finally, m transmission line point clouds in this plane can be extracted;

[0076] Step 2.5: Project the data S to the XOZ plane and repeat steps 2.2 to 2.4. In the XOZ plane, use the RANSAC algorithm to perform line detection to extract the transmission line point cloud. The straight line fitted in the XOZ plane corresponds to the arc missing when extracting the transmission line in the XOY plane. The transmission line point clouds extracted from the XOY and XOZ planes are combined to obtain a complete transmission line point cloud.

[0077] Step 3: Transmission Line Modeling. When performing tree barrier detection on a transmission line, in order to efficiently and accurately measure the distance from the tree barrier to the transmission line, the present invention uses the catenary equation to perform a three-dimensional reconstruction of the transmission line extracted in step 2.

[0078] The catenary equation can describe the basic relationship between conductor sag and stress, specific load and span, and the expression is:

[0079] ;

[0080] Where, is the stress at the lowest point of the conductor, g is the acceleration due to gravity, y is the sag of the conductor, x is the arc length span, cosh is the hyperbolic cosine function, C1 and C2 are integration constants, and their values are determined by the position of the coordinate origin and the initial conditions.

[0081] According to the collected data type, the catenary formula is expanded according to the Taylor series as follows: ; Then convert it into a polynomial equation in three-dimensional space, the expression is:

[0082] ;

[0083] In the formula, p and q are the parameters of the projection line of the transmission line on the two-dimensional plane. Finally, the least squares method is used to fit and solve the polynomial model coefficients.

[0084] Step 4: Tree obstacle detection. After the power line extraction is complete, a spatial index structure is established to search for points other than the power line point cloud. If the distance between a point and any power line is less than a safety threshold, it is identified as an obstacle point. However, the detected obstacle points will also include some tower point clouds in addition to the tree obstacle point cloud. Therefore, the tower points must be removed from the obstacle point cloud. The specific steps are as follows:

[0085] Step 4.1: Use the non-transmission line point cloud as data to establish a spatial index structure. Then, traverse and calculate the Euclidean distance between all points in the non-transmission line point cloud and the transmission line. Mark all point clouds with a distance less than the safety threshold as obstacle point clouds.

[0086] Step 4.2: At this point, the hidden danger point cloud contains not only the tree obstacle point cloud but also the tower point cloud. Therefore, it is necessary to find the exact location of the tower and remove the tower point cloud from the obstacle point cloud. The present invention designs an adaptive height quantity statistics method to locate the tower point. The point cloud density of the tower in the transmission channel is relatively large and the point cloud density of different tower heights in the same line is basically the same, while the point cloud density on the two sides of the tower is relatively small (such as Figure 2 As shown), based on this feature, the point cloud elevation density factor is calculated , and use Draw a statistical histogram and locate the tower point according to the extreme point in the graph. The specific steps are as follows:

[0087] Step 4.2.1: Traverse the obstacle point cloud along the horizontal X axis to calculate the elevation factor , the calculation formula is:

[0088] ;

[0089] Among them, m i It represents the number of point clouds with the same X-axis coordinate as the i-th point and a Y-axis coordinate value greater than 10. H represents the height of the tower in this line;

[0090] Step 4.2.2: Calculate the height factor Draw a statistical histogram. In the same line, the tower height, density and diameter are the same, but the point cloud density at the tower boundary varies greatly. Therefore, the point cloud density at the tower boundary varies greatly. The tower point in the obstacle point cloud can be determined by the position of the maximum point of . After removing the tower point, the remaining point cloud is the tree obstacle in the transmission channel.

[0091] In this embodiment, the main focus is on the application and verification of a method for detecting tree-obstruction hazards in power transmission channels:

[0092] Specifically, for a 10KV transmission line in a certain place, a drone equipped with a lidar was used to collect original point cloud data, such as Figure 3In this point cloud data, there are not only many hidden dangers of tree obstacles but also large terrain fluctuations, which can be detected using the algorithm designed by the present invention.

[0093] right Figure 3 The original point cloud data is conducive to normalizing the digital surface model, and the transmission line is preliminarily extracted by combining the elevation histogram algorithm to realize point cloud data preprocessing. The results are as follows: Figure 4 As shown in the figure, it can be seen that this algorithm can effectively normalize the point cloud elevation information and completely extract the transmission line point cloud to achieve the effect of differential downsampling. This can greatly reduce the amount of point cloud data and the number of interference points in the original point cloud data, and can effectively improve the subsequent transmission line extraction capability;

[0094] The RANSAC algorithm is used to extract the transmission line point cloud in the XOY and XOZ planes respectively, and the transmission line modeling is performed using the method proposed in step 3. The transmission line modeling results are shown in Figure 2. Figure 5 As shown in the figure, extracting transmission lines from two directions and combining them with the catenary equation for modeling achieves good results. This route has three transmission lines and 2,775 transmission line points. The effectiveness of this method is evaluated by the transmission line point cloud extraction accuracy and fitting accuracy. The transmission line point cloud extraction accuracy is equal to the number of extracted transmission line points divided by the number of transmission line point clouds in the original point cloud data. The fitting accuracy is calculated by comparing the mean error between the fitted transmission line point elevation values and the actual transmission line point elevation values. The extraction accuracy is shown in Table 1, and the fitting accuracy is shown in Table 2.

[0095]

[0096] Table 1 Transmission line extraction accuracy

[0097]

[0098] Table 2 Transmission line fitting accuracy table

[0099] The obstacle detection results of this algorithm are as follows Figure 6 As shown in the red area (black box), a total of six tree obstacles were detected during this inspection. A field survey of the line was conducted using a total station to verify the detection accuracy of the algorithm. The field survey results found a total of six tree obstacle hazards. The field survey results were consistent with the results of the algorithm of the present invention, which also proves the effectiveness of the present invention. The specific tree obstacle information is shown in Table 3.

[0100]

[0101] Table 3 Transmission channel tree obstacle hidden danger detection table

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting tree obstacles in power transmission channels based on 3D point clouds, characterized by: The detection steps include the following: Step 1: Based on the elevation histogram filtering method of the normalized digital surface model, the interference point clouds of the ground, buildings, and trees in the original point cloud data of the transmission channel are filtered out, and the transmission line point cloud data is preliminarily extracted and preprocessed: Step 1.1: Use the normalized digital surface model to process the transmission channel point cloud data to achieve elevation normalization of the point cloud data and eliminate the impact of terrain height differences on the ability to initially extract the transmission line point cloud from the elevation histogram; Step 1.2: Use the elevation histogram to process the normalized raw point cloud data, remove the interference point clouds of the ground, buildings, and trees, traverse the point cloud data at this time to obtain the point cloud data elevation distribution map, set the threshold to filter the background points, and realize the preliminary extraction of the transmission line point cloud data; Step 2: Based on the transmission line extraction method of the random sampling consistency RANSAC algorithm, the transmission line point cloud data after data preprocessing is extracted: Step 2.1: Record the point cloud data processed in step 1 as data S, and project S onto the XOY plane; Step 2.2: The algorithm starts to iterate, randomly selecting the data point with the minimum sample size from the projected data, and randomly initializing two data points as inliers in each iteration; Step 2.3: According to the selected internal point, the formula Calculate the parameters of the fitted straight line and calculate the Euclidean distance s from the remaining data points to the straight line. The calculation formula is: ； Set a threshold, consider data points with distances less than the threshold as inliers, and data points with distances greater than the threshold as outliers, and calculate the total distance L of all inliers. k , the calculation formula is: ； The algorithm performs fitting k times in this step. The value of k is determined according to the line conditions. After the fitting is completed, the L value among the K times is selected. k The smallest result is taken as the result of this iteration; Step 2.4: Set the number of algorithm iterations m according to the number of transmission lines in the actual line, iterate step 2.3 and remove the straight lines obtained in each iteration, and finally extract the point cloud of m transmission lines in this plane; Step 2.5: Project the data S to the XOZ plane and repeat steps 2.2 to 2.

4. Use the RANSAC algorithm to perform line detection in the XOZ plane to extract the transmission line point cloud. The straight line fitted in the XOZ plane corresponds to the arc missing when extracting the transmission line in the XOY plane. Combine the transmission line point clouds extracted from the XOY and XOZ planes to obtain a complete transmission line point cloud. Step 3: Detect tree barriers around the transmission lines. Specifically, measure the distance from the tree barriers around the transmission lines to the transmission lines. Use the catenary equation to perform 3D reconstruction of the transmission lines extracted in step 2: The catenary equation used specifically describes the basic relationship between conductor sag and stress, specific load, and span, and the expression is: ； Where, is the stress at the lowest point of the conductor, g is the acceleration of gravity, y is the sag of the conductor, x is the arc length span, cosh is the hyperbolic cosine function, C1 and C2 are integration constants, whose values are determined by the position of the coordinate origin and the initial conditions; According to the collected data type, the catenary formula is expanded according to the Taylor series as follows: ; Convert the Taylor expansion expression into a polynomial equation in three-dimensional space, the expression is: ； In the formula, p and q are the parameters of the projection line of the transmission line on the two-dimensional plane. Finally, the least squares method is used to fit and solve the polynomial model coefficients; Step 4: After the transmission line extraction is completed, a spatial index structure is established to search for points other than the transmission line point cloud. If the distance between a point and any transmission line is less than the safety threshold, it is judged as an obstacle point, and the tower points are removed from the obstacle point cloud at this time: Step 4.1: Use the non-transmission line point cloud as data to establish a spatial index structure, traverse and calculate the Euclidean distance between all points in the non-transmission line point cloud and the transmission line, and mark all point clouds with distances less than the safety threshold as obstacle point clouds; Step 4.2: Use the adaptive elevation quantity statistics method to locate the tower point, and calculate the point cloud elevation density factor ,based on Draw a statistical histogram, locate the tower position according to the extreme points in the graph, and remove the tower obstacle point cloud; Step 5: Based on the transmission channel point cloud data after the tower obstacle point cloud is removed, detect tree obstacle hazards around the transmission channel.

2. The method for detecting tree obstacles in power transmission channels based on 3D point clouds according to claim 1, characterized in that: The specific method for processing the transmission channel point cloud data in step 1.1 is: Step 1.1.1: Traverse the point cloud data and obtain the coordinates p of each point i and the corresponding elevation value z i , calculate the average elevation value μ of all points in the point cloud data, the calculation formula is: ； Step 1.1.2: Calculate the normalized elevation value N of each point i , the calculation formula is: ； Among them, p iz Represents point p i The Z-axis coordinate value of σ represents the standard deviation of the elevation values of all points in the point cloud data, and the calculation formula is: ； Step 1.1.3: Traverse the elevation value of each point in the point cloud data and subtract the normalized elevation value N i , get the normalized elevation value, and realize the elevation normalization of the original point cloud data.

3. The method for detecting tree obstacles in power transmission channels based on 3D point clouds according to claim 1, characterized in that: The specific method for locating the tower position and removing the tower obstacle point cloud in step 4.2 is: Step 4.2.1: Traverse the obstacle point cloud along the horizontal X axis to calculate the elevation factor , the calculation formula is: ； Among them, m i It represents the number of point clouds with the same X-axis coordinate as the i-th point and a Y-axis coordinate value greater than 10. H represents the height of the tower in this line; Step 4.2.2: Calculate the height factor Draw a statistical histogram by locating the The tower point in the obstacle point cloud is determined by the position of the maximum point of . After removing the tower point, the remaining point cloud is the tree obstacle in the transmission channel.

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