Power transmission and distribution line analysis method based on three-dimensional laser point cloud

Abstract

The invention proposes a three-dimensional laser point cloud-based power transmission and distribution line analysis method, and the method comprises the following steps: carrying out the flight operation through employing an unmanned plane, carrying out the carrying of a three-dimensional laser scanner and a high-definition camera, collecting the point cloud data and image data of a power grid infrastructure scene, and carrying out the reconstruction of a three-dimensional model; segmenting the point cloud data, and distinguishing the ground, buildings and trees; and performing tree detection on the image, and outputting a bounding box, a type and a confidence coefficient of the tree. According to the method, lodging prediction is carried out by combining the health condition of the tree, the meteorological data and the soil environment data, and compared with the prior art, the method can be used for a power grid infrastructure scene with only poles and towers and without wires, and the reliability of the power grid infrastructure scene is greatly improved during actual simulation of wire erection. The method can effectively predict the possible tree lodging condition in the later-stage lead use process, facilitates the proceeding of the lead erection work in the initial stage, greatly reduces the operation complexity, and improves the intelligent means of line operation and maintenance.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission and distribution line analysis, and in particular to a power transmission and distribution line analysis method based on three-dimensional laser point cloud. Background Art

[0002] Power grid security is an important part of social public security. Most of the hidden dangers of power grid failure are caused by tree obstacles. In order to ensure the safety of the power grid and the power supply, relevant personnel should inspect the power line environment in a timely manner and conduct tree obstacle analysis. They should also use aerial survey equipment such as drones to conduct flight surveys along the power lines to obtain point cloud data, and then conduct tree obstacle analysis.

[0003] For example, a Chinese patent (publication number CN 115311551 A) discloses a tree barrier analysis method, device and electronic device based on pole tower hanging wires and pole tower spacing. The tree barrier analysis method includes the following steps: obtaining initial point cloud data, obtaining tree barrier point cloud data from the initial point cloud data; obtaining the pole tower hanging wire method and the pole tower coordinates of two adjacent pole towers, and determining the target hidden danger area according to the pole tower hanging wire method and the pole tower coordinates of the adjacent pole towers; performing tree barrier hidden danger analysis based on the tree barrier point cloud data and the target hidden danger area. The tree barrier analysis method can determine the target hidden danger area including power lines based on the pole tower hanging wires and the pole tower spacing, and then determine whether the vegetation point cloud falls within the target hidden danger area based on the positioning of the tree barrier point cloud data, thereby realizing tree barrier analysis more accurately.

[0004] However, the existing technology conducts tree barrier analysis based on the current status of the trees and fails to predict the later stage of tree lodging. Summary of the invention

[0005] The purpose of the present invention is to propose a power transmission and distribution line analysis method based on three-dimensional laser point cloud, which can effectively predict the tree falling situation that may occur during the use of the later conductors, which is beneficial to the initial conductor installation work, greatly reduces the cumbersomeness of operation, and improves the intelligent means of line operation and maintenance.

[0006] To achieve the above object, the technical solution of the present invention is: a power transmission and distribution line analysis method based on three-dimensional laser point cloud, comprising the following steps:

[0007] S1. Use drones equipped with 3D laser scanners and high-definition cameras to perform flight operations, collect point cloud data and image data of power grid infrastructure scenes, and reconstruct 3D models;

[0008] S2. Segment the point cloud data to distinguish the ground, buildings, and trees; perform tree detection on the image and output the bounding box, type, and confidence of the tree; combine the point cloud data to perform 3D reconstruction of the detected trees to obtain accurate height and shape information;

[0009] S3. Analyze and evaluate the health status of trees, and make lodging predictions based on meteorological data and soil environment data;

[0010] S4. Construct a virtual wire in the three-dimensional model and output a lodging warning based on the position of the virtual wire.

[0011] Preferably, the power grid infrastructure scenario includes a power transmission and distribution area and a slope area.

[0012] Preferably, the health status of the trees in S3 includes crown density and trunk curvature, which can be obtained as follows:

[0013] Crown density: extract the crown area from the image data, calculate the area of ​​the crown area, and obtain the ratio of the crown area to the total area of ​​the trees;

[0014] Trunk curvature: Extract trunk points from point cloud data and calculate using the least squares method or curve fitting method.

[0015] Preferably, the meteorological data in S3 includes wind data, and the force of wind on trees is:

[0016]

[0017] Where ρ is the air density, C d is the drag coefficient, A is the canopy projection area, and v is the wind speed;

[0018] The lodging moment of a tree under wind load is calculated based on trunk diameter, height and soil type.

[0019] Preferably, the environmental data in S3 includes soil erosion amount and slope, and the acquisition method is as follows:

[0020] Soil erosion: A s =R*K*LS*C*P

[0021] Among them, A s is the amount of soil erosion, R is the rainfall erosivity factor, K is the soil erodibility factor, LS is the topographic factor, C is the vegetation cover and management factor, and P is the soil and water conservation measures factor;

[0022] slope:

[0023] Where Δz is the change in elevation and Δd is the change in horizontal distance.

[0024] Preferably, the specific process of analyzing and evaluating the health status of trees and predicting lodging in combination with meteorological data and soil environment data is as follows:

[0025] S31. Standardize the crown density, trunk curvature, wind force on trees, anti-lodging moment, soil erosion, and slope;

[0026] S32. Calculate the lodging risk index based on the standardized data:

[0027]

[0028] Among them, w1, w2, w3, w4, w5 are the weights of each factor.

[0029] Preferably, the specific process of constructing the virtual wire in the three-dimensional model is:

[0030] Select the two end points of the wire to automatically form a line, use the parabola fitting formula to implement the wire fitting algorithm to fit the wire shape, and provide wire endpoint offset, manual adjustment of sag, and angle adjustment to simulate wire offset functions to complete the virtual wire simulation.

[0031] Preferably, the output of the lodging warning based on the position of the virtual wire specifically includes projection simulation of fallen tree barriers and display of analysis information on the cause of lodging.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] The present invention combines the health status of trees, meteorological data and soil environmental data to predict tree fall. Compared with the prior art, the present invention can be used in power grid infrastructure scenarios with only poles and towers but no wires. When simulating the actual installation of wires, it can effectively predict the tree fall that may occur during the use of the wires in the later stage, which is beneficial to the initial installation of wires, greatly reduces the complexity of operations, and improves the intelligent means of line operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A schematic diagram of constructing a virtual wire for the power transmission and distribution line analysis method of the present invention. DETAILED DESCRIPTION

[0035] The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings.

[0036] Reference Figure 1 The present invention proposes a power transmission and distribution line analysis method based on three-dimensional laser point cloud, comprising the following steps:

[0037] Use drones equipped with 3D laser scanners and high-definition cameras to perform flight operations, collect point cloud data and image data of power grid infrastructure scenes, and reconstruct 3D models;

[0038] Filter and denoise the point cloud data, and then segment it to distinguish between the ground, buildings, and trees;

[0039] Preprocess the image data, such as denoising, resizing, and contrast enhancement, to improve the effect of model training;

[0040] Perform tree detection on the image and output the tree’s bounding box, type, and confidence level.

[0041] Combined with point cloud data, the detected trees are reconstructed in 3D to obtain accurate height and shape information;

[0042] Analyze and evaluate the health status of trees, and make lodging predictions based on meteorological data and soil environment data;

[0043] Construct a virtual wire in the 3D model and output a lodging warning based on the position of the virtual wire.

[0044] In this embodiment, the power grid infrastructure scenario includes a power transmission and distribution area and a slope area.

[0045] In this embodiment, in the process of analyzing and evaluating the health status of trees and performing lodging prediction in combination with meteorological data and environmental data, the health status of trees includes:

[0046] Crown density: extract the crown area from the image data, calculate the area of ​​the crown area, and obtain the ratio of the crown area to the total area of ​​the trees;

[0047] The curvature of the trunk: extract the trunk points from the point cloud data and calculate them using the least squares method or curve fitting method;

[0048] The health status of trees is determined by their species. The crown density and trunk curvature of each species of tree have standard values, which are the standard values ​​obtained through machine learning algorithms (such as decision trees) based on the same species, the same soil environment, and the same number of years of growth.

[0049] In this embodiment, in the process of analyzing and evaluating the health status of trees and combining meteorological data and environmental data to predict lodging, the meteorological data includes:

[0050] Wind forces on trees:

[0051] Where ρ is the air density, C d is the drag coefficient, A is the canopy projection area, and v is the wind speed;

[0052] The tree's lodging resistance moment calculated based on trunk diameter, height and soil type.

[0053] In this embodiment, the analysis and evaluation of the health status of trees and the combination of meteorological data and environmental data in the process of lodging prediction include:

[0054] Soil erosion: A s =R*K*LS*C*P

[0055] Among them, A s is the amount of soil erosion, R is the rainfall erosivity factor, K is the soil erodibility factor, LS is the topographic factor, C is the vegetation cover and management factor, and P is the soil and water conservation measures factor;

[0056] Generate digital elevation models (DEMs) using LiDAR point cloud data;

[0057] Calculate slope from DEM:

[0058] Where Δz is the change in elevation and Δd is the change in horizontal distance.

[0059] In this embodiment, the specific process of analyzing and evaluating the health status of trees and predicting lodging in combination with meteorological data and soil environment data is as follows:

[0060] Standardize the crown density, trunk curvature, wind force on trees, lodging resistance, soil erosion, and slope;

[0061] The lodging risk index is calculated based on the standardized data:

[0062]

[0063] Among them, w1, w2, w3, w4, and w5 are the weights of each factor, which can be determined by expert scoring, data analysis, or machine learning algorithms. In this formula, the ratio of the anti-lodging moment to the wind force has a negative impact on the lodging risk (i.e., the larger the ratio, the smaller the lodging risk), while other factors have a positive impact (i.e., the larger the value, the greater the lodging risk).

[0064] In this embodiment, the specific process of constructing the virtual wire in the three-dimensional model is:

[0065] Select the two end points of the wire to automatically form a line, use the parabola fitting formula to implement the wire fitting algorithm to fit the wire shape, and provide wire endpoint offset, manual adjustment of sag, and angle adjustment to simulate wire offset functions to complete the virtual wire simulation.

[0066] In this embodiment, the output of the lodging warning based on the position of the virtual wire specifically includes projection simulation of fallen tree barriers and display of analysis information on the cause of lodging.

[0067] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A power transmission and distribution line analysis method based on three-dimensional laser point cloud, characterized in that: The following steps are involved: S1. Use drones equipped with 3D laser scanners and high-definition cameras to perform flight operations, collect point cloud data and image data of power grid infrastructure scenes, and reconstruct 3D models; S2. Segment the point cloud data to distinguish the ground, buildings, and trees; perform tree detection on the image and output the bounding box, type, and confidence of the tree; combine the point cloud data to perform 3D reconstruction of the detected trees to obtain accurate height and shape information; S3. Analyze and evaluate the health status of trees, and make lodging predictions based on meteorological data and soil environment data; S4. Construct a virtual wire in the three-dimensional model and output a lodging warning based on the position of the virtual wire.

2. The power transmission and distribution line analysis method based on three-dimensional laser point cloud according to claim 1, characterized in that: The power grid infrastructure scenario includes a power transmission and distribution area and a slope area.

3. The power transmission and distribution line analysis method based on three-dimensional laser point cloud according to claim 1, characterized in that: The health status of trees in S3 includes crown density and trunk curvature, which can be obtained as follows: Crown density: extract the crown area from the image data, calculate the area of ​​the crown area, and obtain the ratio of the crown area to the total area of ​​the trees; Trunk curvature: Extract trunk points from point cloud data and calculate using the least squares method or curve fitting method.

4. The power transmission and distribution line analysis method based on three-dimensional laser point cloud according to claim 3 is characterized in that: The meteorological data in S3 includes wind data, and the force of wind on trees is expressed as: Where ρ is the air density, C d is the drag coefficient, A is the canopy projection area, and v is the wind speed; The lodging moment of a tree under wind load is calculated based on trunk diameter, height and soil type.

5. The power transmission and distribution line analysis method based on three-dimensional laser point cloud according to claim 4 is characterized in that: The environmental data in S3 include soil erosion and slope, which can be obtained as follows: Soil erosion: A s =R*K*LS*C*P Among them, A s is the amount of soil erosion, R is the rainfall erosivity factor, K is the soil erodibility factor, LS is the topographic factor, C is the vegetation cover and management factor, and P is the soil and water conservation measures factor; slope: Where Δz is the change in elevation and Δd is the change in horizontal distance.

6. The power transmission and distribution line analysis method based on three-dimensional laser point cloud according to claim 5, characterized in that: The specific process of analyzing and evaluating the health status of trees and predicting their fall by combining meteorological data and soil environment data is as follows: S31. Standardize the crown density, trunk curvature, wind force on trees, anti-lodging moment, soil erosion, and slope; S32. Calculate the lodging risk index based on the standardized data: Among them, w1, w2, w3, w4, w5 are the weights of each factor.

7. The power transmission and distribution line analysis method based on three-dimensional laser point cloud according to claim 1, characterized in that: The specific process of constructing the virtual wire in the three-dimensional model is as follows: Select the two end points of the wire to automatically form a line, use the parabola fitting formula to implement the wire fitting algorithm to fit the wire shape, and provide wire endpoint offset, manual adjustment of sag, and angle adjustment to simulate wire offset functions to complete the virtual wire simulation.

8. The power transmission and distribution line analysis method based on three-dimensional laser point cloud according to claim 7 is characterized in that: The output of the lodging warning based on the position of the virtual wire specifically includes projection simulation of fallen tree barriers and display of analysis information on the cause of lodging.

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