CN116739558A - Tree obstacle hidden danger early warning method based on laser point cloud modeling - Google Patents
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Abstract
The application belongs to the field of distribution network fault early warning, and particularly relates to a tree obstacle hidden danger early warning method based on laser point cloud modeling. Firstly, acquiring electric power corridor point cloud data by using an airborne laser radar of an unmanned aerial vehicle, then extracting ground points, power line points and vegetation points based on the preprocessed point cloud data, drawing an electric power corridor line curve according to the ground points, establishing a three-dimensional laser model, and analyzing electric power corridor tree obstacle risk points based on the three-dimensional laser model. The application is different from the current on-site inspection, can perform early warning more advanced, reduces the power failure times of line faults, avoids periodic and purposeless blind type whole-domain inspection, accurately matches inspection in two dimensions of time and place, and improves inspection quality.
Description
Technical Field
The application belongs to the field of distribution network fault early warning, and particularly relates to a tree obstacle hidden danger early warning method based on laser point cloud modeling.
Background
At present, the electric power corridor space is scarce, and the electric transmission line is often erected in mountain areas, fields and forest lands. The growth of trees below the power transmission line threatens the safety distance, easily causes tree obstacle accidents such as short circuit tripping or tree firing, and seriously influences forest safety and power grid safety.
The current tree obstacle risk prevention mainly comprises modes of manual line inspection, unmanned aerial vehicle multipoint shooting return, unmanned aerial vehicle airborne laser radar and the like. The manual inspection has the defects of large labor capacity, low efficiency, uneven quality and the like; although the unmanned aerial vehicle multi-point shooting return saves manpower to a certain extent, the unmanned aerial vehicle still needs manual image viewing in the later period, and the defects of uneven quality and large workload still exist; the unmanned plane carrying the laser radar can acquire high-precision high-density three-dimensional space information of the terrain and ground objects near the electric power corridor, has the advantages of quick inspection, quality improvement and the like, and gradually enters various fields in recent years. How to combine the unmanned aerial vehicle laser point cloud technology with the tree obstacle hidden danger detection and apply to actual work becomes the focus of solving above-mentioned problem.
Disclosure of Invention
The current tree obstacle risk prevention mainly comprises modes of manual line inspection, unmanned aerial vehicle multipoint shooting return, unmanned aerial vehicle airborne laser radar and the like. The manual inspection has the defects of large labor capacity, low efficiency, uneven quality and the like; although the unmanned aerial vehicle multi-point shooting return saves manpower to a certain extent, the unmanned aerial vehicle still needs manual image viewing in the later period, and the defects of uneven quality and large workload still exist; the unmanned plane carrying the laser radar can acquire high-precision high-density three-dimensional space information of the terrain and ground objects near the electric power corridor, has the advantages of quick inspection, quality improvement and the like, and gradually enters various fields in recent years. How to combine the unmanned aerial vehicle laser point cloud technology with the tree obstacle hidden danger detection and apply to actual work becomes the focus of solving above-mentioned problem.
In order to solve the defects in the prior art, the application provides a tree obstacle hidden danger early warning method based on laser point cloud modeling, which scans a distribution network channel through the laser point cloud, establishes a distribution network tree obstacle laser point cloud three-dimensional model, realizes the acquisition of tree obstacle type, height and position information in the ground, calculates and analyzes tree obstacle risk points, and provides a risk point time-place matrix.
The application adopts the following technical scheme. Step S1: acquiring power corridor point cloud data by using an airborne laser radar of an unmanned aerial vehicle, dividing a horizontal space, obtaining a reference plane of a scanned area through an elevation filter, denoising the point cloud data, and preprocessing the point cloud data to obtain preprocessed point cloud data;
step S2: extracting ground points, power line points and vegetation points based on the preprocessed point cloud data, wherein the ground points are reference planes in S1 and serve as reference horizontal planes of the graph;
step S3: based on the preprocessed point cloud data, combining adjacent two elevation maxima, and adopting polynomial fitting to obtain a power corridor line curve for fitting line trend between every two base towers;
step S4: based on the preprocessed point cloud data, the extracted ground points, power line points, vegetation points and power corridor line curves are used for establishing a three-dimensional laser model;
step S5: and analyzing the power corridor tree obstacle risk points based on the three-dimensional laser model.
Preferably, in step S1, denoising preprocessing is performed on the point cloud data, and the preprocessing method specifically includes: and filtering point clouds existing in the space and point clouds on the ground by adopting a mobile curved surface fitting method, and analyzing to obtain the ground elevation.
Further preferably, the moving curved surface fitting method in step S1 specifically includes:
considering the ground as a space complex curved surface, adding a window neighborhood in consideration of the continuity of the terrain, considering that the fitting curved surface of each window is obtained based on the lowest point of the neighborhood window, and fitting the local surface element of the curved surface by using a quadric surface as shown in a formula (1); when the bin is less than the threshold, the fitting may be approximated with equation (2);
Z i ＝f(X i ,Y i )＝a 0 +a 1 X i +a 2 Y i (2)
wherein X, Y, Z are three-axis coordinates of a three-dimensional coordinate system, i refers to the fitting of the ith bin, a 0 ,a 1 ,a 2 ,a 3 ,a 4 ,a 5 Is a fitting parameter.
Through point cloud data analysis, a corresponding threshold value is set according to environmental factors of a scanned area to remove maximum and minimum values, denoising is carried out, point cloud data is divided according to terrains, plane coordinates of the point cloud data are marked, each grid can use a formula (1) to take a lowest point fitting curved surface as a ground point, a difference value between a fitting elevation value and a real elevation value in each grid is calculated, and threshold value screening is set to finish point cloud filtering.
Preferably, the specific content of step S2 includes:
the method is characterized in that ground point extraction is realized by a self-adaptive threshold point cloud filtering method based on moving surface fitting;
based on the spatial distribution characteristics and projection distribution characteristics of the power line point cloud, a random sampling consistency algorithm is adopted to realize power line point extraction;
and the vegetation point extraction is realized based on the relative height difference of the vegetation distribution area and the tower distribution area and the huge difference of the geometric forms of trees and buildings.
Further preferably, the power line point extraction specifically includes: after the extraction of the ground points is realized, the residual elements of the point cloud data comprise suspended power lines and vegetation, the power lines are roughly extracted by utilizing space grid division and combining with Euclidean clustering according to the up-down continuity of the power lines, and then the accurate extraction of the power line points is realized by adopting a random sampling consistency algorithm according to the projection distribution characteristics of the power line points; based on linear distribution characteristics of the power line projection on a horizontal plane, removing noise points which are not on the same line with the power line by utilizing a random sampling consistency algorithm linear fitting method, then based on parabolic distribution characteristics of the power line projection on a vertical plane, removing noise points which are on the same vertical plane with the high-voltage line by utilizing a random sampling consistency algorithm parabolic fitting method, and finishing fine extraction of the power line point cloud.
Further preferably, the extraction of vegetation points specifically includes: after the extraction of the ground points and the power line points is realized, the residual elements of the point cloud data comprise towers and buildings, and the corresponding height threshold value is set to remove the towers according to the difference between the on-site vegetation height and the tower height; then according to the difference of the geometric forms of the tree and the building, the accurate separation of the vegetation points and the building points is realized, and the accurate separation method comprises the following steps:
firstly, based on the building with smooth surface and the normal vector of local surface elements tending to be vertical, the normal vector among the surface elements is also nearly parallel, which is different from the characteristic of strong randomness of the plant leaf distribution direction, and the included angle of the normal vector is taken as a method for screening vegetation points;
secondly, calculating a local surface element normal vector of each point based on the local point cloud, comparing the local surface element normal vector of the target point with that of the adjacent point, calculating an included angle variance, establishing a frequency histogram, setting a trough position point as a threshold value according to the bimodal distribution characteristics, and setting a building point as a lower point, or else, setting a vegetation point as a lower point.
Preferably, the specific step of fitting the power corridor line curve to the line trend between every two base towers in the step S3 includes:
the electric power corridor line curve fitting has the following characteristics that the electric power corridor line curve fitting can be approximated as a straight line on an XY plane, and the electric power corridor line curve fitting is performed by using a formula (3); it can be approximated as a conic in the XZ plane and YZ plane, fitted with equation (4). Taking the center of the power line on the XY plane as the center point of the fitted power corridor curve, the center point can be represented by formula (5).
y＝kx+b (3)
Wherein (x, y, z) is the point cloud space coordinates, k, b, s 1 ,s 2 ,s 3 And m is the number of point clouds in the line curve of the electric power corridor for equation coefficients to be solved. And (3) and (4) and (5) of the simultaneous formulas can obtain a power corridor line curve equation and obtain a three-dimensional power corridor scanning diagram.
Preferably, the three-dimensional convex hulls of the power line vectors and the vegetation are generated through three-dimensional reconstruction, the three-dimensional convex hulls of the power line points, the vegetation points and the power corridor line curves are generated based on the formulas (3) (4) (5) in the step S3, and the distance d between the vegetation convex hull points and the power line vectors is calculated.
d＝h t -h l (6)
Wherein h is t ,h l Respectively the heights of vegetation points and power line points, analyzing the power corridor tree barrier risk points based on the three-dimensional laser model,
when d > d s When there is no risk at this point, where d s Is a safe distance; when d is less than or equal to d s When the point is a power corridor tree obstacle risk point,
and (3) calculating the current point-line distance and the time which is not greater than the safety distance according to a formula (7) based on the growth characteristic parameters of different tree species, and forming a tree barrier risk point time-place matrix.
Wherein t is pn For next tour time, t pc And v is the growth speed of the tree for the current tour time.
The application has the beneficial effects that compared with the prior art:
1. the laser point cloud technology can rapidly acquire high-precision high-density three-dimensional space information of the terrain and ground objects near the power corridor, and meanwhile, high-precision point cloud data filtering and extraction are performed, so that the accuracy of the acquired terrain data is greatly improved, and the working efficiency is greatly improved.
2. The method can plan a future inspection plan taking years as a span according to the growth speed of the existing common tree, and provides considerable convenience for field maintenance work.
3. The line fault power failure warning device is different from the current on-site inspection, early warning can be carried out more advanced, line fault power failure times are reduced, periodic and purposeless blind type whole-domain inspection is avoided, inspection in two dimensions of time and place is accurately matched, and inspection quality is improved.
Drawings
FIG. 1 is a flow chart of a tree obstacle hidden danger early warning method based on laser point cloud modeling provided by an embodiment of the application;
FIG. 2 is a three-dimensional power corridor scan;
FIG. 3 is a graph of growth rates of different common trees;
fig. 4 is a time-place matrix of tree barrier risk points formed by taking poplar and pinus koraiensis as examples under a certain line in a certain area.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. The described embodiments of the application are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art without making any inventive effort, are within the scope of the present application.
Fig. 1 is a flowchart of a tree obstacle hidden danger warning method based on laser point cloud modeling, which is provided by an embodiment of the present application, and includes:
step S1: acquiring power corridor point cloud data by using an airborne laser radar of an unmanned aerial vehicle, dividing a horizontal space, obtaining a reference plane of a scanned area through an elevation filter, denoising the point cloud data, and preprocessing the point cloud data to obtain preprocessed point cloud data;
step S2: extracting ground points, power line points and vegetation points based on the preprocessed point cloud data, wherein the ground points are reference planes in S1 and serve as reference horizontal planes of the graph;
step S3: based on the preprocessed point cloud data, combining adjacent two elevation maxima, and adopting polynomial fitting to obtain a power corridor line curve for fitting line trend between every two base towers;
step S4: based on the preprocessed point cloud data, the extracted ground points, power line points, vegetation points and power corridor line curves are used for establishing a three-dimensional laser model;
step S5: and analyzing the power corridor tree obstacle risk points based on the three-dimensional laser model.
Specifically, the moving curved surface fitting method in step S1 specifically includes:
considering the ground as a space complex curved surface, adding a window neighborhood in consideration of the continuity of the terrain, considering that the fitting curved surface of each window is obtained based on the lowest point of the neighborhood window, and fitting the local surface element of the curved surface by using a quadric surface as shown in a formula (1); when the bin is less than the threshold, i.e., the selected bin is approximately planar, such as a flat or hilly region with a diameter of no more than 5 meters, the approximation fit can be made using equation (2);
Z i ＝f(X i ,Y i )＝a 0 +a 1 X i +a 2 Y i (2)
wherein X, Y, Z are three-axis coordinates of a three-dimensional coordinate system, i refers to the fitting of the ith bin, a 0 ,a 1 ,a 2 ,a 3 ,a 4 ,a 5 Is a fitting parameter.
And (3) through analyzing point cloud data, setting a corresponding threshold value according to the environmental factors of a scanned area to remove the maximum and minimum values, for example, the factors which can cause interference to the wire trend are not generated below 1 meter in the area, removing the point cloud of which the scanned area is lower than 1 meter and the point cloud of which the height is higher than the height of a pole tower, denoising, carrying out grid segmentation on the point cloud data according to the topography, marking the plane coordinates of the point cloud data, using a formula (1) to take the lowest point fitting curved surface of each grid as a ground point, calculating the difference value between the fitting elevation value and the real elevation value in each grid, and setting a threshold value for screening and finishing the point cloud filtering.
Specifically, in step S2, ground points, power line points, and vegetation points are extracted based on the preprocessed point cloud data.
The adaptive threshold point cloud filtering method based on the moving surface fitting realizes ground point extraction. Namely the specific method;
the power line point extraction is realized by adopting a random sampling consistency algorithm based on the spatial distribution characteristics and the projection distribution characteristics of the power line point cloud. The method comprises the following steps: after the ground point extraction is carried out, the residual elements contain suspended power lines, and the power lines are roughly extracted by utilizing space grid division and combining with Euclidean clustering according to the up-down continuity of the power lines. And then according to the projection distribution characteristics of the power line points, adopting a random sampling consistency algorithm to realize the accurate extraction of the power line points. Based on linear distribution characteristics of the power line projection on a horizontal plane, removing noise points which are not on the same line with the power line by utilizing a random sampling consistency algorithm linear fitting method, then based on parabolic distribution characteristics of the power line projection on a vertical plane, removing noise points which are on the same vertical plane with the high-voltage line by utilizing a random sampling consistency algorithm parabolic fitting method, and finishing fine extraction of the power line point cloud.
The extraction of vegetation points specifically comprises: after the extraction of ground points and power line points is realized, towers and buildings are also arranged in the residual elements of the point cloud data, firstly, according to the difference between the on-site vegetation height and the tower height, a corresponding height threshold value is set to remove the towers, and if the height of the towers in the scanned area is 8 meters, a height threshold value of 8.3 meters is set to remove the towers; then according to the difference of the geometric forms of the tree and the building, the accurate separation of the vegetation points and the building points is realized, and the accurate separation method comprises the following steps:
firstly, based on the building with smooth surface and the normal vector of local surface elements tending to be vertical, the normal vector among the surface elements is also nearly parallel, which is different from the characteristic of strong randomness of the plant leaf distribution direction, and the included angle of the normal vector is taken as a method for screening vegetation points;
secondly, calculating a local surface element normal vector of each point based on the local point cloud, comparing the local surface element normal vector of the target point with that of the adjacent point, calculating an included angle variance, establishing a frequency histogram, setting a trough position point as a threshold value according to the bimodal distribution characteristics, and setting a building point as a lower point, or else, setting a vegetation point as a lower point.
Specifically, in step S3: and (3) combining the preprocessed point cloud data with adjacent two elevation maxima, and adopting polynomial fitting to obtain a power corridor line curve for fitting line trend between every two base towers.
The electric power corridor line curve fitting has the following characteristics that the electric power corridor line curve fitting can be approximated as a straight line on an XY plane, and the electric power corridor line curve fitting is performed by using a formula (3); it can be approximated as a conic in the XZ plane and YZ plane, fitted with equation (4). Taking the center of the power line on the XY plane as the center point of the fitted power corridor curve, the center point can be represented by formula (5).
y＝kx+b (3)
Wherein (x, y, z) is the point cloud space coordinates, k, b, s 1 ,s 2 ,s 3 And m is the number of point clouds in the line curve of the electric power corridor for equation coefficients to be solved. And (3) and (4) and (5) of the simultaneous formulas can obtain a power corridor line curve equation and obtain a three-dimensional power corridor scanning diagram.
Specifically, in step S4: based on the preprocessed point cloud data, the extracted ground points, power line points, vegetation points and power corridor line curves are used for establishing a three-dimensional laser model;
and generating a three-dimensional convex hull of the power line vector and the vegetation through three-dimensional reconstruction. And (3) based on the formulas (3) (4) (5) in the step (S3), generating three-dimensional convex hulls of the power line points, the vegetation points and the power corridor line curves, and calculating the distance d between the vegetation convex hull points and the power line vector.
d＝h t -h l (6)
Wherein h is t ,h l The heights of the vegetation points and the power line points are respectively.
Specifically, in step S5: and analyzing the power corridor tree obstacle risk points based on the three-dimensional laser model.
When d > d s When this point is not at risk (where d s Is a safe distance); when d is less than or equal to d s When the power corridor tree obstacle risk point is the power corridor tree obstacle risk point.
Based on growth characteristic parameters (such as table 1) of different tree species, calculating the current point-line distance and the time which is not greater than the safety distance according to a formula (7) to form a tree barrier risk point time-place matrix.
Wherein t is pn For next tour time, t pc And v is the growth speed of the tree for the current tour time.
Taking poplar and Chinese pine under a certain line in a certain area as an example in the present example as an example, a four-tree obstacle risk point time-place matrix is formed.
Through FIG. 4, early warning can be performed according to a tree obstacle risk point time-place matrix.
The application has the beneficial effects that compared with the prior art:
1. the laser point cloud technology can rapidly acquire high-precision high-density three-dimensional space information of the terrain and ground objects near the power corridor, and meanwhile, high-precision point cloud data filtering and extraction are performed, so that the accuracy of the acquired terrain data is greatly improved, and the working efficiency is greatly improved.
2. The method can plan a future inspection plan taking years as a span according to the growth speed of the existing common tree, and provides considerable convenience for field maintenance work.
3. The line fault power failure warning device is different from the current on-site inspection, early warning can be carried out more advanced, line fault power failure times are reduced, periodic and purposeless blind type whole-domain inspection is avoided, inspection in two dimensions of time and place is accurately matched, and inspection quality is improved.
The present disclosure may be a system, method, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
Computer program instructions for performing the operations of the present disclosure can be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the specific embodiments of the application without departing from the spirit and scope of the application, which is intended to be covered by the claims.
Claims (10)
1. A tree obstacle hidden danger early warning method based on laser point cloud modeling is characterized by comprising the following steps:
step S1: acquiring power corridor point cloud data by using an unmanned aerial vehicle laser radar, dividing according to a horizontal space, obtaining a reference plane of a scanning area through an elevation filter, and denoising the point cloud data;
step S2: extracting ground points, power line points and vegetation points based on the preprocessed point cloud data, wherein the ground points are reference planes in S1 and serve as reference horizontal planes of the graph;
step S3: based on the combination of the two adjacent elevation maxima and the preprocessed point cloud data, adopting polynomial fitting to obtain a line curve of the electric power corridor, and fitting line trend between every two base towers;
step S4: based on the extracted ground points, the power line points and the vegetation points, the preprocessed point cloud data and the electric power corridor line curve, a three-dimensional laser model is built;
step S5: and analyzing the power corridor tree obstacle risk points based on the three-dimensional laser model.
2. The tree obstacle hidden danger warning method based on laser point cloud modeling according to claim 1, wherein the denoising pretreatment is performed on point cloud data in step S1, and the pretreatment method specifically comprises the following steps: and filtering point clouds existing in the space and point clouds on the ground by adopting a mobile curved surface fitting method, and analyzing to obtain the ground elevation.
3. The tree obstacle hidden danger warning method based on laser point cloud modeling according to claim 1, wherein the moving curved surface fitting method in step S1 specifically comprises:
considering the ground as a space complex curved surface, adding a window neighborhood in consideration of the continuity of the terrain, considering that the fitting curved surface of each window is obtained based on the lowest point of the neighborhood window, and fitting the local surface element of the curved surface by using a quadric surface as shown in a formula (1); when the bin is less than the threshold, the fitting may be approximated with equation (2);
Z i ＝f(X i ,Y i )＝a 0 +a 1 X i +a 2 Y i (2)
wherein X, Y, Z are three-axis coordinates of a three-dimensional coordinate system, i refers to the fitting of the ith bin, a 0 ,a 1 ,a 2 ,a 3 ,a 4 ,a 5 In order to fit the parameters of the model,
through point cloud data analysis, a corresponding threshold value is set according to environmental factors of a scanned area to remove maximum and minimum values, denoising is carried out, point cloud data is divided according to terrains, plane coordinates of the point cloud data are marked, each grid can use a formula (1) to take a lowest point fitting curved surface as a ground point, a difference value between a fitting elevation value and a real elevation value in each grid is calculated, and threshold value screening is set to finish point cloud filtering.
4. The tree obstacle hidden danger warning method based on laser point cloud modeling as claimed in claim 1, wherein the specific content of step S2 includes:
the method is characterized in that ground point extraction is realized by a self-adaptive threshold point cloud filtering method based on moving surface fitting;
based on the spatial distribution characteristics and projection distribution characteristics of the power line point cloud, a random sampling consistency algorithm is adopted to realize power line point extraction;
and the vegetation point extraction is realized based on the relative height difference of the vegetation distribution area and the tower distribution area and the huge difference of the geometric forms of trees and buildings.
5. The tree obstacle hidden danger warning method based on laser point cloud modeling as claimed in claim 4, wherein the extracting of the power line points specifically comprises: after the extraction of the ground points is realized, the residual elements of the point cloud data comprise suspended power lines and vegetation, the power lines are roughly extracted by utilizing space grid division and combining with Euclidean clustering according to the up-down continuity of the power lines, and then the accurate extraction of the power line points is realized by adopting a random sampling consistency algorithm according to the projection distribution characteristics of the power line points; based on linear distribution characteristics of the power line projection on a horizontal plane, removing noise points which are not on the same line with the power line by utilizing a random sampling consistency algorithm linear fitting method, then based on parabolic distribution characteristics of the power line projection on a vertical plane, removing noise points which are on the same vertical plane with the high-voltage line by utilizing a random sampling consistency algorithm parabolic fitting method, and finishing fine extraction of the power line point cloud.
6. The tree obstacle hidden danger warning method based on laser point cloud modeling as claimed in claim 4, wherein the extracting of vegetation points specifically comprises: after the extraction of the ground points and the power line points is realized, the residual elements of the point cloud data comprise towers and buildings, and the corresponding height threshold value is set to remove the towers according to the difference between the on-site vegetation height and the tower height; then according to the difference of the geometric forms of the tree and the building, the accurate separation of the vegetation points and the building points is realized, and the accurate separation method comprises the following steps:
firstly, based on the building with smooth surface and the normal vector of local surface elements tending to be vertical, the normal vector among the surface elements is also nearly parallel, which is different from the characteristic of strong randomness of the plant leaf distribution direction, and the included angle of the normal vector is taken as a method for screening vegetation points;
secondly, calculating a local surface element normal vector of each point based on the local point cloud, comparing the local surface element normal vector of the target point with that of the adjacent point, calculating an included angle variance, establishing a frequency histogram, setting a trough position point as a threshold value according to the bimodal distribution characteristics, and setting a building point as a lower point, or else, setting a vegetation point as a lower point.
7. The tree obstacle hidden danger warning method based on laser point cloud modeling according to claim 1, wherein the specific step of fitting the line curve of the power corridor between every two base towers in the step S3 comprises the following steps:
the electric power corridor line curve fitting has the following characteristics that the electric power corridor line curve fitting can be approximated as a straight line on an XY plane, and the electric power corridor line curve fitting is performed by using a formula (3); it can be approximated as a conic in the XZ plane and YZ plane, fitted with equation (4). Taking the center of the power line on the XY plane as the center point of the fitted power corridor curve, the center point can be represented by formula (5),
y＝kx+b (3)
wherein (x, y, z) is the point cloud space coordinates, k, b, s 1 ,s 2 ,s 3 And (3) obtaining an equation coefficient to be solved, wherein m is the point cloud number in the electric power corridor line curve, and the electric power corridor line curve equation can be obtained by the simultaneous equations (3), (4) and (5), and a three-dimensional electric power corridor scan map can be obtained.
8. The tree obstacle hidden danger warning method based on laser point cloud modeling according to claim 1, wherein three-dimensional convex hulls of power line vectors and vegetation are generated through three-dimensional reconstruction, three-dimensional convex hulls of power line points and vegetation points and power corridor line curves are generated based on formulas (3) (4) (5) in S3, distance d between vegetation convex hull points and power line vectors is calculated,
d＝h t -h l (6)
wherein h is t ,h l Respectively the heights of vegetation points and power line points, analyzing the power corridor tree barrier risk points based on the three-dimensional laser model,
when d > d s When there is no risk at this point, where d s Is a safe distance; when d is less than or equal to d s When the point is a power corridor tree obstacle risk point,
based on the growth characteristic parameters of different tree species, calculating the current point-line distance and the time not greater than the safety distance according to a formula (7) to form a time-place matrix of tree barrier risk points,
wherein t is pn For next tour time, t pc And v is the growth speed of the tree for the current tour time.
9. A terminal comprising a processor and a storage medium; the method is characterized in that:
the storage medium is used for storing instructions;
the processor being operative according to the instructions to perform the steps of the method according to any one of claims 1-8.
10. Computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any of claims 1-8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116935234A (en) * | 2023-09-18 | 2023-10-24 | 众芯汉创(江苏)科技有限公司 | Automatic classification and tree obstacle early warning system and method for power transmission line corridor point cloud data |
| CN117132915A (en) * | 2023-10-27 | 2023-11-28 | 国网江西省电力有限公司电力科学研究院 | Power transmission line tree obstacle hidden danger analysis method based on automatic classification of point cloud |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116935234A (en) * | 2023-09-18 | 2023-10-24 | 众芯汉创(江苏)科技有限公司 | Automatic classification and tree obstacle early warning system and method for power transmission line corridor point cloud data |
| CN116935234B (en) * | 2023-09-18 | 2023-12-26 | 众芯汉创(江苏)科技有限公司 | Automatic classification and tree obstacle early warning system and method for power transmission line corridor point cloud data |
| CN117132915A (en) * | 2023-10-27 | 2023-11-28 | 国网江西省电力有限公司电力科学研究院 | Power transmission line tree obstacle hidden danger analysis method based on automatic classification of point cloud |
| CN117132915B (en) * | 2023-10-27 | 2024-03-12 | 国网江西省电力有限公司电力科学研究院 | Power transmission line tree obstacle hidden danger analysis method based on automatic classification of point cloud |
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