CN113344242A - Tree barrier hidden danger prediction method based on plant growth model - Google Patents
Tree barrier hidden danger prediction method based on plant growth model Download PDFInfo
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Abstract
The invention discloses a plant growth model-based tree barrier hidden danger prediction method, which comprises the following steps: acquiring laser point clouds of a current power transmission line, and extracting power line point clouds and vegetation point clouds from the laser point clouds; grading the vegetation point cloud, and generating vegetation hidden danger point cloud of the graded vegetation point cloud based on a physical grord plant growth equation; constructing a power line vector corresponding to the power line point cloud, and calculating a three-dimensional convex hull of the vegetation hidden danger point cloud; if the physical distance between the tree convex hull point in the three-dimensional convex hull and the power line vector is smaller than the safety threshold, the tree convex hull point is determined to be a vegetation hidden danger point, and because the vegetation hidden danger point cloud is the point cloud corresponding to the current vegetation growing around the power transmission line, whether the future vegetation growing affects the normal operation of the power transmission line can be predicted.
Description
Technical Field
The invention relates to the field of electric wire inspection, in particular to a method, a device and equipment for predicting hidden danger of tree obstacle based on a plant growth model and a computer readable storage medium.
Background
Trees growing in the power transmission line corridor can cause great influence and harm to the power transmission line, such as short circuit, line tripping, line power failure, and even total substation voltage loss of a power substation, and the trees become a great obstacle affecting the safe operation of the power transmission line, namely a tree obstacle. At present, the existing tree obstacles can be checked, but new tree obstacles can be formed along with the growth of trees, and the potential tree obstacles cannot be detected in the prior art, namely the hidden danger of the tree obstacles cannot be predicted.
Disclosure of Invention
The invention mainly aims to provide a method, a device and equipment for predicting hidden danger of a tree obstacle based on a plant growth model and a computer readable storage medium, and aims to realize prediction of hidden danger of the tree obstacle. The method for predicting the hidden danger of the tree barrier based on the plant growth model comprises the following steps:
acquiring laser point clouds of a current power transmission line, and extracting power line point clouds and vegetation point clouds from the laser point clouds;
grading the vegetation point cloud, and generating the graded vegetation hidden danger point cloud of the vegetation point cloud based on a richard plant growth equation;
constructing a power line vector corresponding to the power line point cloud, and calculating a three-dimensional convex hull of the vegetation hidden danger point cloud;
and if the physical distance between the tree convex hull point in the three-dimensional convex hull and the power line vector is smaller than a safety threshold, determining that the tree convex hull point is a vegetation hidden danger point.
Optionally, the step of extracting a power line point cloud and a vegetation point cloud from the laser point cloud includes:
filtering ground points in the laser point cloud by using a cloth filtering algorithm to obtain a first point cloud to be processed;
and extracting a power line point cloud and a vegetation point cloud from the first point cloud to be processed.
Optionally, the step of extracting a power line point cloud and a vegetation point cloud from the first point cloud to be processed includes:
extracting power line point clouds in the first point cloud to be processed based on a filtering algorithm of the average value of the inclination angles among the point clouds, and obtaining a second point cloud to be processed;
searching a tower point in the second point cloud to be processed according to the actual position coordinate of the tower in the power transmission line, and extracting the tower point to obtain a third point cloud to be processed;
and removing the noise points of the third point cloud to be processed to obtain the vegetation point cloud.
Optionally, the step of grading the vegetation point cloud and generating the graded vegetation hidden danger point cloud of the vegetation point cloud based on the richard plant growth equation includes:
grading the vegetation point cloud according to the end point of the power line point cloud, searching a target point position in the graded vegetation point cloud, and screening out complete tree point cloud corresponding to the target point position in the vegetation point cloud based on the target point position;
and predicting the growth condition of the vegetation corresponding to the complete tree point cloud through a richard plant growth equation, and determining the vegetation hidden danger point cloud forming the tree barrier in the complete tree point cloud.
Optionally, the step of predicting the growth condition of vegetation corresponding to the complete tree point cloud by using a richard plant growth equation, and determining the vegetation hidden danger point cloud forming a tree barrier in the tree point cloud includes:
calculating parameters in a simultaneous physical research plant growth equation and a regression equation corresponding to the height elevation scatter diagram of the vegetation;
and calculating the future growth height of the vegetation according to the parameters, and adding the future growth height to the elevation of the complete tree point cloud to obtain the vegetation hidden danger point cloud forming the tree barrier.
Optionally, the step of constructing a power line vector corresponding to the power line point cloud and calculating a three-dimensional convex hull of the vegetation hidden danger point cloud includes:
grading the power line point clouds according to the pole and tower points, and striping the graded power line point clouds to obtain single power line point clouds;
constructing a power line vector corresponding to the single power line point cloud by utilizing straight line fitting and curve fitting;
taking the vegetation hidden danger point clouds among the tower points as block-shaped whole bodies, and segmenting the block-shaped whole bodies;
and calculating the three-dimensional convex hull of each section of the blocky whole body by using an incremental method.
Optionally, the physical distance comprises a horizontal distance, a vertical distance, and a clearance distance, the safety thresholds comprise a first safety threshold, a second safety threshold, and a third safety threshold,
if the physical distance between the tree convex hull point in the three-dimensional convex hull and the power line vector is smaller than a safety threshold, the step of determining the vegetation point as a vegetation hidden danger point comprises the following steps:
if the horizontal distance is smaller than the first safety threshold value, the vertical distance is smaller than the second safety threshold value, and the clearance distance is smaller than the third safety threshold value, the tree convex hull point is determined to be a vegetation hidden danger point.
In addition, in order to achieve the above object, the present invention further provides a plant growth model-based tree barrier hidden danger prediction apparatus, including:
the acquisition module is used for acquiring laser point cloud of the current power transmission line;
the extraction module is used for extracting a power line point cloud and a vegetation point cloud from the laser point cloud;
the grading generation module is used for grading the vegetation point cloud and generating the graded vegetation hidden danger point cloud of the vegetation point cloud based on the richard plant growth equation;
the construction module is used for constructing a power line vector corresponding to the power line point cloud;
the calculation module is used for calculating a three-dimensional convex hull of the vegetation hidden danger point cloud;
and the determining module is used for determining that the tree convex hull point is a vegetation hidden danger point if the physical distance between the tree convex hull point in the three-dimensional convex hull and the power line vector is smaller than a safety threshold.
In addition, in order to achieve the above object, the present invention further provides a plant growth model based tree obstacle hidden danger prediction apparatus, which includes a memory, a processor, and a plant growth model based tree obstacle hidden danger prediction program stored in the memory and executable on the processor, where the plant growth model based tree obstacle hidden danger prediction program, when executed by the processor, implements the steps of the plant growth model based tree obstacle hidden danger prediction method described above.
In addition, in order to achieve the above object, the present invention further provides a computer readable storage medium, where the plant growth model-based tree barrier potential hazard prediction program is stored on the computer readable storage medium, and when executed by a processor, the method for predicting the plant growth model-based tree barrier potential hazard is implemented.
Drawings
FIG. 1 is a diagram illustrating a hardware configuration of an apparatus for implementing various embodiments of the invention;
FIG. 2 is a schematic flow chart of a method for predicting hidden danger of tree obstacle based on a plant growth model according to a first embodiment of the present invention;
FIG. 3 is a schematic view of a distribution grid according to the present invention;
FIG. 4 is a schematic diagram of three-dimensional coordinates of a relationship between two points and a distance in the power line point cloud of the present invention.
The implementation, functional features and advantages of the present invention will be described with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides a plant growth model-based tree obstacle hidden danger prediction device, and referring to fig. 1, fig. 1 is a schematic structural diagram of a hardware operating environment related to the embodiment of the invention.
It should be noted that fig. 1 is a schematic structural diagram of a hardware operating environment of a plant growth model-based tree obstacle hazard prediction device. The plant growth model-based tree obstacle hidden danger prediction device in the embodiment of the invention can be a Personal Computer (PC), a portable Computer, a server and other devices.
As shown in fig. 1, the plant growth model-based tree-obstacle risk prediction apparatus may include: a processor 1001, such as a CPU, a memory 1005, a user interface 1003, a network interface 1004, a communication bus 1002. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Optionally, the plant growth model-based tree obstacle prediction device may further include an RF (Radio Frequency) circuit, a sensor, a WiFi module, and the like.
Those skilled in the art will appreciate that the plant growth model based tree barrier potential hazard prediction apparatus configuration shown in fig. 1 does not constitute a limitation of the plant growth model based tree barrier potential hazard prediction apparatus, and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a computer storage readable storage medium, may include an operating system, a network communication module, a user interface module, and a plant-growth model-based tree-obstacle prediction program. The operating system is a program for managing and controlling hardware and software resources of the plant growth model-based tree obstacle hidden danger prediction device, and supports the operation of the plant growth model-based tree obstacle hidden danger prediction program and other software or programs.
The plant growth model-based tree obstacle hidden danger prediction device shown in fig. 1 may be used to predict tree obstacle hidden danger of a power transmission line, and the user interface 1003 is mainly used to detect or output various information, such as input of a search instruction and output of tree obstacle hidden danger information; the network interface 1004 is mainly used for interacting with a background server and communicating; processor 1001 may be configured to invoke a plant growth model based tree-obstacle prediction program stored in memory 1005 and perform the following operations:
acquiring laser point clouds of a current power transmission line, and extracting power line point clouds and vegetation point clouds from the laser point clouds;
grading the vegetation point cloud, and generating the graded vegetation hidden danger point cloud of the vegetation point cloud based on a richard plant growth equation;
constructing a power line vector corresponding to the power line point cloud, and calculating a three-dimensional convex hull of the vegetation hidden danger point cloud;
and if the physical distance between the tree convex hull point in the three-dimensional convex hull and the power line vector is smaller than a safety threshold, determining that the tree convex hull point is a vegetation hidden danger point.
Further, the step of extracting a power line point cloud and a vegetation point cloud from the laser point cloud comprises:
filtering ground points in the laser point cloud by using a cloth filtering algorithm to obtain a first point cloud to be processed;
and extracting a power line point cloud and a vegetation point cloud from the first point cloud to be processed.
Further, the step of extracting the power line point cloud and the vegetation point cloud from the first point cloud to be processed comprises:
extracting power line point clouds in the first point cloud to be processed based on a filtering algorithm of the average value of the inclination angles among the point clouds, and obtaining a second point cloud to be processed;
searching a tower point in the second point cloud to be processed according to the actual position coordinate of the tower in the power transmission line, and extracting the tower point to obtain a third point cloud to be processed;
and removing the noise points of the third point cloud to be processed to obtain the vegetation point cloud.
Further, the step of grading the vegetation point cloud and generating the graded vegetation hidden danger point cloud of the vegetation point cloud based on the richard plant growth equation comprises:
grading the vegetation point cloud according to the end point of the power line point cloud, searching a target point position in the graded vegetation point cloud, and screening out complete tree point cloud corresponding to the target point position in the vegetation point cloud based on the target point position;
and predicting the growth condition of the vegetation corresponding to the complete tree point cloud through a richard plant growth equation, and determining the vegetation hidden danger point cloud forming the tree barrier in the complete tree point cloud.
Further, the step of predicting the vegetation growth condition corresponding to the complete tree point cloud through a richard plant growth equation and determining the vegetation hidden danger point cloud forming the tree barrier in the tree point cloud comprises the following steps:
calculating parameters in a simultaneous physical research plant growth equation and a regression equation corresponding to the height elevation scatter diagram of the vegetation;
and calculating the future growth height of the vegetation according to the parameters, and adding the future growth height to the elevation of the complete tree point cloud to obtain the vegetation hidden danger point cloud forming the tree barrier.
Further, the step of constructing a power line vector corresponding to the power line point cloud and calculating a three-dimensional convex hull of the vegetation hidden danger point cloud comprises:
grading the power line point clouds according to the pole and tower points, and striping the graded power line point clouds to obtain single power line point clouds;
constructing a power line vector corresponding to the single power line point cloud by utilizing straight line fitting and curve fitting;
taking the vegetation hidden danger point clouds among the tower points as block-shaped whole bodies, and segmenting the block-shaped whole bodies;
and calculating the three-dimensional convex hull of each section of the blocky whole body by using an incremental method.
Further, the physical distances include a horizontal distance, a vertical distance, and a clearance distance, the safety thresholds include a first safety threshold, a second safety threshold, and a third safety threshold,
if the physical distance between the tree convex hull point in the three-dimensional convex hull and the power line vector is smaller than a safety threshold, the step of determining the vegetation point as a vegetation hidden danger point comprises the following steps:
if the horizontal distance is smaller than the first safety threshold value, the vertical distance is smaller than the second safety threshold value, and the clearance distance is smaller than the third safety threshold value, the tree convex hull point is determined to be a vegetation hidden danger point.
According to the method, the laser point cloud of the current power transmission line is obtained, the power line point cloud and the vegetation point cloud are extracted from the laser point cloud, the vegetation point cloud is graded, the vegetation hidden danger point cloud of the graded vegetation point cloud is generated based on the richard plant growth equation, the power line vector of the power line point cloud is constructed, the three-dimensional convex hull of the vegetation hidden danger point cloud is calculated, when the physical distance between the tree convex hull point and the power line vector in the three-dimensional convex hull is smaller than the safety threshold value, the tree convex hull point is determined to be the vegetation hidden danger point, and the vegetation hidden danger point cloud is the point cloud corresponding to the current vegetation growing around the power transmission line, so that whether the future vegetation growing affects the normal operation of the power transmission line can be predicted.
The specific implementation manner of the mobile terminal of the invention is basically the same as that of each embodiment of the tree obstacle hidden danger prediction method based on the plant growth model, and is not described herein again.
Based on the structure, the invention provides various embodiments of the tree barrier hidden danger prediction method based on the plant growth model.
The invention provides a tree barrier hidden danger prediction method based on a plant growth model.
Referring to fig. 2, fig. 2 is a schematic flowchart of a method for predicting hidden danger of tree obstacle based on a plant growth model according to a first embodiment of the present invention.
In the present embodiment, an embodiment of a method for predicting hidden tree obstacle based on a plant growth model is provided, and it should be noted that, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in an order different from that here.
In this embodiment, the method for predicting hidden danger of tree obstacle based on a plant growth model includes:
step S10, acquiring laser point clouds of the current power transmission line, and extracting power line point clouds and vegetation point clouds from the laser point clouds;
along with the increasing electricity demand of national life and production, the transmission line has large scale, which makes important contribution to economic development, social progress and national construction, the factors which most easily cause the potential safety hazard of the transmission line are the growth of various trees in vegetation, and the prediction of tree obstacles can effectively avoid the safety of the transmission line from being influenced by the trees.
The laser point cloud can reflect the condition around transmission line and the transmission line, the laser point cloud of current transmission line obtains through unmanned aerial vehicle aircraft technique, the unmanned aerial vehicle aircraft is unmanned aerial vehicle multisensor power inspection system, can carry on multiple instruments such as visible light tilt camera, the visible light camera, ultraviolet scanner, thermal infrared imager and three-dimensional laser scanner, can acquire the high accuracy high density three-dimensional space information of near transmission line corridor topography ground thing, it is high to have efficiency and data quality of patrolling and examining, data acquisition is with low costs, it is little etc. very showing advantage to patrol and examine the risk. It can be understood that the laser point cloud carries power line information, vegetation information, ground information, tower information and the like of the shooting place, and the power line information, the vegetation information, the ground information and the tower information are represented by the power line point cloud, the vegetation point cloud, the ground point cloud and the tower point cloud in the laser point cloud.
The prediction of the power transmission line tree barrier is mainly embodied in that whether the growth of vegetation hinders the normal operation of a power line or not, so that power line point cloud and vegetation point cloud in the laser point cloud need to be extracted, and the prediction of the tree barrier hidden danger can be realized by processing the power line point cloud and the vegetation point cloud.
Step S20, grading the vegetation point cloud, and generating the vegetation hidden danger point cloud of the graded vegetation point cloud based on the Richards plant growth equation;
the vegetation point cloud is the point cloud corresponding to actual vegetation around the power transmission line, the vegetation hidden danger point cloud is generated by predicting the future growth condition of vegetation according to the vegetation point cloud, namely the vegetation hidden danger point cloud represents the point cloud corresponding to the future vegetation. And grading the vegetation point cloud according to the end point of each grade of the power line point cloud, and then obtaining the vegetation hidden danger point cloud of each grade of the vegetation point cloud based on the richard plant growth equation.
Step S30, constructing a power line vector corresponding to the power line point cloud, and calculating a three-dimensional convex hull of the vegetation hidden danger point cloud;
specifically, the constructing of the power line vector corresponding to the power line point cloud includes:
step a, grading the power line point cloud according to the pole and tower points, and dividing the graded power line point cloud to obtain a single power line point cloud;
b, constructing a power line vector corresponding to the single power line point cloud by utilizing straight line fitting and curve fitting;
the method comprises the steps of taking tower points as a first gear to perform gear grading on power line point clouds, taking projection of the power line point clouds between two adjacent tower points on a horizontal plane as a straight line, and performing stripe extraction on each gear of the power line point clouds according to the parallel characteristics, specifically, taking 2/5 of the shortest distance between the horizontal projection straight lines of the power line point clouds as a distance threshold value of straight line fitting, obtaining a projection straight line of the power line point clouds through a RANSAC straight line fitting method, reducing points on the straight line into three-dimensional coordinates, obtaining the power line point clouds in a strip shape, namely single power line point clouds, and performing circular fitting until all the single power line point clouds in the gear are extracted.
In this embodiment, a straight line model is combined with a parabolic model to perform three-dimensional reconstruction on a single power line point cloud to obtain a power line vector, and the specific reconstruction process is as follows: when the straight line is fitted, each single power line point cloud is projected on an XY plane, and the minimum value x of the x coordinate of the single power line point cloud is calculatedminAnd maximum value xmaxThen, calculating the optimal parameters k and b of the fitted straight line model by a least square method, wherein the equation formula of the fitted straight line is as follows: and y is kx + b. During curve fitting, a single power line point cloud is projected to a plane where a fitting straight line and a Z axis are located, and then the optimal parameter a of a fitting parabolic model is obtained through calculation by adopting a least square rule0,a1,a2The equation for fitting a parabola is given as z ═ a0x2+a1x+a2. Finally setting step length along X axis in interval [ Xmin，xmax]And generating corresponding y coordinates and z coordinates according to the fitting straight line and the fitting parabola to obtain three-dimensional vector nodes of each single power line point cloud and approximately express the power line vector.
Specifically, the vegetation convex hull of the vegetation point cloud is detected, and the method comprises the following steps:
c, taking the vegetation hidden danger point clouds among the tower points as block-shaped whole bodies, and segmenting the block-shaped whole bodies;
and d, calculating the three-dimensional convex hull of each section of the blocky whole body by using an incremental method.
Partitioning the vegetation hidden danger point cloud, specifically taking the vegetation hidden danger point cloud between pole and tower points as a block whole, segmenting each block whole to obtain segment-shaped vegetation hidden danger point cloud, and calculating a three-dimensional convex hull of each segment of vegetation hidden danger point cloud by using an incremental method.
As the first-grade power line point cloud and the massive vegetation hidden danger point cloud (which can be understood as the massive whole point cloud is the massive vegetation hidden danger point cloud) are arranged between the adjacent pole tower points, each grade of power line point cloud corresponds to one massive vegetation hidden danger point cloud, one massive vegetation hidden danger point cloud is divided into a plurality of sections to obtain a plurality of sections of vegetation hidden danger point clouds, and the three-dimensional convex hull of each section of vegetation hidden danger point cloud is calculated, so each grade of power line point cloud can correspond to a plurality of three-dimensional convex hulls.
Step S40, if the physical distance between the tree convex hull point in the three-dimensional convex hull and the power line vector is smaller than a safety threshold, determining that the tree convex hull point is a vegetation hidden danger point.
Further, the physical distances include a horizontal distance, a vertical distance and a clearance distance, the safety thresholds include a first safety threshold, a second safety threshold and a third safety threshold, and the step S40 includes:
and e, if the horizontal distance is smaller than the first safety threshold, the vertical distance is smaller than the second safety threshold, and the clearance distance is smaller than the third safety threshold, determining that the tree convex hull point is a vegetation hidden danger point.
The first safety threshold, the second safety threshold and the third safety threshold are set according to the power transmission line grade and the safety distance specified by the country. The tree convex hull point is a vegetation point on a section where the power line is located in the three-dimensional convex hull, the horizontal distance, the vertical distance and the clearance distance of the vegetation convex hull point and the power line vector are calculated, when the horizontal distance between the vegetation convex hull point and the power line vector is smaller than a first safety threshold value, the vertical distance is smaller than a second safety distance, the clearance distance is smaller than a third safety distance, the vegetation point is determined to be a vegetation hidden danger point, and the vegetation point is marked.
In the embodiment, the laser point cloud of the current power transmission line is obtained, the power line point cloud and the vegetation point cloud are extracted from the laser point cloud, the vegetation point cloud is graded, the vegetation hidden danger point cloud of the graded vegetation point cloud is generated based on the richard plant growth equation, the power line vector of the power line point cloud is constructed, the three-dimensional convex hull of the vegetation hidden danger point cloud is calculated, when the physical distance between the tree convex hull point in the three-dimensional convex hull and the power line vector is smaller than the safety threshold value, the tree convex hull point is determined to be the vegetation hidden danger point, and the vegetation hidden danger point cloud is the point cloud corresponding to the current vegetation growing around the power transmission line, so that whether the future vegetation growing affects the normal operation of the power.
Further, a second embodiment of the method for predicting the hidden danger of the tree barrier based on the plant growth model is provided. The difference between the second embodiment of the plant growth model-based tree barrier hidden danger prediction method and the first embodiment of the plant growth model-based tree barrier hidden danger prediction method is that the step of extracting the power line point cloud and the vegetation point cloud from the laser point cloud comprises the following steps:
f, filtering ground points in the laser point cloud by using a cloth filtering algorithm to obtain a first point cloud to be processed;
and g, extracting power line point clouds and vegetation point clouds from the first point cloud to be processed.
The ground point cloud reflects ground information and is irrelevant to prediction of tree barrier hidden dangers, so that the ground point cloud in the laser point cloud is filtered out firstly in order to reduce the number of processing laser point cloud midpoints.
And filtering ground points in the laser point cloud of the power transmission line according to the existing distribution filtering algorithm, namely filtering the ground point cloud in the laser point cloud to obtain a first point cloud to be processed, and extracting the power line point cloud and the vegetation electric cloud from the first point cloud to be processed. The cloth filtering algorithm is a computer graphic algorithm for simulating cloth, the algorithm firstly inverts the data of the laser point cloud of the power transmission line, then assumes a piece of virtual rigid cloth to cover the surface of the inverted laser point cloud data under the action of self gravity, determines the positions of cloth nodes to generate an approximate earth surface shape, and finally extracts the ground points from the laser point cloud by comparing the distance between the non-inverted laser point cloud and the cloth curved surface. The cloth graticule mesh comprises a large amount of nodes of interconnection, and when the cloth graticule mesh was fine enough, the node of cloth can approximate expression transmission line's digital terrain model. The cloth filtering algorithm can be suitable for terrain simulation in various scenes such as flat areas, hills and mountains, and adjustable parameters in the algorithm are as follows: cloth graticule mesh resolution ratio, iteration number of times and distance threshold value, wherein cloth graticule mesh resolution ratio generally sets up the interval into 3 ~ 5 times ground point, avoids the cloth graticule mesh too thick to lead to the ground point to filter incompletely, and fig. 3 is cloth graticule mesh sketch map. The number of iterations is used to terminate the algorithm iteration process and is typically set to 500 by default. And finally, setting a distance threshold between the inverted point cloud data and the distribution grid according to the resolution of the distribution grid and the actual terrain of the power transmission line, judging a point with the distance of the distribution grid smaller than the distance threshold as a ground point, and filtering the ground point to remove the ground point cloud, wherein the point cloud which is not removed is the first point cloud to be processed.
Further, step g further comprises:
step g1, extracting power line point clouds in the first point cloud to be processed based on a filtering algorithm of the average value of the inclination angles among the point clouds, and obtaining a second point cloud to be processed;
step g2, searching tower points in the second point cloud to be processed according to the actual position coordinates of towers in the power transmission line, and extracting the tower points to obtain a third point cloud to be processed;
and g3, removing the noise points of the third point cloud to be processed to obtain the vegetation point cloud.
The elevation change of the power line point in a small range is characterized by being smaller than the height of the vegetation point and the height of the tower point, so that the non-power points (the vegetation point and the height of the tower point) in the first point cloud to be processed can be filtered by adopting a filtering algorithm based on the average value of the inclination angles between the point clouds, and the algorithm principle is as follows: traversing the first point cloud to be processed, regarding each point in the first point cloud to be processed as a search point, giving a search radius r to each search point for KdTree search, calculating the average value of the inclination angles from other points to the search point in the search radius area, regarding the search point with the inclination angle average value smaller than a threshold beta as a power line point, and regarding the search point as a non-power line point if the search point is not the power line pointAnd extracting power line points to obtain power line point clouds. The relationship between the angle of inclination and the distance between the two points is shown in FIG. 4, point Pi(xi,yi,zi) And point Pj(xj,yj,zj) Inclination angle thetai,jThe calculation formula of (2) is as follows:
According to the known actual position coordinates of the tower in the power transmission line, the tower point is extracted from the second point cloud to be processed through a Kdtree-based range search method to obtain a third point cloud to be processed, and the tower point in the third point cloud to be processed can be understood to be clearly marked. And searching a tower point from the second point cloud to be processed by taking the known actual position coordinate of the tower as a search center, wherein the search radius is the maximum radius of the tower in the power transmission line.
And removing the noise points in the third to-be-processed electric cloud, wherein the removing method comprises the steps of inputting a denoising instruction by a researcher, obtaining the positions of the noise points in the denoising instruction, and removing the points corresponding to the positions of the noise points in the third to-be-processed point cloud to obtain the vegetation point cloud.
The power line point cloud, the tower point cloud and the vegetation point cloud are extracted step by step from the laser electric cloud with the ground points filtered, namely the laser point cloud is classified, the laser point cloud is divided into the ground point cloud, the power line point cloud, the tower point cloud, the vegetation point cloud and the noise point, the ground point cloud and the noise point are filtered, the power line point cloud, the tower point cloud and the vegetation point cloud are respectively marked, the calculation amount is reduced, and the efficiency of follow-up tree obstacle hidden danger prediction is improved.
The invention relates to a tree barrier hidden danger prediction method based on a plant growth model. The third embodiment of the method for predicting hidden danger due to tree barrier based on a plant growth model is different from the first and second embodiments of the method for predicting hidden danger due to tree barrier based on a plant growth model in that the step S20 includes:
step h, grading the vegetation point cloud according to the end point of the power line point cloud, searching a target point position in the graded vegetation point cloud, and screening out complete tree point cloud corresponding to the target point position in the vegetation point cloud based on the target point position;
and i, predicting the growth condition of vegetation corresponding to the complete tree point cloud through a richard plant growth equation, and determining the vegetation hidden danger point cloud forming the tree barrier in the complete tree point cloud.
Classifying vegetation point clouds according to the end points of each power line point cloud, searching in the classified vegetation point clouds by taking the safety distance corresponding to the state-specified power transmission line level plus 5-10 meters as a search radius based on power line vectors, regarding points in a search range as target points, and determining the positions of the target points.
In order to scientifically and reasonably predict the potential danger condition of the tree obstacle of the power transmission line within a certain time, improve the detection efficiency of the tree obstacle and prevent the tree obstacle from happening in the future, a mathematical model for predicting the growth of the tree needs to be established, and then the trees which may form the tree obstacle after a certain period of time are screened out, and the prediction information of the potential danger of the tree obstacle is given out. The method adopts a richard plant growth equation as a mathematical model to predict the growth condition of the trees, determines the vegetation hidden danger point cloud which forms a tree barrier in the complete tree electric cloud in a certain time in the future,
further, step i further comprises:
step i1, a regression equation corresponding to the simultaneous physical research plant growth equation and the height elevation scatter diagram of the vegetation is established, and parameters in the physical research plant growth equation are calculated;
and i2, calculating the future growth height of the vegetation according to the parameters, and adding the future growth height to the elevation of the complete tree point cloud to obtain the vegetation hidden danger point cloud forming the tree barrier.
The growth equation of the richard plant is that y is a (1-e)-kt)cWherein t is the growth age of the tree; y is the predicted tree height growth amount; parameters a and c are growth investigation factors; k is a plant growth rule revision parameter, and the value of k is between 0 and 1. The parameters a, c and k are obtained as follows:
1) and measuring the vegetation (such as eucalyptus, pine, bamboo and the like) corresponding to the complete tree point cloud in a plurality of groups according to the tree ages.
2) And drawing a height elevation scatter diagram by using the age of the tree and the observed height of the tree, and drawing a trend line according to the height elevation scatter diagram.
3) Obtaining a regression equation through the trend line, and obtaining parameter values in the growth equation of the richard plant by combining the regression equation and the growth equation of the richard plant.
And predicting the growth height of various vegetations in a certain time, namely the future growth height, according to the calculated parameter values, adding the future growth height to the elevation of the complete tree point cloud, and generating the potential vegetation hidden danger point cloud forming the tree barrier in the complete tree point cloud.
Because the vegetation corresponding to the complete tree point cloud is close to the power transmission line, the possibility of forming the tree barrier exists, the calculation amount is greatly reduced compared with the method for obtaining the vegetation hidden danger point cloud from the complete tree point cloud, the tree growth prediction model is established through the richardson plant growth equation, the vegetation hidden danger point cloud forming the tree barrier in a certain time in the future is obtained, and the accuracy of tree barrier prediction is improved.
In addition, the embodiment of the present invention further provides a plant growth model-based tree barrier hidden danger prediction apparatus, where the plant growth model-based tree barrier hidden danger prediction apparatus includes:
the acquisition module is used for acquiring laser point cloud of the current power transmission line;
the extraction module is used for extracting a power line point cloud and a vegetation point cloud from the laser point cloud;
the grading generation module is used for grading the vegetation point cloud and generating the graded vegetation hidden danger point cloud of the vegetation point cloud based on the richard plant growth equation;
the construction module is used for constructing a power line vector corresponding to the power line point cloud;
the calculation module is used for calculating a three-dimensional convex hull of the vegetation hidden danger point cloud;
and the determining module is used for determining that the tree convex hull point is a vegetation hidden danger point if the physical distance between the tree convex hull point in the three-dimensional convex hull and the power line vector is smaller than a safety threshold.
The implementation of the plant growth model-based tree barrier hidden danger prediction device is basically the same as that of each embodiment of the plant growth model-based tree barrier hidden danger prediction, and details are not repeated here.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, where a plant growth model-based tree obstacle hidden danger prediction program is stored on the computer-readable storage medium, and when executed by a processor, the plant growth model-based tree obstacle hidden danger prediction program implements the steps of the plant growth model-based tree obstacle hidden danger prediction method described above.
It should be noted that the computer readable storage medium may be disposed in a plant growth model-based tree obstacle hazard prediction apparatus.
The specific implementation manner of the computer-readable storage medium of the present invention is substantially the same as that of each embodiment of the plant growth model-based tree obstacle hidden danger prediction method, and is not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A plant growth model-based tree barrier hidden danger prediction method is characterized by comprising the following steps:
acquiring laser point clouds of a current power transmission line, and extracting power line point clouds and vegetation point clouds from the laser point clouds;
grading the vegetation point cloud, and generating the graded vegetation hidden danger point cloud of the vegetation point cloud based on a richard plant growth equation;
constructing a power line vector corresponding to the power line point cloud, and calculating a three-dimensional convex hull of the vegetation hidden danger point cloud;
and if the physical distance between the tree convex hull point in the three-dimensional convex hull and the power line vector is smaller than a safety threshold, determining that the tree convex hull point is a vegetation hidden danger point.
2. The method for predicting hidden danger of tree barrier based on plant growth model according to claim 1, wherein the step of extracting power line point cloud and vegetation point cloud from the laser point cloud comprises:
filtering ground points in the laser point cloud by using a cloth filtering algorithm to obtain a first point cloud to be processed;
and extracting a power line point cloud and a vegetation point cloud from the first point cloud to be processed.
3. The method for predicting hidden danger of tree barrier based on plant growth model according to claim 2, wherein the step of extracting the power line point cloud and the vegetation point cloud from the first point cloud to be processed comprises:
extracting power line point clouds in the first point cloud to be processed based on a filtering algorithm of the average value of the inclination angles among the point clouds, and obtaining a second point cloud to be processed;
searching a tower point in the second point cloud to be processed according to the actual position coordinate of the tower in the power transmission line, and extracting the tower point to obtain a third point cloud to be processed;
and removing the noise points of the third point cloud to be processed to obtain the vegetation point cloud.
4. The method of claim 1, wherein the step of grading the vegetation point cloud and generating the graded vegetation risk point cloud of the vegetation point cloud based on a richard plant growth equation comprises:
grading the vegetation point cloud according to the end point of the power line point cloud, searching a target point position in the graded vegetation point cloud, and screening out complete tree point cloud corresponding to the target point position in the vegetation point cloud based on the target point position;
and predicting the growth condition of the vegetation corresponding to the complete tree point cloud through a richard plant growth equation, and determining the vegetation hidden danger point cloud forming the tree barrier in the complete tree point cloud.
5. The method for predicting hidden danger of tree barrier based on plant growth model according to claim 4, wherein the step of predicting the growth of vegetation corresponding to the complete tree point cloud through the richard plant growth equation and determining the hidden danger point cloud of vegetation forming the tree barrier in the tree point cloud comprises:
calculating parameters in a simultaneous physical research plant growth equation and a regression equation corresponding to the height elevation scatter diagram of the vegetation;
and calculating the future growth height of the vegetation according to the parameters, and adding the future growth height to the elevation of the complete tree point cloud to obtain the vegetation hidden danger point cloud forming the tree barrier.
6. The method for predicting hidden danger of tree barrier based on plant growth model as claimed in claim 3, wherein the step of constructing power line vectors corresponding to the power line point clouds and calculating three-dimensional convex hulls of the hidden danger of vegetation point clouds comprises:
grading the power line point clouds according to the pole and tower points, and striping the graded power line point clouds to obtain single power line point clouds;
constructing a power line vector corresponding to the single power line point cloud by utilizing straight line fitting and curve fitting;
taking the vegetation hidden danger point clouds among the tower points as block-shaped whole bodies, and segmenting the block-shaped whole bodies;
and calculating the three-dimensional convex hull of each section of the blocky whole body by using an incremental method.
7. The plant growth model-based tree obstacle prediction method of any one of claims 1 to 6, wherein the physical distances comprise a horizontal distance, a vertical distance and a clearance distance, the safety thresholds comprise a first safety threshold, a second safety threshold and a third safety threshold,
if the physical distance between the tree convex hull point in the three-dimensional convex hull and the power line vector is smaller than a safety threshold, the step of determining the vegetation point as a vegetation hidden danger point comprises the following steps:
if the horizontal distance is smaller than the first safety threshold value, the vertical distance is smaller than the second safety threshold value, and the clearance distance is smaller than the third safety threshold value, the tree convex hull point is determined to be a vegetation hidden danger point.
8. The plant growth model-based tree barrier hidden danger prediction device is characterized by comprising the following components:
the acquisition module is used for acquiring laser point cloud of the current power transmission line;
the extraction module is used for extracting a power line point cloud and a vegetation point cloud from the laser point cloud;
the grading generation module is used for grading the vegetation point cloud and generating the graded vegetation hidden danger point cloud of the vegetation point cloud based on the richard plant growth equation;
the construction module is used for constructing a power line vector corresponding to the power line point cloud;
the calculation module is used for calculating a three-dimensional convex hull of the vegetation hidden danger point cloud;
and the determining module is used for determining that the tree convex hull point is a vegetation hidden danger point if the physical distance between the tree convex hull point in the three-dimensional convex hull and the power line vector is smaller than a safety threshold.
9. A plant growth model based tree barrier potential hazard prediction device, comprising a memory, a processor and a plant growth model based tree barrier potential hazard prediction program stored in the memory and executable on the processor, wherein the plant growth model based tree barrier potential hazard prediction program, when executed by the processor, implements the steps of the plant growth model based tree barrier potential hazard prediction as recited in any one of claims 1 to 7.
10. A readable storage medium, wherein the readable storage medium is a computer readable storage medium, and the readable storage medium has a plant growth model-based tree barrier potential hazard prediction program stored thereon, and when the plant growth model-based tree barrier potential hazard prediction program is executed by a processor, the steps of the plant growth model-based tree barrier potential hazard prediction method according to any one of claims 1 to 7 are implemented.
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