CN115755088A - Laser point cloud-based automatic measurement method for power transmission line engineering construction parameters - Google Patents
Laser point cloud-based automatic measurement method for power transmission line engineering construction parameters Download PDFInfo
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Abstract
The invention discloses a laser point cloud-based automatic measurement method for power transmission line engineering construction parameters, which comprises the following steps of: s1, carrying a laser radar by using an unmanned aerial vehicle to obtain point cloud data of a power transmission line tower; s2, point cloud data are selected, and the inclination angle of the power transmission line tower is evaluated; s3, planning a route survey area and a route according to the path of the power transmission line, obtaining a holographic image in a channel corridor of the power transmission line by using an unmanned aerial vehicle, and calculating the height space of a tower and a hanging line; s4, calibrating coordinates of the power transmission line tower by acquiring parameters of the power transmission line tower; and S5, identifying the defects of the power transmission line tower by acquiring the real-time video of the unmanned aerial vehicle. According to the invention, the unmanned aerial vehicle carries a laser radar to obtain the point cloud data of the power transmission line tower, and the inclination angle of the tower, the height space of a hanging line point, the coordinates of the tower and other key parameters can be conveniently calculated, so that whether the requirements of design and standard acceptance are met or not can be conveniently judged.
Description
Technical Field
The invention belongs to the technical field of power transmission line engineering, and particularly relates to a laser point cloud-based automatic measurement method for power transmission line engineering construction parameters.
Background
Our country mainly examines the transmission line manually, for example, it depends on the ground transportation means or hand-held instrument to detect, also detects the deformation of the pole tower through satellite remote sensing, at present, many exploratory researches are being done in this respect.
The existing transmission line engineering construction parameter measuring method has some problems: due to the lack of efficient monitoring means, the risk state of the power transmission line pole and tower inclined geological disaster is difficult to grasp in time, the risk and pressure of operation and maintenance work are large, in addition, the defect collection type of acceptance is different from the defect of production operation and maintenance, besides the qualitative defect of an acceptance scene, quantitative analysis needs to be carried out on the defect, and therefore the automatic measurement method for the power transmission line engineering construction parameters based on the laser point cloud is provided.
Disclosure of Invention
The invention aims to provide a laser point cloud-based automatic measurement method for power transmission line engineering construction parameters, which aims to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a power transmission line engineering construction parameter automatic measurement method based on laser point cloud comprises the following steps:
s1, carrying a laser radar by using an unmanned aerial vehicle to obtain point cloud data of a power transmission line tower;
s2, point cloud data are selected, and the inclination angle of the power transmission line tower is evaluated;
s3, planning a route survey area and a route according to the path of the power transmission line, obtaining a holographic image in a channel corridor of the power transmission line by using an unmanned aerial vehicle, and calculating the height space of a tower and a hanging line;
s4, calibrating coordinates of the power transmission line tower by acquiring parameters of the power transmission line tower;
and S5, identifying the defects of the power transmission line tower by acquiring the real-time video of the unmanned aerial vehicle, and giving an alarm if the defects exist.
Preferably, the step S1 further includes preprocessing the point cloud data after acquiring the point cloud data of the power transmission line tower, and the preprocessing method includes: and carrying out abnormal value elimination preprocessing operation on the original point cloud data, and then separating out ground points and non-ground points through filtering and sorting processing.
Preferably, the specific step of S2 includes:
s201, selecting tower point cloud in the laser point cloud of the power transmission line tower, and removing miscellaneous points and electric wire point cloud in the tower point cloud to obtain filtered tower point cloud;
s202, dividing the filtered tower point cloud into a tower foot point cloud, a tower body point cloud and a tower head point cloud to obtain a tower foot three-dimensional vector model, a tower body three-dimensional vector model and a tower head three-dimensional vector model;
s203, combining a tower foot three-dimensional vector model, a tower body three-dimensional vector model and a tower head three-dimensional vector model into a typical tower type three-dimensional point cloud model;
s204, according to the typical tower type three-dimensional point cloud model, performing center point calculation on a characteristic plane of the power transmission line tower to be evaluated to obtain center point coordinates of each characteristic plane of the tower to be evaluated;
and S205, calculating the inclination angle of the tower to be evaluated according to the coordinates of the central point of each characteristic plane of the tower to be evaluated.
Preferably, the center point calculation in S204 includes the following steps:
s2041, layering of power transmission line towers: horizontally slicing the three-dimensional tower point cloud, enabling the sliced point cloud to be parallel to the horizontal ground, projecting the sliced point cloud on the horizontal ground, and extracting the sliced boundary point cloud;
s2042, boundary segmentation: detecting boundary point clouds by adopting Hough transformation, converting lines of an image space into a parameter space, detecting extreme points of the parameter space, and extracting boundary lines;
s2043, boundary straight line fitting: the boundary point cloud extracted through the Hongh transformation has an abnormal value, and a straight line is fitted by adopting an improved least square method to obtain an accurate boundary; the calculation process is as follows: let the equation of a straight line be
y＝ax+b
Wherein a and b are linear parameters;
the standard deviation of the distances of the points to the fitted line is as follows:
wherein d is i Represents the distance of the ith point to the fitted line,
represents the average distance of all points to the fitted straight line, n is the total number of all points;
the average distance of all points to the fitted line is:
when d is i When the value is more than 2 sigma, the point is an abnormal point, and the point is removed; otherwise, reservingThe point is reached; then, calculating the parameters a and b again; the above steps are circularly operated when d i Stopping operation when the standard deviation is less than 2 times;
s2044, central point calculation: the intersection point of the two straight lines is set as:
then the projection coordinates of the corner points of the patch on the XOY plane are
The intersection point calculation is carried out on all the four straight lines to obtain four intersection points of the four edges of the plane, namely four boundary corner points which are respectively (x) 1 ,y 1 )，(x 2 ,y 2 )，(x 3 ,y 3 )，(x 4 ,y 4 ) Wherein (x) 1 ,y 1 ) Is the upper left corner point coordinate of the plane, (x) 2 ,y 2 ) Upper right corner point coordinate of bit plane, (x) 3 ,y 3 ) Is the coordinate of the lower left corner point of the plane, (x) 4 ,y 4 ) Coordinates of a lower right corner point of the bit plane;
the center point of the plane is then:
the center point of the plane is finally obtained through the calculation of the formula.
Preferably, the specific steps in S3 include:
s301, planning a route survey area and a route according to a route of the power transmission line, then acquiring an image of a channel corridor of the power transmission line by using an unmanned aerial vehicle, and measuring a three-dimensional coordinate of an image control point according to the position of the image control point which is arranged in advance;
s302, extracting characteristic points of collected image data of a power transmission line channel, matching images with overlapping degrees, determining surface elevation information by combining three-dimensional coordinates of image control points, and performing secondary processing on the images to obtain a holographic image in a power transmission line channel corridor;
and S303, spreading and drawing the power transmission line path on the holographic image map, determining the spatial trend of the power transmission line path, determining the position of the tower along the trend, calling the image pair in the area range, calculating the real-time horizontal epipolar line of the image pair, and calculating the height of the tower and the height space of the hanging line point by using computer vision based on the real-time horizontal epipolar line.
Preferably, the specific steps in S4 include:
s401, obtaining parameters of a power transmission line tower: the method comprises the following steps of measuring longitude of a tower, measuring latitude of the tower, operating span of the tower and corner conditions of the tower;
s402, calculating a measuring span according to the tower measuring longitude and the tower measuring latitude, comparing the measuring span with an operating span, and determining the tower needing to be calibrated;
and S403, converting the coordinates of the power transmission line tower into a format which can be identified by geographic information software, and performing coordinate calibration on the tower which is determined to need calibration.
Preferably, the step S4 further includes measuring the ground resistance of the power transmission line tower by obtaining parameters of the power transmission line tower, and the specific measuring method includes:
acquiring target information of a transmission tower to be measured, wherein the target information is a target measurement frequency point or a measurement parameter of the transmission tower to be measured;
measuring the measured impedance data of the transmission tower to be measured by a loop impedance method;
step three, obtaining an error prediction model of the previously trained target information;
acquiring the bias error of the target information according to the error prediction model;
and fifthly, calibrating the measured impedance data into corrected impedance data according to the deviation error.
Preferably, the specific step of S5 includes:
s501, collecting defect samples and non-defect sample images of towers and nodes of the power transmission line to form an intelligent defect detection sample data set of the power transmission line;
s502, manually marking the defect position and the defect type of the power transmission line tower on the intelligent defect detection sample data set of the power transmission line, and storing the marking result of each image into an xml format; recording the coordinates of the upper left corner of the outsourcing rectangle, the length and the width of the rectangle and the defect type by the marking result;
s503, training a tower defect detection model by adopting a tow-stage deep convolution neural network target detection algorithm;
s504, deploying the tower sinking detection model to a rear-end server;
and S505, collecting a real-time video of the unmanned aerial vehicle, extracting a key frame image from the real-time video and sending the key frame image to the back-end server through a network, calling the tower sinking detection model by the back-end server, detecting the defects and defect types existing in the power transmission line tower, and sending out early warning information.
Preferably, the S501 further includes an ROI on the given input image, and a frame of the ROI on the given input image is represented in the following distribution manner:
p(x，y，w，h|I)＝p(x，y|I)p(w，h|x，y，I)
wherein x and y respectively represent the window center coordinates of the ROI area, w represents the window width of the ROI area, h represents the window height of the ROI area, I represents the original image, and p is the probability.
Preferably, the S5 further includes a DenseBlock module, which is defined as follows:
x l ＝H l ([x 0 ，x 1 ，…，x l-1 ])
wherein H 1 Contains Batch Normalization->ReLU->Conv (3 x 3) operation, adopt 4 the DenseBlock module constitutes 121 layers of discernment networks, through Batch Normalization and 1 x 1's convolution and mean value pooling connection between the DenseBlock module, connect the complete connectivity layer and softmax categorised classification finallyThe device, class design 1000, is defined as follows:
compared with the prior art, the invention has the beneficial effects that:
(1) According to the invention, the unmanned aerial vehicle carries the laser radar to obtain the point cloud data of the power transmission line tower, and the inclination angle of the tower, the height space of the hanging line point, the coordinates of the tower and other key parameters can be conveniently calculated, so that whether the requirements of design and standard acceptance are met or not can be conveniently judged.
(2) According to the invention, the unmanned aerial vehicle inspection video image is transmitted to the rear-end server through the unmanned aerial vehicle inspection, so that the state monitoring of the damage identification and diagnosis of the power transmission line tower and the node is realized, and the damage defects of the power transmission line tower and the node are identified and early-warned, thereby stably detecting the safety condition of the power facility and ensuring the safe operation of the line.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of S2 of the present invention;
FIG. 3 is a flow chart of S3 of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-3, the present invention provides a technical solution: a power transmission line engineering construction parameter automatic measurement method based on laser point cloud comprises the following steps:
s1, carrying a laser radar by using an unmanned aerial vehicle to obtain point cloud data of a power transmission line tower;
s2, point cloud data are selected, and the inclination angle of the power transmission line tower is evaluated;
s3, planning a route survey area and a route according to the path of the power transmission line, obtaining a holographic image in a channel corridor of the power transmission line by using an unmanned aerial vehicle, and calculating the height space of a tower and a hanging line;
s4, calibrating coordinates of the power transmission line tower by acquiring parameters of the power transmission line tower;
and S5, identifying the defects of the power transmission line tower by acquiring the real-time video of the unmanned aerial vehicle, and giving an alarm if the defects exist.
In this embodiment, preferably, after the point cloud data of the power transmission line tower is obtained, the step S1 further includes preprocessing the point cloud data, where the preprocessing method includes: and carrying out abnormal value elimination preprocessing operation on the original point cloud data, and then separating out ground points and non-ground points through filtering and sorting processing.
In this embodiment, preferably, the specific step of S2 includes:
s201, selecting tower point cloud in the laser point cloud of the power transmission line tower, and removing miscellaneous points and electric wire point cloud in the tower point cloud to obtain filtered tower point cloud;
s202, dividing the filtered tower point cloud into a tower foot point cloud, a tower body point cloud and a tower head point cloud to obtain a tower foot three-dimensional vector model, a tower body three-dimensional vector model and a tower head three-dimensional vector model;
s203, combining a tower foot three-dimensional vector model, a tower body three-dimensional vector model and a tower head three-dimensional vector model into a typical tower type three-dimensional point cloud model;
s204, according to the typical tower type three-dimensional point cloud model, performing center point calculation on a characteristic plane of the power transmission line tower to be evaluated to obtain center point coordinates of each characteristic plane of the tower to be evaluated;
s205, calculating the inclination angle of the tower to be evaluated according to the coordinates of the central point of each characteristic plane of the tower to be evaluated.
In this embodiment, preferably, the calculating of the central point in S204 includes the following steps:
s2041, layering of power transmission line towers: horizontally slicing the three-dimensional tower point cloud, enabling the sliced point cloud to be parallel to the horizontal ground, projecting the sliced point cloud on the horizontal ground, and extracting the sliced boundary point cloud;
s2042, boundary segmentation: detecting boundary point clouds by adopting Hough transformation, converting lines of an image space into a parameter space, detecting extreme points of the parameter space, and extracting boundary lines;
s2043, boundary straight line fitting: the boundary point cloud extracted through Hongh transformation has an abnormal value, and a straight line is fitted by adopting an improved least square method to obtain an accurate boundary; the calculation process is as follows: let the equation of a straight line be
y＝ax+b
Wherein a and b are linear parameters;
the standard deviation of the distances of the points to the fitted line is as follows:
wherein d is i Represents the distance of the ith point to the fitted line,
represents the average distance of all points to the fitted straight line, n is the total number of all points;
the average distance of all points to the fitted line is:
when d is i When the value is more than 2 sigma, the point is an abnormal point, and the point is removed; otherwise, the point is reserved; then, calculating the parameters a and b again; performing cyclic operation on the steps when d i Stopping operation when the standard deviation is less than 2 times;
s2044, central point calculation: the intersection point of the two straight lines is set as:
then the projection coordinate of the corner point of the patch on the XOY plane is
The intersection point calculation is carried out on all the four straight lines to obtain four intersection points of the four edges of the plane, namely four boundary corner points which are respectively (x) 1 ,y 1 )，(x 2 ,y 2 )，(x 3 ,y 3 )，(x 4 ,y 4 ) Wherein (x) 1 ,y 1 ) Is the upper left corner point coordinate of the plane, (x) 2 ,y 2 ) Upper right corner point coordinate of bit plane, (x) 3 ,y 3 ) Is the coordinate of the lower left corner of the plane, (x) 4 ,y 4 ) Coordinates of a lower right corner point of the bit plane;
the center point of the plane is then:
the center point of the plane is finally obtained through the calculation of the formula.
In this embodiment, preferably, the specific step in S3 includes:
s301, planning a route survey area and a route according to a route of the power transmission line, acquiring an image of a channel corridor of the power transmission line by using an unmanned aerial vehicle, and measuring three-dimensional coordinates of image control points according to positions of the image control points which are arranged in advance;
s302, extracting characteristic points of collected image data of a power transmission line channel, matching images with overlapping degrees, determining surface elevation information by combining three-dimensional coordinates of image control points, and performing secondary processing on the images to obtain a holographic image in a power transmission line channel corridor;
and S303, spreading the power transmission line path on a holographic image, determining the spatial direction of the power transmission line path, determining the position of the tower along the direction, calling the image pair in the area range, calculating the real-time horizontal epipolar line of the image pair, and calculating the height of the tower and the height space of the hanging line point by using computer vision based on the real-time horizontal epipolar line.
In this embodiment, preferably, the specific step in S4 includes:
s401, obtaining parameters of a power transmission line tower: the method comprises the following steps of measuring longitude of a tower, measuring latitude of the tower, operating span of the tower and corner conditions of the tower;
s402, calculating a measuring span according to the tower measuring longitude and the tower measuring latitude, comparing the measuring span with an operating span, and determining the tower needing to be calibrated;
and S403, converting the coordinates of the power transmission line tower into a format which can be identified by geographic information software, and performing coordinate calibration on the tower which is determined to need calibration.
In this embodiment, preferably, the step S4 further includes measuring the ground resistance of the power transmission line tower by obtaining parameters of the power transmission line tower, and the specific measurement method includes:
acquiring target information of a transmission tower to be measured, wherein the target information is a target measurement frequency point or a measurement parameter of the transmission tower to be measured;
measuring the measured impedance data of the transmission tower to be measured by a loop impedance method;
step three, obtaining an error prediction model of the previously trained target information;
acquiring the bias error of the target information according to the error prediction model;
and fifthly, calibrating the measured impedance data into corrected impedance data according to the deviation error.
In this embodiment, preferably, the specific step of S5 includes:
s501, collecting defect samples and non-defect sample images of poles and towers and nodes of the power transmission line to form an intelligent defect detection sample data set of the power transmission line;
s502, manually marking the defect position and the defect type of the power transmission line tower on the intelligent defect detection sample data set of the power transmission line, and storing the marking result of each image into an xml format; recording the coordinates of the upper left corner of the outsourcing rectangle, the length and the width of the rectangle and the defect type by the marking result;
s503, training a tower defect detection model by adopting a tow-stage deep convolutional neural network target detection algorithm;
s504, deploying the tower sinking detection model to a rear-end server;
and S505, collecting a real-time video of the unmanned aerial vehicle, extracting a key frame image from the real-time video and sending the key frame image to the back-end server through a network, calling the tower sinking detection model by the back-end server, detecting the defects and defect types existing in the power transmission line tower, and sending out early warning information.
In this embodiment, preferably, the S501 further includes an ROI on the given input image, and a frame of the ROI on the given input image is represented in a distribution manner as follows:
p(x，y，W，h|I)＝p(x，y|I)p(w，h|x，y，I)
wherein x and y respectively represent the window center coordinates of the ROI area, w represents the window width of the ROI area, h represents the window height of the ROI area, I represents the original image, and p is the probability.
In this embodiment, preferably, the S5 further includes a DenseBlock module, where the DenseBlock module is defined as follows:
x l ＝H l ([x 0 ，x 1 ，…，x l-1 ])
wherein H 1 Contains Batch Normalization->ReLU->Conv (3 x 3) operation, adopt 4 the DenseBlock module to constitute 121 layers of discernment networks, through Batch Normalization and 1 x 1's convolution and mean value pooling connection between the DenseBlock module, connect full connectivity layer and softmax classifier finally, the classification design is 1000, defines as follows:
the principle and the advantages of the invention are as follows: according to the invention, the unmanned aerial vehicle carries a laser radar to obtain the point cloud data of the power transmission line tower, and the inclination angle of the tower, the height space of a hanging line point, the coordinates of the tower and other key parameters can be conveniently calculated, so that whether the design and standard acceptance requirements are met or not can be conveniently judged; patrol and examine through unmanned aerial vehicle, patrol and examine video image with unmanned aerial vehicle and transmit rear end server, realize transmission line shaft tower and node damage discernment diagnostic state monitoring, discern the early warning to transmission line shaft tower and node damage defect to the safety aspect of stable detection electric power facility ensures the safe operation of circuit.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A power transmission line engineering construction parameter automatic measurement method based on laser point cloud is characterized in that: the method comprises the following steps:
s1, carrying a laser radar by using an unmanned aerial vehicle to obtain point cloud data of a power transmission line tower;
s2, point cloud data are selected, and the inclination angle of the power transmission line tower is evaluated;
s3, planning a route survey area and a route according to the path of the power transmission line, obtaining a holographic image in a channel corridor of the power transmission line by using an unmanned aerial vehicle, and calculating the height space of a tower and a hanging line;
s4, calibrating coordinates of the power transmission line tower by acquiring parameters of the power transmission line tower;
and S5, identifying the defects of the power transmission line tower by acquiring the real-time video of the unmanned aerial vehicle, and giving an alarm if the defects exist.
2. The automatic measurement method for the power transmission line engineering construction parameters based on the laser point cloud as recited in claim 1, wherein the method comprises the following steps: the method comprises the following steps that S1, after the point cloud data of the power transmission line tower are obtained, point cloud data are preprocessed, and the preprocessing method comprises the following steps: and carrying out abnormal value elimination preprocessing operation on the original point cloud data, and then separating out ground points and non-ground points through filtering and sorting processing.
3. The automatic measurement method for the power transmission line engineering construction parameters based on the laser point cloud as claimed in claim 1, characterized in that: the specific steps of S2 include:
s201, selecting tower point cloud in the laser point cloud of the power transmission line tower, and removing miscellaneous points and electric wire point cloud in the tower point cloud to obtain filtered tower point cloud;
s202, dividing the filtered tower point cloud into a tower foot point cloud, a tower body point cloud and a tower head point cloud to obtain a tower foot three-dimensional vector model, a tower body three-dimensional vector model and a tower head three-dimensional vector model;
s203, combining a tower foot three-dimensional vector model, a tower body three-dimensional vector model and a tower head three-dimensional vector model into a typical tower type three-dimensional point cloud model;
s204, according to the typical tower type three-dimensional point cloud model, performing central point calculation on a characteristic plane of the power transmission line tower to be evaluated to obtain central point coordinates of each characteristic plane of the tower to be evaluated;
s205, calculating the inclination angle of the tower to be evaluated according to the coordinates of the central point of each characteristic plane of the tower to be evaluated.
4. The automatic measurement method for the power transmission line engineering construction parameters based on the laser point cloud as claimed in claim 3, wherein the method comprises the following steps: the center point calculation in S204 includes the following methods:
s2041, layering of power transmission line towers: horizontally slicing the three-dimensional tower point cloud, enabling the sliced point cloud to be parallel to the horizontal ground, projecting the sliced point cloud on the horizontal ground, and extracting the sliced boundary point cloud;
s2042, boundary segmentation: detecting boundary point clouds by adopting Hough transformation, converting lines of an image space into a parameter space, detecting extreme points of the parameter space, and extracting boundary lines;
s2043, boundary straight line fitting: the boundary point cloud extracted through the Hongh transformation has an abnormal value, and a straight line is fitted by adopting an improved least square method to obtain an accurate boundary; the calculation process is as follows: let the equation of a straight line be
y＝ax+b
Wherein a and b are linear parameters;
the standard deviation of the distances of the points to the fitted line is as follows:
wherein d is i Represents the distance of the ith point to the fitted line,
represents the average distance of all points to the fitted straight line, n is the total number of all points;
the average distance of all points to the fitted line is:
when d is i When the value is more than 2 sigma, the point is an abnormal point, and the point is removed; otherwise, the point is reserved; then, calculating the parameters a and b again; performing cyclic operation on the steps when d i Stopping operation when the standard deviation is less than 2 times;
s2044, central point calculation: the intersection point of the two straight lines is set as:
then the projection coordinates of the corner points of the patch on the XOY plane are
The above intersection point calculation is performed on all the four straight lines to obtain four intersection points of the four edges of the plane, namely four boundary corner points, which are respectively (x) 1 ,y 1 )，(x 2 ,y 2 )，(x 3 ,y 3 )，(x 4 ,y 4 ) Wherein (x) 1 ,y 1 ) Is the upper left corner point coordinate of the plane, (x) 2 ,y 2 ) Upper right corner point coordinate of bit plane, (x) 3 ,y 3 ) Is the coordinate of the lower left corner point of the plane, (x) 4 ,y 4 ) Coordinates of a lower right corner point of the bit plane;
the center point of the plane is then:
the center point of the plane is finally obtained through the calculation of the formula.
5. The automatic measurement method for the power transmission line engineering construction parameters based on the laser point cloud as recited in claim 1, wherein the method comprises the following steps: the specific steps in S3 include:
s301, planning a route survey area and a route according to a route of the power transmission line, acquiring an image of a channel corridor of the power transmission line by using an unmanned aerial vehicle, and measuring three-dimensional coordinates of image control points according to positions of the image control points which are arranged in advance;
s302, extracting characteristic points of collected image data of a power transmission line channel, matching images with overlapping degrees, determining surface elevation information by combining three-dimensional coordinates of image control points, and performing secondary processing on the images to obtain a holographic image in a power transmission line channel corridor;
and S303, spreading the power transmission line path on a holographic image, determining the spatial direction of the power transmission line path, determining the position of the tower along the direction, calling the image pair in the area range, calculating the real-time horizontal epipolar line of the image pair, and calculating the height of the tower and the height space of the hanging line point by using computer vision based on the real-time horizontal epipolar line.
6. The automatic measurement method for the power transmission line engineering construction parameters based on the laser point cloud as recited in claim 1, wherein the method comprises the following steps: the specific steps in S4 include:
s401, obtaining parameters of a power transmission line tower: the method comprises the following steps of measuring longitude of a tower, measuring latitude of the tower, operating span of the tower and corner conditions of the tower;
s402, calculating a measuring span according to the tower measuring longitude and the tower measuring latitude, comparing the measuring span with an operating span, and determining the tower needing to be calibrated;
and S403, converting the coordinates of the power transmission line tower into a format which can be identified by geographic information software, and performing coordinate calibration on the tower which is determined to need calibration.
7. The automatic measurement method for the power transmission line engineering construction parameters based on the laser point cloud as recited in claim 1, wherein the method comprises the following steps: the step S4 of measuring the grounding resistance of the power transmission line tower by acquiring parameters of the power transmission line tower is further included, and the specific measuring method comprises the following steps:
acquiring target information of a transmission tower to be measured, wherein the target information is a target measurement frequency point or a measurement parameter of the transmission tower to be measured;
measuring the measured impedance data of the transmission tower to be measured by a loop impedance method;
step three, obtaining an error prediction model of the previously trained target information;
step four, acquiring the bias error of the target information according to the error prediction model;
and fifthly, calibrating the measured impedance data into corrected impedance data according to the deviation error.
8. The automatic measurement method for the power transmission line engineering construction parameters based on the laser point cloud as recited in claim 1, wherein the method comprises the following steps: the specific step of S5 comprises:
s501, collecting defect samples and non-defect sample images of towers and nodes of the power transmission line to form an intelligent defect detection sample data set of the power transmission line;
s502, manually marking the defect position and the defect type of the power transmission line tower on the intelligent defect detection sample data set of the power transmission line, and storing the marking result of each image into an xml format; recording the coordinates of the upper left corner of the outsourcing rectangle, the length and the width of the rectangle and the defect type by the marking result;
s503, training a tower defect detection model by adopting a tow-stage deep convolution neural network target detection algorithm;
s504, deploying the tower sinking detection model to a rear-end server;
and S505, collecting a real-time video of the unmanned aerial vehicle, extracting a key frame image from the real-time video and sending the key frame image to the back-end server through a network, calling the tower sinking detection model by the back-end server, detecting the defects and defect types existing in the power transmission line tower, and sending out early warning information.
9. The automatic measurement method for the power transmission line engineering construction parameters based on the laser point cloud as recited in claim 8, wherein the method comprises the following steps: the S501 further includes an ROI region on the given input image, and a frame of the ROI region on the given input image is represented in a distribution manner as follows:
p(x，y，w，h|I)＝p(x，y|I)p(w，h|x，y，I)
wherein x and y respectively represent the window center coordinates of the ROI area, w represents the window width of the ROI area, h represents the window height of the ROI area, I represents the original image, and p is the probability.
10. The automatic measurement method for the power transmission line engineering construction parameters based on the laser point cloud as recited in claim 8, wherein the method comprises the following steps: the S5 also comprises a DenseBlock module which is defined as follows:
x 1 ＝H l ([x 0 ，x l ，…，x l-1 ]
wherein H l Contains Batch Normalization->ReLU->Conv (3 x 3) operation, adopt 4 the DenseBlock module to constitute 121 layers of discernment networks, through Batch Normalization and 1 x 1's convolution and mean value pooling connection between the DenseBlock module, connect full connectivity layer and softmax classifier finally, the classification design is 1000, defines as follows:
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| CN116433750A (en) * | 2023-06-15 | 2023-07-14 | 国网江苏省电力有限公司苏州供电分公司 | Transmission tower extraction method and system based on laser point cloud |
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| CN116433750A (en) * | 2023-06-15 | 2023-07-14 | 国网江苏省电力有限公司苏州供电分公司 | Transmission tower extraction method and system based on laser point cloud |
| CN116433750B (en) * | 2023-06-15 | 2023-08-18 | 国网江苏省电力有限公司苏州供电分公司 | Transmission tower extraction method and system based on laser point cloud |
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