WO2022061945A1

WO2022061945A1 - Power line safe distance measurement method

Info

Publication number: WO2022061945A1
Application number: PCT/CN2020/118734
Authority: WO
Inventors: 黄练栋; 廖卫平; 方涛; 温健锋; 伍建炜
Original assignee: 广东电网有限责任公司; 广东电网有限责任公司江门供电局
Priority date: 2020-09-27
Filing date: 2020-09-29
Publication date: 2022-03-31
Also published as: CN112147633A

Abstract

A power line safe distance measurement method, comprising the following steps: S1. an unmanned aerial vehicle-mounted LiDAR system performs scanning, so as to obtain a three-dimensional space image; and then performing real-time measurement on the distances between wires and ground features, so as to acquire original LiDAR point cloud data; S2. performing segmentation processing on the original LiDAR point cloud data, so as to obtain wire point cloud data and other background point cloud data; S3. Performing fitting on discrete data in the wire point cloud data, so as to extract a two-dimensional wire projection line; S4. back-projecting the two-dimensional wire projection line to the three-dimensional space image, so as to obtain precise wire point cloud data; S5. performing area division on the precise wire point cloud data, so as to finally generate three-dimensional wire vector data; and S6. performing superimposition and analysis on the three-dimensional wire vector data and other background point cloud data, so as to report a dangerous ground feature. The present invention can satisfy the requirement that maintenance personnel handles safety hazards immediately in real time.

Description

A method for detecting safety distance of power line

technical field

The invention relates to the technical field of power line safety detection, and more particularly, to a power line safety distance detection method.

Background technique

For the detection of the safety distance of the conductor line, the traditional conductor line inspection process is that the staff inspects the power facilities, such as towers, conductor/ground wires, transformers, insulators, cross arms, knife switches and other equipment, and conducts manual visual judgment on the line section or The total station measures, and records the inspection situation in the form of paper medium, and then manually enters it into the computer. The patrol inspection is affected by too many human factors, which will endanger the life and safety of the line selection workers in the dangerous area, and the manual input data volume is large, and the manual data input process is prone to errors; The inspection quality cannot be guaranteed, and the safety status of the line cannot be guaranteed, leaving a potential safety hazard; and the frequent occurrence points with insufficient safety distance of the line are usually in places that are hard to reach. These measurement methods are blocked by trees, buildings, etc. and visual perspective. It is difficult to make an accurate and effective judgment on the suspected overrun points, and it cannot meet the needs of the development and safe operation of modern power grids. Ultra-high voltage power grids urgently need efficient, advanced and scientific conductor/ground line safety detection methods.

The Chinese patent document with the publication number CN106772412A discloses a method and device for measuring the spatial distance of the transmission line of the unmanned aerial vehicle, which can accurately measure the spatial distance of the transmission line of the unmanned aerial vehicle, identify obstacles, and make the distance measurement of the line inspection more accurate. Efficient and fast.

However, in the above scheme, the safety distance measurement is still performed by operating the drone to fly close to the line for visual observation, and it still fails to accurately measure and judge the line sag and safety distance.

SUMMARY OF THE INVENTION

The purpose of the present invention is to overcome the deficiencies of the prior art, and to provide a method for detecting the safety distance of a power line, which can meet the requirements of maintenance personnel to deal with potential safety hazards on the spot in real time.

In order to solve the above-mentioned technical problems, the technical scheme adopted in the present invention is:

Provided is a power line safety distance detection method, comprising the following steps:

S1. Scan the section to be inspected by the UAV LiDAR system to obtain a three-dimensional space image; then measure the distance between the wire and the ground object in real time to obtain the original LiDAR point cloud data;

S2. After step S1, the original LiDAR point cloud data is divided and processed to obtain wire point cloud data and other background point cloud data;

S3. After step S2, the discrete data in the wire point cloud data is fitted, and a two-dimensional wire projection line is extracted;

S4. After step S3, back-project the two-dimensional wire projection line to the three-dimensional space image to obtain accurate wire point cloud data;

S5. After step S4, the accurate wire point cloud data is divided into regions, and the regional centroid is used as a vector node to output wire polyline data, and finally generate three-dimensional wire vector data;

S6. After step S5, superimpose and analyze the three-dimensional wire vector data and the other background point cloud data, and then report the dangerous objects.

Further, in step S1, let the moment when the UAV LiDAR system laser scans point P be t _L , and the coordinates of the laser scanning point of point P at this moment in the WGS-84 coordinate system are:

In the formula,

represents the three-dimensional coordinate point of the laser scanning point in the WGS-84 coordinate system at time t _L ,

Represents the three-dimensional coordinate point of the POS system reference point in the WGS-84 coordinate system at time t _L ,

represents the eccentric component from the laser scanning center to the reference point of the POS system at time t _L , [d _L sinθ _L ,d _L cosθ _L ,0] ^T represents the distance vector of laser ranging at time t _L ,

represents the system pose matrix at time t _L ,

represents the rotation matrix at time t _L ;

Suppose the moment when the camera of the UAV LiDAR system shoots point P is t _S , and the coordinates of the imaging point of point P at this moment in the WGS-84 coordinate system are:

In the formula,

Indicates the three-dimensional coordinate point of the camera imaging point in the WGS-84 coordinate system at time t _s ,

Represents the three-dimensional coordinate point of the POS system reference point in the WGS-84 coordinate system at time t _s ,

represents the eccentric component from the camera imaging point to the POS system reference point at time t _s ,

represents the system attitude matrix at time t _s ,

represents the rotation matrix at time t _s ;

By combining the above two equations, the coordinates of point P in the imaging system can be obtained as:

Further, in step S1, the calculation formula of the color information of each point in the original LiDAR point cloud data is:

In the formula, (r _p , c _p ) represent the coordinates of the image coordinate system in pixels, (r _p , _cp ) represent the coordinates of the intersection of the center optical axis of the image and the image plane, and f _u and f _v represent X, Y, respectively The equivalent focal length of the direction.

Further, in step S2, the wire point cloud data and other background point cloud data are segmented through the elevation threshold segmentation.

Further, the step S3 specifically includes the following steps:

S31. Gridize the wire point cloud data specifications, and generate an elevation image map;

S32. Perform edge extraction on the elevation image map to obtain the edge of the wire data that is highlighted by the elevation;

S33. When multiple wires are in the same image, the wire point cloud is clustered to determine the specific point cloud data participating in the fitting of a specific wire.

Further, in step S32, edge extraction is performed by the Canny operator or the Laplacian operator.

Further, in step S33, the clusters of the straight lines are extracted by the Hough transform or the maximum likelihood method, and then the straight lines are fitted by the least squares method.

Further, the step S4 specifically includes: back-projecting the two-dimensional wire projection line to the three-dimensional spatial image as an initial clustering kernel, and then performing distance clustering on the spatial point cloud to obtain accurate wire point cloud data.

Further, in step S5, the three-dimensional wire vector data is divided into areas by using the area division theory in linear programming.

Further, the step S6 specifically includes the following steps:

S61. Set the shortest distance parameter between the wire and the surface object, and then retrieve according to the shortest distance parameter to obtain the dangerous wire segment;

S62. Set the limit parameter, and then detect the distance between adjacent wire lines. When the distance is less than the limit parameter, the adjacent wire line is a dangerous line segment;

S63. Mark the dangerous line segment and the dangerous line segment.

Compared with the prior art, the beneficial effects of the present invention are:

The invention is a safety distance detection method for power lines, which adopts unmanned aerial vehicles to inspect and maintain power transmission lines in daily inspection work, which is beneficial to the electric power department to formulate targeted maintenance measures and increase line operation and maintenance work. To ensure the safe operation of important transmission lines; it is beneficial to increase special patrols in key sections after heavy rainfall, and increase the number of equipment inspections under heavy load operation; it is also beneficial to regularly check and clean up trees and illegal buildings in line passages , to ensure that the transmission channel is unobstructed. The present invention is based on the unmanned aerial vehicle LiDAR system, and automatically completes data processing and analysis in the process of data acquisition, that is, the distance between the guide/ground wire and the ground object is measured in real time through the unmanned aerial vehicle LiDAR system, and the real-time distance between the ground wire and the ground object is measured through the unmanned aerial vehicle LiDAR system. The positioning and attitude data and laser scanning data are sent back in real time, and then calculated in real time to generate a three-dimensional LiDAR point cloud, and real-time detection of the distance between the guide/ground line and the ground object, and automatic alarm when a problem is found, so as to meet the needs of maintenance personnel to deal with safety in real time. Hidden requirements.

Description of drawings

FIG. 1 is a flowchart of a method for detecting a safe distance of a power line according to the present invention.

FIG. 2 is a flowchart of step S6 of the present invention.

FIG. 3 is a structural diagram of the unmanned aerial vehicle LiDAR system of the present invention.

FIG. 4 is a schematic diagram of the unmanned aerial vehicle LiDAR system of the present invention.

FIG. 5 is a working flow chart of RTK carrier phase differential positioning according to the present invention.

FIG. 6 is a flow chart of generating a point cloud in real time according to the present invention.

FIG. 7 is a schematic diagram of the relationship between the coordinate systems of the present invention.

detailed description

The present invention will be further described below in conjunction with specific embodiments. Among them, the accompanying drawings are only used for exemplary description, and they are only schematic diagrams, not physical drawings, and should not be construed as restrictions on this patent; in order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings will be omitted, The enlargement or reduction does not represent the size of the actual product; it is understandable to those skilled in the art that some well-known structures and their descriptions in the accompanying drawings may be omitted.

The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there are terms “upper”, “lower”, “left” and “right” The orientation or positional relationship indicated by etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the indicated device or element must have a specific orientation, with a specific orientation. Orientation structure and operation, so the terms describing the positional relationship in the accompanying drawings are only used for exemplary illustration, and should not be construed as a limitation on the present patent. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to specific situations.

Example 1

Figures 1 to 7 show an embodiment of a method for detecting a safe distance of a power line according to the present invention, comprising the following steps:

S1. Scan the section to be inspected by the UAV LiDAR system to obtain a three-dimensional space image; then measure the distance between the wire and the ground object in real time to obtain the original LiDAR point cloud data.

As shown in Figure 3 and Figure 4, the UAV LiDAR system is based on a low-altitude multi-rotor UAV aircraft platform, which is equipped with a POS system, a sensing system and a control system. On the basis of GPS/IMU high-precision combined positioning, the automatic synchronization acquisition of high-resolution images and three-dimensional laser point cloud data, integrated fusion processing and three-dimensional visualization are realized, which are used for low-altitude remote sensing earth observation and rapid high-precision mapping. The basic principle is shown in Figure 5. Based on the CORS base station, this application can realize RTK (Real-time kinematic, real-time dynamic) carrier phase differential positioning. Coordinate information (RTCM32 format) is sent to the user station together. The user receives the carrier phase of the GPS satellite and the carrier phase from the base station, and forms phase difference observations for real-time processing, which can give centimeter-level positioning results in real time. The positioning type of GNSS in stable working state is RTK (narrow-int) solution.

The sensing system includes an imaging system and a laser rangefinder. Among them, the laser rangefinder is used to transmit and receive laser signals, and is used to measure the distance from the launch reference point to the laser foot point. The imaging system is a CCD camera, which is used to obtain true color or infrared digital image information of ground landforms. During data post-processing, the generated digital elevation model DEM can be used to correct the obtained original image to obtain an orthophoto. In this embodiment, a high-resolution CCD camera is used.

The control system includes a synchronous controller and a single-board computer. The control system adopts a navigation, positioning and management system to synchronously record the increments of the angular velocity and acceleration of the IMU, as well as the position of the GPS, the data of the laser range finder and the CCD camera.

The role of the POS system is to determine the external orientation elements such as the position and attitude of the sensing system through strict data calculation (Kalman filtering), so as to achieve sensing positioning and orientation with little ground control. The POS system includes GPS and inertial navigator IMU. GPS is used to determine the spatial position of the central projection of the scanning device; inertial navigator IMU, that is, an attitude measurement device, generally adopts an inertial measurement device to determine the spatial attitude parameters of the main optical axis of the laser rangefinder. Among them, GPS adopts the real-time differential positioning mode provided by M600, and the position accuracy of post-processing can reach centimeter level. The PPS pulse and digital time signal of GPS are used for laser scanner time synchronization and camera trigger synchronization signal. The laser rangefinder can obtain ranging signals of 100,000 points in one second. The information of each point includes time, angle, distance and reflection intensity information. The longest scanning distance is 1000 meters, and the angular resolution can reach 0.1°. Distance accuracy 25mm. Before the laser rangefinder starts scanning, it needs to use GPS to perform time synchronization, so that each scanning point can obtain accurate GPS time. In preprocessing, each point is fused with POS data through time to generate 3D point cloud data. Differential GPS positioning technology, that is, a GPS receiver is placed on the reference station for observation. According to the known precise coordinates of the base station, the GPS satellite signal is measured to calculate the differential correction amount from the base station to the satellite, and the base station broadcasts the differential correction amount to the user receivers located in the differential service range in real time. When the user receiver performs GPS observation, it also receives the correction number sent by the base station, and corrects the positioning result, thereby improving the positioning accuracy. It can be understood as placing a GPS receiver (called a reference station) on a point with known coordinates, using the known coordinates and satellite ephemeris to calculate the correction value of the observed value, and correcting it through a radio device (called a data link). The values are sent to a GPS receiver in motion (called a rover), and the rover applies the received corrections to make corrections to its own GPS observations to remove satellite clock errors, receiver clock errors, atmospheric ionosphere, and The effect of tropospheric refraction error.

Figure 6 shows the flow chart of real-time point cloud generation. The UAV LiDAR system is a typical dynamic measurement system. The system and CCD camera data are fused and registered to obtain color point cloud data.

As shown in Figure 7, O _W -X _W Y _W Z _W represents the WGS-84 coordinate system, and the WGS-84 coordinate system is a geocentric coordinate system; O _POS -X _POS Y _POS Z _POS represents the POS coordinate system, and O _S -X _S Y _S Z _S represents the camera imaging coordinate system, and O _L -X _L Y _L Z _L represents the laser scanning coordinate system.

Wherein, let the time of the laser scanning point P of the UAV LiDAR system be t _L , and the coordinates of the laser scanning point of point P at this time in the WGS-84 coordinate system are:

In the formula,

represents the eccentric component from the laser scanning center to the reference point of the POS system at time t _L ,

represents the distance vector of laser ranging at time t _L ,

represents the system pose matrix at time t _L ,

represents the rotation matrix at time t _L ;

In the formula,

represents the system attitude matrix at time t _s ,

represents the rotation matrix at time t _s ;

By combining the above two equations, the coordinates of point P in the camera imaging system can be obtained as:

Also, the calculation formula for the color information of each point in the original LiDAR point cloud data is:

S2. After step S1, the original LiDAR point cloud data is segmented by the iterative threshold segmentation method in the elevation threshold segmentation to obtain the wire point cloud data and other background point cloud data, so as to realize the rough extraction of the wire point cloud data.

S3. After step S2, fit the discrete data in the wire point cloud data, and then extract the two-dimensional wire projection line.

Wherein, step S3 specifically includes the following steps:

S32. For the elevation image map, perform edge extraction through Canny operator or Laplacian operator to obtain the edge of the wire data highlighted by the elevation;

S33. When multiple wires are in the same image, the wire point cloud is clustered to determine the specific point cloud data participating in the fitting of a specific wire. When fitting, specifically, the Hough transform or the maximum likelihood method can be used to extract the clustering of the straight line; the least squares method can be used to implement the straight line fitting for the point cloud data where a line cluster is located.

S4. After step S3, back-project the 2D wire projection line to the 3D space image as an initial clustering kernel, and then perform distance clustering on the space point cloud to obtain accurate wire point cloud data. This step is mainly supported by the relevant theories of projection and spatial clustering.

S5. After step S4, the accurate wire point cloud data is divided into regions, and the regional centroid is used as a vector node to output wire polyline data, and finally generate three-dimensional wire vector data. This step is mainly supported by the relevant theories of region segmentation in linear programming, the centroid and centroid theories of space objects.

S6. After step S5, superimpose and analyze the three-dimensional wire vector data and other background point cloud data, and then report the dangerous objects; specifically, combine the three-dimensional wire vector data and other background point cloud data to generate a DSM model, and then Calculate the distance between the DSM model and the wire and the surface object, and then compare it with the standard safety distance, and give an early warning to the area where the three-dimensional wire vector data that is smaller than the standard safety distance is located.

Step S6 specifically includes the following steps:

S61. Set the parameter of the shortest distance between the wire and the surface object as 1m, and then retrieve according to the shortest distance parameter to obtain the dangerous wire segment;

S62. Analyze the vertical distance and horizontal distance of each wire on each section, obtain wire segments with close spacing, and then detect the distance between adjacent wire lines by setting limit parameters. When the distance between adjacent wire lines is When it is less than the limit parameter, the adjacent wire line is a dangerous line segment; it should be noted that the section refers to the plane perpendicular to the direction of the wire;

S63. Mark the dangerous line segment and the dangerous line segment;

S64. Provide the danger level of the line and the coordinate information of the dangerous line segment in the form of a report to assist decision-making.

Obviously, the above-mentioned embodiments of the present invention are only examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those of ordinary skill in the art, changes or modifications in other different forms can also be made on the basis of the above description. There is no need and cannot be exhaustive of all implementations here. Any modifications, equivalent replacements and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the claims of the present invention.

Claims

A method for detecting a safe distance of a power line, comprising the following steps:

S1. Scan the section to be inspected by the UAV LiDAR system to obtain a three-dimensional space image; then measure the distance between the wire and the ground object in real time to obtain the original LiDAR point cloud data;

S2. After step S1, perform segmentation processing on the original LiDAR point cloud data to obtain wire point cloud data and other background point cloud data;

S3. After step S2, the discrete data in the wire point cloud data is fitted, and a two-dimensional wire projection line is extracted;

S4. After step S3, back-project the two-dimensional wire projection line to the three-dimensional space image to obtain accurate wire point cloud data;

S5. After step S4, the accurate wire point cloud data is divided into regions, and the regional centroid is used as a vector node to output wire polyline data, and finally generate three-dimensional wire vector data;

S6. After step S5, superimpose and analyze the three-dimensional wire vector data and the other background point cloud data, and then report the dangerous objects.
A power line safety distance detection method according to claim 1, characterized in that, in step S1, the time when the unmanned aerial vehicle LiDAR system laser scans point P is set to be t L , and at this time point P is The coordinates of the laser scanning point in the WGS-84 coordinate system are:

In the formula,
represents the three-dimensional coordinate point of the laser scanning point in the WGS-84 coordinate system at time t L ,
Represents the three-dimensional coordinate point of the POS system reference point in the WGS-84 coordinate system at time t L ,
represents the eccentric component from the laser scanning center to the reference point of the POS system at time t L , [d L sinθ L ,d L cosθ L ,0] T represents the distance vector of laser ranging at time t L ,
represents the system pose matrix at time t L ,
represents the rotation matrix at time t L ;

Suppose the moment when the camera of the UAV LiDAR system shoots point P is t s , and the coordinates of the imaging point of point P at this moment in the WGS-84 coordinate system are:

In the formula,
Indicates the three-dimensional coordinate point of the camera imaging point in the WGS-84 coordinate system at time t s ,
Represents the three-dimensional coordinate point of the POS system reference point in the WGS-84 coordinate system at time t s ,
represents the eccentric component from the camera imaging point to the POS system reference point at time t s ,
represents the system attitude matrix at time t s ,
represents the rotation matrix at time t s ;

By combining the above two equations, the coordinates of point P in the imaging system can be obtained as:
A power line safety distance detection method according to claim 2, characterized in that, in step S1, the calculation formula for the color information of each point in the original LiDAR point cloud data is:

In the formula, (r p , c p ) represent the coordinates of the image coordinate system in pixels, (r p , cp ) represent the coordinates of the intersection of the center optical axis of the image and the image plane, and f u and f v represent X, Y, respectively The equivalent focal length of the direction.
The method for detecting a safe distance of a power line according to claim 3, characterized in that, in step S2, the wire point cloud data and other background point cloud data are segmented by an elevation threshold segmentation.
The method for detecting a safe distance of a power line according to claim 1, wherein the step S3 specifically includes the following steps:

S31. Gridize the wire point cloud data specifications, and generate an elevation image map;

S32. Perform edge extraction on the elevation image map to obtain the edge of the wire data that is highlighted by the elevation;

S33. When multiple wires are in the same image, the wire point cloud is clustered to determine the specific point cloud data participating in the fitting of a specific wire.
A method for detecting a safe distance of a power line according to claim 5, characterized in that, in step S32, edge extraction is performed by a Canny operator or a Laplacian operator.
A power line safety distance detection method according to claim 5, characterized in that, in step S33, clusters of straight lines are extracted by Hough transform or maximum likelihood method, and then straight line fitting is performed by least squares method.

Conduct wire fitting using any of the least squares methods.
The method for detecting a safe distance of a power line according to claim 1, wherein the step S4 specifically comprises: back-projecting the two-dimensional wire projection line to the three-dimensional space image as an initial clustering kernel, and then Perform distance clustering on the spatial point cloud to obtain accurate wire point cloud data.
The method for detecting a safe distance of a power line according to claim 1, characterized in that, in step S5, the three-dimensional wire vector data is divided into areas by using the area division theory in linear programming.
The method for detecting a safe distance of a power line according to claim 1, wherein the step S6 specifically includes the following steps:

S61. Set the shortest distance parameter between the wire and the surface object, and then retrieve according to the shortest distance parameter to obtain the dangerous wire segment;

S62. Set the limit parameter, and then detect the distance between adjacent wire lines. When the distance is less than the limit parameter, the adjacent wire line is a dangerous line segment;

S63. Mark the dangerous line segment and the dangerous line segment.