CN115932885A - Overhead transmission line channel cross-over real-time dynamic monitoring method - Google Patents
Overhead transmission line channel cross-over real-time dynamic monitoring methodDownload PDFInfo
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Abstract
Description
Technical Field
The invention relates to a real-time dynamic monitoring method for crossing and crossing of overhead transmission line channels, and belongs to the technical field of electric power.
Background
The safety of the crossed and spanned section of the overhead transmission line is relevant to sag, the sag of the line is easily influenced by factors such as temperature, humidity, wind speed and natural disasters due to long-term exposure of the transmission line in the field, and the crossed and spanned clearance is reduced due to the increase of the radian, so that the phenomena such as electromagnetic mutual interference among phases of the overhead line and flashover are caused, and the adverse effect is generated on the safety of a power grid and the safety of public facilities. At present, technologies such as theodolite, total station, ultrasonic ranging appearance, laser range finder are adopted usually, and cross over clearance measurement is carried out according to certain cycle of patrolling and examining, but can only reflect the safe distance situation when measuring, can't reflect real-time safe distance situation in this cycle of patrolling and examining. Therefore, it is necessary to provide a method for monitoring the crossing and crossing of the overhead transmission line channel in real time, so as to realize real-time measurement of the clearance of the crossing and crossing section and ensure the safety of the crossing and crossing section.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method for monitoring the crossing and crossing of the overhead transmission line channel in real time is provided to overcome the defects of the prior art.
The technical scheme of the invention is as follows: a real-time dynamic monitoring method for cross spanning of overhead power transmission line channels comprises the following steps:
step 1, data acquisition: installing three-dimensional laser radar equipment and a visible light camera on a cross spanning section of the overhead transmission line, and acquiring point cloud data and image data of the cross spanning section in real time;
step 2, data preprocessing: respectively preprocessing the collected two-dimensional image data and the collected three-dimensional point cloud data to remove point cloud noise;
step 3, fusing two three-dimensional data: fusing two-dimensional image data and three-dimensional point cloud data, and assigning texture information of an image to the point cloud under a unified coordinate system to increase the point cloud information from four dimensions (X, Y, Z, I) to seven dimensions (X, Y, Z, I, R, G, B);
step 4, identifying a cross-over target;
step 5, measuring the crossing clearance;
wherein, X and Y represent plane coordinates, Z represents elevation, I represents laser reflection intensity information, R represents a red channel, G represents a green channel, and B represents a blue channel.
Specifically, in the data preprocessing, the two-dimensional image preprocessing is performed by linear or non-linear image-to-image conversion; and the three-dimensional point cloud data is preprocessed by a filtering algorithm.
Specifically, the data preprocessing specifically includes the following steps:
step 2.1, preprocessing two-dimensional image data: firstly, processing each pixel point in an image to be processed in a window traversal mode;
step 2.2, preprocessing three-dimensional point cloud data: and (4) denoising by adopting wavelet analysis and performing wavelet transformation.
Specifically, the specific method for preprocessing the two-dimensional image data includes:
setting the pixel coordinates of any pixel point A in the image to be processed as (x, y), adopting the size of a traversal window pixel as m x n, and performing median filtering on the gray value of the current pixel point A, namely averaging the gray values of all pixel points in the m x n neighborhood of the pixel point A by taking the current pixel point as the center of the window to serve as the gray value of the pixel point A.
Specifically, the method for fusing the two-dimensional data and the three-dimensional data specifically comprises the following steps:
firstly, projecting point cloud and image data to a uniform plane coordinate system, establishing a corresponding relation based on characteristic information by constructing an affine transformation model, realizing coarse registration between the point cloud and the image data, and carrying out spectrum information fusion processing on the registered point cloud; then, realizing registration between the image and the three-dimensional point cloud by utilizing an ICP (inductively coupled plasma) algorithm; and finally, fusing the image spectrum information to each three-dimensional point coordinate of the point cloud to realize pixel-level fusion of the point cloud and the image data.
Preferably, the establishing of the corresponding relationship based on the characteristic information is establishing of the corresponding relationship based on point, line and plane information.
Specifically, the method for identifying the cross-over target specifically includes:
firstly, extracting spectral characteristics, and using the combination of spectral information of the ground features of the image, the laser reflection intensity and wave bands and new characteristic variables generated by operation as characteristic parameters reflecting the difference of the ground features; then extracting geometric characteristics, performing principal component transformation on the three-dimensional coordinates of the neighborhood point set, and performing normalization processing to obtain the spatial distribution characteristics of the neighborhood points; respectively calculating the geometric characteristic parameter values under the corresponding analysis scales by continuously adjusting the size of the spatial analysis scales to obtain a characteristic vector, constructing a characteristic subset, identifying a ground object target of a cross span section by using a random forest algorithm, extracting a lead point set and cross span object points, and constructing a lead point set Pi (x, y, z) and crossover point set Ni (x,y,z)。
Specifically, the method for measuring the crossing clearance comprises the following steps:
according to the extracted lead point set Pi (x, y, z) and crossover point set Ni (x, y, z), sequentially traversing and calculating the space distance D between the wire point and the cross object pointi Then extractingDistance Di Minimum value of Dmin I.e. the minimum clearance between the wire and the crossover.
The invention has the beneficial effects that: compared with the prior art, the method has the advantages that the real-time monitoring equipment is installed on the cross spanning section of the overhead transmission line, the two-dimensional image data and the three-dimensional point cloud data of the cross spanning section are obtained, then the obtained data are preprocessed, the influence of noise is removed, the two-dimensional data and the three-dimensional data are fused, the abundant color information and texture information of the two-dimensional image data and the accurate three-dimensional space position information of the three-dimensional point cloud data are fully utilized, the cross spanning target is extracted based on the fusion of the two-dimensional data and the cross spanning clearance is calculated by using an iterative search mode, the problem that the existing cross spanning clearance measurement mode is insufficient in real-time is solved, accidents such as line tripping and the like caused by overlarge sag and insufficient safe clearance between a line and a spanned object can be effectively avoided, and the safety of power transmission is improved.
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FIG. 1 is a flow chart of the present invention.
Detailed Description
In order to better understand the technical solutions, the technical solutions will be described in detail below with reference to the drawings and the detailed description.
Example 1 was carried out: referring to fig. 1, the present embodiment employs a real-time dynamic monitoring method for cross-over of overhead power transmission line channels, including:
step 1, data acquisition: installing three-dimensional laser radar equipment and a visible light camera on the cross spanning section of the overhead transmission line, and acquiring point cloud data and image data of the cross spanning section in real time; the laser point cloud can rapidly restore the three-dimensional scene of the channel and realize the measurement of the spatial distance, and the image can rapidly identify the target, so that the laser point cloud and the image are combined and applied to cross-over real-time dynamic monitoring.
Step 2, data preprocessing: respectively preprocessing the collected two-dimensional image data and the three-dimensional point cloud data, wherein the two-dimensional image preprocessing is to perform denoising and contrast enhancement effects on a shot original image through linear or nonlinear transformation from the image to the image; the three-dimensional point cloud data preprocessing is to remove point cloud noise data through a filtering algorithm.
Step 2.1, preprocessing two-dimensional image data: firstly, processing each pixel point in the image to be processed in a window traversal mode. Setting the pixel coordinates of any pixel point A in the image to be processed as (x, y), adopting the size of a traversal window pixel as m x n, and performing median filtering on the gray value of the current pixel point A, namely averaging the gray values of all pixel points in the m x n neighborhood of the pixel point A by taking the current pixel point as the center of the window to serve as the gray value of the pixel point A. The interference of image noise to the effective information of the image can be effectively reduced through the filtered image; after noise interference is filtered, an image enhancement method based on logarithm is adopted to carry out image enhancement processing, and gray values of all pixel points of the image are used as true numbers in the logarithm. The gray information of the whole image is converted to a proper interval by utilizing a logarithmic gray value mode, so that the image information of a high gray part is effectively reduced, the image information of a low gray part is enhanced, and further more details in the image are displayed.
Step 2.2, preprocessing three-dimensional point cloud data: due to the complex channel environment, the collected three-dimensional laser point cloud data always has noise, and the quality of the point cloud data is reduced. The wavelet analysis is adopted for denoising, and is a time-frequency analysis, so that the signals can be analyzed in a frequency domain and a time domain at the same time, the noise and mutation parts of the input signals can be effectively distinguished, and finally the signals are denoised; by wavelet transform, a higher frequency resolution is used when analyzing the high frequency components of the signal; while in analyzing the low frequency content, a lower frequency resolution is used. Where a stationary or low frequency signal is a useful signal and a high frequency signal is typically noise.
Step 3, fusing two three-dimensional data: the two-dimensional image data and the three-dimensional point cloud data are fused, the texture information of the image is given to the point cloud under a unified coordinate system, the point cloud information is increased from four-dimensional (X, Y, Z, I) to seven-dimensional (X, Y, Z, I, R, G, B), and more accurate characteristic conditions are provided for subsequent point cloud processing and information application. Firstly, projecting point cloud and image data to a uniform plane coordinate system, establishing a corresponding relation based on characteristic information (points, lines and surfaces) by constructing an affine transformation model, realizing coarse registration between the point cloud and the image data, and carrying out spectrum information fusion processing on the registered point cloud; then, realizing registration between the image and the three-dimensional point cloud by utilizing an ICP (inductively coupled plasma) algorithm; and finally, fusing the image spectrum information to each three-dimensional point coordinate of the point cloud to realize pixel-level fusion of the point cloud and the image data. Wherein, X and Y represent plane coordinates, Z represents elevation, I represents laser reflection intensity information, R represents a red channel, G represents a green channel, and B represents a blue channel.
Step 4, cross-over target identification: firstly, extracting spectral characteristics, and using the combination of spectral information of the ground features of the image, the laser reflection intensity and wave bands and new characteristic variables generated by operation as characteristic parameters reflecting the difference of the ground features; then extracting geometric features, wherein the spatial relative position relationship between points in a geographic space and neighborhoods can reflect the geometric feature difference of ground objects, and the three-dimensional coordinates of a neighborhood point set are subjected to principal component transformation and normalization processing to obtain the spatial distribution features of the domain points; respectively calculating the geometric characteristic parameter values under the corresponding analysis scales by continuously adjusting the size of the spatial analysis scales to obtain a characteristic vector, constructing a characteristic subset, identifying a ground object target of a cross span section by using a random forest algorithm, extracting a lead point set and cross span object points, and constructing a lead point set Pi (x, y, z) and crossover object point set Ni (x,y,z)。
Step 5, measurement of crossing clearance: according to the extracted lead point set Pi (x, y, z) and crossover point set Ni (x, y, z), sequentially traversing and calculating the space distance D between the guide line point and the cross object pointi Then extracting the distance Di Minimum value of Dmin I.e. the minimum clearance between the wire and the crossover.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (8)
1. A real-time dynamic monitoring method for crossing of overhead transmission line channels is characterized by comprising the following steps:
step 1, data acquisition: installing three-dimensional laser radar equipment and a visible light camera on a cross spanning section of the overhead transmission line, and acquiring point cloud data and image data of the cross spanning section in real time;
step 2, data preprocessing: respectively preprocessing the collected two-dimensional image data and the collected three-dimensional point cloud data to remove point cloud noise;
step 3, fusing two three-dimensional data: fusing two-dimensional image data and three-dimensional point cloud data, and assigning image texture information to the point cloud under a unified coordinate system to increase the point cloud information from four dimensions (X, Y, Z, I) to seven dimensions (X, Y, Z, I, R, G, B);
step 4, identifying a cross-over target;
step 5, measuring the crossing clearance;
wherein, X and Y represent plane coordinates, Z represents elevation, I represents laser reflection intensity information, R represents a red channel, G represents a green channel, and B represents a blue channel.
2. The overhead power line channel crossing real-time dynamic monitoring method of claim 1, wherein in the data preprocessing, the two-dimensional image preprocessing is processing by image-to-image linear or non-linear transformation; the three-dimensional point cloud data is preprocessed through a filtering algorithm.
3. The overhead transmission line channel crossing real-time dynamic monitoring method according to claim 2, wherein the data preprocessing specifically comprises the following steps:
step 2.1, preprocessing two-dimensional image data: firstly, processing each pixel point in an image to be processed in a window traversal mode;
step 2.2, preprocessing three-dimensional point cloud data: and denoising by wavelet analysis through wavelet transformation.
4. The overhead transmission line channel crossing real-time dynamic monitoring method according to claim 3, wherein the specific method for preprocessing the two-dimensional image data is as follows:
setting the pixel coordinates of any pixel point A in the image to be processed as (x, y), adopting the size of a traversal window pixel as m x n, and performing median filtering on the gray value of the current pixel point A, namely averaging the gray values of all pixel points in the m x n neighborhood of the pixel point A by taking the current pixel point as the center of the window to serve as the gray value of the pixel point A.
5. The overhead transmission line channel crossing real-time dynamic monitoring method according to claim 2, wherein the method for fusing the two-dimensional data specifically comprises the following steps:
firstly, projecting point cloud and image data to a uniform plane coordinate system, establishing an affine transformation model and establishing a corresponding relation based on characteristic information to realize coarse registration between the point cloud and the image data, and performing spectral information fusion processing on the registered point cloud; then, realizing registration between the image and the three-dimensional point cloud by utilizing an ICP (inductively coupled plasma) algorithm; and finally, fusing the image spectrum information to each three-dimensional point coordinate of the point cloud to realize pixel-level fusion of the point cloud and the image data.
6. The overhead power line channel crossing span real-time dynamic monitoring method of claim 5, wherein the establishing a correspondence based on the characteristic information is establishing a correspondence based on point, line and plane information.
7. The overhead transmission line channel crossing real-time dynamic monitoring method according to claim 4, wherein the method for identifying crossing targets specifically comprises:
first extracting spectral featuresThe new characteristic variable generated by combination and operation among the spectral information of the ground features, the laser reflection intensity and the wave bands of the image is used as a characteristic parameter reflecting the difference of the ground features; then extracting geometric features, performing principal component transformation on the three-dimensional coordinates of the neighborhood point set, and performing normalization processing to obtain the spatial distribution features of the neighborhood points; respectively calculating the geometric characteristic parameter values under the corresponding analysis scales by continuously adjusting the size of the spatial analysis scales to obtain a characteristic vector, constructing a characteristic subset, identifying a ground object target of a cross span section by using a random forest algorithm, extracting a lead point set and cross span object points, and constructing a lead point set Pi (x, y, z) and crossover object point set Ni (x,y,z)。
8. The overhead transmission line channel crossing real-time dynamic monitoring method of claim 7, wherein the method for measuring crossing clearance specifically comprises:
according to the extracted lead point set Pi (x, y, z) and crossover point set Ni (x, y, z), sequentially traversing and calculating the space distance D between the wire point and the cross object pointi Then extracting the distance Di Minimum value of Dmin I.e. the minimum clearance between the wire and the crossover.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116577788A (en)* | 2023-07-12 | 2023-08-11 | 南方电网数字电网研究院有限公司 | Power transmission line foreign matter intrusion monitoring method, device and computer equipment |
| CN118965658A (en)* | 2024-09-29 | 2024-11-15 | 国网浙江省电力有限公司建设分公司 | Arrangement method and system for transmission line crossing measurement |
| US12198447B1 (en)* | 2023-11-13 | 2025-01-14 | Molar Intelligence (Hangzhou) Co., Ltd. | Method and apparatus for 4D road scene annotation based on time series data, and electronic device |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116577788A (en)* | 2023-07-12 | 2023-08-11 | 南方电网数字电网研究院有限公司 | Power transmission line foreign matter intrusion monitoring method, device and computer equipment |
| CN116577788B (en)* | 2023-07-12 | 2024-01-23 | 南方电网数字电网研究院有限公司 | Power transmission line foreign matter intrusion monitoring method, device and computer equipment |
| US12198447B1 (en)* | 2023-11-13 | 2025-01-14 | Molar Intelligence (Hangzhou) Co., Ltd. | Method and apparatus for 4D road scene annotation based on time series data, and electronic device |
| CN118965658A (en)* | 2024-09-29 | 2024-11-15 | 国网浙江省电力有限公司建设分公司 | Arrangement method and system for transmission line crossing measurement |
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