Power transmission line visual monitoring ranging method and system based on three-dimensional laser point cloud

Abstract

The application relates to the field of visual ranging technology, in particular to a power transmission line visual monitoring ranging method and system based on three-dimensional laser point clouds. A UAV transmits laser to a power transmission line, acquires regional point cloud data, obtains power line point cloud data and reference object point cloud data of the power transmission line, analyzes a point cloud data measurement precision index, carries out subsequent processing when the point cloud data measurement precision index is not lower than a set threshold, otherwise reacquires data, acquires a first power line point cloud group and a second power line point cloud group, extracts standard power line point clouds from the first power line point cloud group and the second power line point cloud group, fits the standard power line point clouds with the reference object point cloud data, obtains a model of the power line and the reference object, and carries out ranging based on the model. The application introduces a point cloud data measurement precision index to screen data, accurately extracts power line point cloud data in combination with various algorithms, and improves the modeling precision and ranging accuracy of the power transmission line.

Description

Technical Field

[0001] This invention relates to the field of visual ranging technology, and in particular to a method and system for visual monitoring and ranging of power transmission lines based on three-dimensional laser point clouds. Background Technology

[0002] With the rapid development of computer and image processing technologies, computer vision technology has been widely applied in many fields such as robot control, autonomous driving, non-contact measurement, and intelligent power grid inspection. Visual monitoring technology has been widely used in intelligent inspection of transmission lines, enabling online monitoring, intelligent inspection, intelligent identification of hidden dangers, and alarms of key crossing sections, external damage, and wildfires, providing effective support for the visual remote inspection of transmission lines.

[0003] China Southern Power Grid has conducted research on the application of transmission line ranging technology. For example, it uses Global Navigation Satellite System (GNSS) and laser rangefinders to perform high-precision ranging of transmission lines and measure parameters such as the length and height of the lines. It also uses drones equipped with ranging devices to conduct comprehensive inspections and measurements of transmission lines, quickly obtaining the location information and parameter data of the lines.

[0004] For example, the invention patent with publication number CN116012429A discloses a method for determining potential hazards in power transmission corridors based on laser point clouds and GIM 3D models. This method includes: acquiring laser point cloud (LAS) data of the power transmission corridor and GIM 3D model data of the power transmission line along the same route as the corridor; objectifying the laser point cloud (LAS) data of the power transmission corridor into individual objects based on the entity object type of the corridor, obtaining point cloud individual LAS data for different entity objects; performing lightweight processing on the point cloud individual LAS data and the power transmission line GIM 3D model data to obtain lightweight point cloud individual data and power transmission line 3D model data; determining potential hazards based on the coordinate fitting relationship between the point cloud individual data and the power transmission line 3D model data; constructing operation and maintenance ledger data for the power transmission line 3D model based on the determined potential hazards and the established correlation, and loading it into the constructed power transmission digital twin platform for visualization.

[0005] For example, the invention patent with publication number CN114119917A discloses a visualization method for hazard ranging based on a high-precision three-dimensional model of a power transmission line, which includes: acquiring monitoring data of the target area, scanning the target area to collect point cloud data; preprocessing the point cloud data, including removing redundant point cloud data, removing ground point cloud data, and density checking; determining the location of the hazard target and measuring its distance. Summary of the Invention

[0006] In view of the problems existing in the prior art, the present invention is proposed.

[0007] Therefore, the problem that this invention aims to solve is that in the prior art, UAVs are greatly affected by the environment during flight, resulting in errors in the acquired point cloud data. Secondly, when extracting power lines from the acquired point cloud data, it is easily affected by various factors, resulting in insufficient accuracy of the extracted power lines. Both of these factors lead to insufficient accuracy in the subsequent modeling of power transmission lines, which in turn affects the accuracy of ranging and causes ranging errors.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0009] In a first aspect, embodiments of the present invention provide a method for visual monitoring and ranging of transmission lines based on three-dimensional laser point clouds, which includes emitting lasers at the transmission line by a UAV to acquire regional point cloud data, and obtaining power line point cloud data and reference object point cloud data of the transmission line.

[0010] The point cloud data measurement accuracy index is analyzed. If the point cloud data measurement accuracy index is not lower than a set threshold, subsequent processing is performed; otherwise, the point cloud data of the region is reacquired.

[0011] Acquire the first power line point cloud group and the second power line point cloud group, and extract the standard power line point cloud from them;

[0012] The standard power line point cloud is fitted with the reference object point cloud data to obtain a model of the power line and the reference object, and distance measurement is performed based on the model.

[0013] As a preferred embodiment of the transmission line visualization monitoring and ranging method based on three-dimensional laser point clouds described in this invention, the specific steps for classifying the regional point cloud data include:

[0014] Read the regional point cloud data collected by the drone and store it in a 3D point cloud data format;

[0015] The point cloud data of the region is processed to remove noise and outliers;

[0016] By filtering out ground point cloud data and low-altitude point cloud data, we obtain power line point cloud data and reference point cloud data for the transmission lines.

[0017] As a preferred embodiment of the transmission line visualization monitoring and ranging method based on three-dimensional laser point clouds described in this invention, the steps for acquiring the first power line point cloud group and the second power line point cloud group include:

[0018] Based on the statistical filtering algorithm, the preprocessed power line point cloud data of the transmission line is coarsely extracted multiple times to obtain multiple first coarsely extracted power line point clouds.

[0019] The first power line point cloud group is obtained by refining the multiple first coarse-extracted power line point clouds using a cloth simulation filtering algorithm.

[0020] Based on the PCA principal component analysis method, multiple coarse extractions are performed on the preprocessed power line point cloud data of the transmission line to obtain multiple second coarse extraction power line point clouds.

[0021] The multiple coarsely extracted power line point clouds are refined using a fast Euclidean clustering algorithm to obtain the second power line point cloud group.

[0022] As a preferred embodiment of the transmission line visualization monitoring and ranging method based on three-dimensional laser point clouds described in this invention, the step of extracting standard power line point clouds from the first power line point cloud group and the second power line point cloud group includes:

[0023] The pairwise similarity of multiple first power line point clouds in the first power line point cloud group is compared to obtain the power line point cloud group N with the highest similarity.

[0024] The pairwise similarity of multiple second power line point clouds in the second power line point cloud group is compared to obtain the power line point cloud group M with the highest similarity.

[0025] The similarity of the power line point cloud group N and the power line point cloud group M are compared, and the group with higher similarity is selected as the standard power line point cloud.

[0026] As a preferred embodiment of the transmission line visualization monitoring and ranging method based on three-dimensional laser point clouds described in this invention, the step of fitting the standard power line point cloud with the reference object point cloud data includes:

[0027] Based on geometric shape analysis methods, point cloud data that conform to the characteristics of the reference object are marked as reference object point clouds;

[0028] A reference point cloud is selected from the obtained standard power line point cloud and reference object point cloud by using geometric features;

[0029] Based on the selected reference point cloud as the alignment target, the standard power line point cloud and the reference object point cloud data are spatially aligned using the ICP algorithm;

[0030] Gaussian smoothing was used to smooth the fitted standard electric field point cloud and the reference point cloud data.

[0031] The smoothed data was modeled using 3D modeling tools to obtain models of the electric field lines and reference objects.

[0032] As a preferred embodiment of the transmission line visualization monitoring and ranging method based on three-dimensional laser point clouds described in this invention, the step of ranging based on the model of the power line and the reference object includes:

[0033] Calculate the shortest distance from the power line to the reference object;

[0034] The distance from a point to a plane is calculated using the formula for the shortest distance from a point to a plane.

[0035] The distance from a point to a surface is calculated using the shortest distance calculation method for the surface.

[0036] Real-time analysis of the ranging error index, which is used to measure the degree of error in the measurement results.

[0037] As a preferred embodiment of the transmission line visualization monitoring and ranging method based on three-dimensional laser point clouds described in this invention, the step of real-time analysis of the ranging error index includes:

[0038] By connecting sensors through a SCADA system, the stability, linearity, and temperature during distance measurement of the rangefinder can be obtained.

[0039] The stability, linearity, and temperature during distance measurement of the rangefinder are standardized.

[0040] The weighting factors of each parameter on the ranging error index are obtained by objective weighting method;

[0041] The ranging error index is calculated based on the standardized parameters and their weighting factors.

[0042] Secondly, embodiments of the present invention provide a power transmission line visualization monitoring and ranging system based on three-dimensional laser point clouds, which includes...

[0043] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of the transmission line visualization monitoring and ranging method based on three-dimensional laser point clouds as described in the first aspect of the present invention.

[0044] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of the transmission line visualization monitoring and ranging method based on three-dimensional laser point clouds as described in the first aspect of the present invention.

[0045] The beneficial effects of this invention are as follows: by introducing a point cloud data measurement accuracy index, the point cloud data is accurately evaluated and screened, thereby ensuring that only data that meets the high accuracy requirements can be used for subsequent analysis. This achieves high-precision acquisition of power line monitoring data, avoids the error propagation problem caused by low-precision data, and effectively solves the problem of measurement error accumulation caused by insufficient data accuracy in the prior art.

[0046] By combining statistical filtering algorithms, cloth simulation filtering algorithms, PCA principal component analysis, and fast Euclidean clustering algorithms, power line point cloud data is accurately extracted, thereby reducing the interference of noise point clouds and improving the extraction accuracy of power line point clouds. This achieves high precision and stability in power line modeling and ranging, effectively solving the problems of mis-extraction and noise interference in the power line point cloud extraction process in existing technologies.

[0047] By analyzing the ranging error index in real time and combining it with real-time monitoring of parameters such as the stability, linearity, and temperature of the rangefinder, the error control strategy is dynamically adjusted, thereby realizing real-time error correction and adjustment during the ranging process. This ensures the accuracy and stability of the monitoring data and effectively solves the problems of untimely correction of ranging errors and errors caused by environmental changes in existing technologies. Attached Figure Description

[0048] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 The flowchart shows a method for visual monitoring and ranging of transmission lines based on 3D laser point clouds.

[0050] Figure 2 This is a diagram of a computer device used for a visualization monitoring and ranging method for power transmission lines based on 3D laser point clouds. Detailed Implementation

[0051] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0052] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0053] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0054] Example 1

[0055] Reference Figures 1-2 This is the first embodiment of the present invention, which provides a method for visual monitoring and ranging of transmission lines based on three-dimensional laser point clouds, including:

[0056] S100: By using a drone to emit lasers at power transmission lines, regional point cloud data is obtained, resulting in power line point cloud data and reference object point cloud data of the power transmission lines;

[0057] S101: The specific steps for classifying regional point cloud data include:

[0058] Read the regional point cloud data collected by the drone and store it in a 3D point cloud data format;

[0059] Noise and outlier removal are performed on the regional point cloud data.

[0060] By filtering out ground point cloud data and low-altitude point cloud data, we obtain power line point cloud data and reference point cloud data for the transmission lines.

[0061] S200: Analyze the point cloud data measurement accuracy index. If the point cloud data measurement accuracy index is not lower than the set threshold, perform subsequent processing; otherwise, reacquire the regional point cloud data.

[0062] S300: Acquire the first power line point cloud group and the second power line point cloud group, and extract the standard power line point cloud from them;

[0063] S301: The steps for acquiring the first power line point cloud group and the second power line point cloud group include:

[0064] Based on the statistical filtering algorithm, the preprocessed power line point cloud data of the transmission line is coarsely extracted multiple times to obtain multiple first coarsely extracted power line point clouds.

[0065] The first power line point cloud group is obtained by refining multiple coarsely extracted power line point clouds using a cloth simulation filtering algorithm.

[0066] Based on the PCA principal component analysis method, multiple coarse extractions are performed on the preprocessed power line point cloud data of the transmission line to obtain multiple second coarse extraction power line point clouds.

[0067] The second power line point cloud group was obtained by refining multiple coarsely extracted power line point clouds using a fast Euclidean clustering algorithm.

[0068] S302: The steps for extracting standard power line point clouds from the first power line point cloud group and the second power line point cloud group include:

[0069] The pairwise similarity of multiple first power line point clouds in the first power line point cloud group is compared to obtain the power line point cloud group N with the highest similarity.

[0070] The pairwise similarity of multiple second power line point clouds in the second power line point cloud group is compared to obtain the power line point cloud group M with the highest similarity.

[0071] The similarity scores of power line point cloud group N and power line point cloud group M are compared, and the group with higher similarity is selected as the standard power line point cloud.

[0072] S400: Fits the standard power line point cloud with the reference object point cloud data to obtain a model of the power line and the reference object, and performs distance measurement based on the model.

[0073] S401: The steps for fitting the standard power line point cloud to the reference object point cloud data include:

[0074] Based on geometric shape analysis methods, point cloud data that conform to the characteristics of the reference object are marked as reference object point clouds;

[0075] A reference point cloud is selected from the obtained standard power line point cloud and reference object point cloud by using geometric features;

[0076] Based on the selected reference point cloud as the alignment target, the standard power line point cloud and the reference object point cloud data are spatially aligned using the ICP algorithm;

[0077] Gaussian smoothing was used to smooth the fitted standard electric field point cloud and the reference point cloud data.

[0078] The smoothed data was modeled using 3D modeling tools to obtain models of the electric field lines and reference objects.

[0079] S402: The steps for distance measurement based on a model of power lines and a reference object include:

[0080] Calculate the shortest distance from the power line to the reference object;

[0081] The distance from a point to a plane is calculated using the formula for the shortest distance from a point to a plane.

[0082] The distance from a point to a surface is calculated using the shortest distance calculation method for the surface.

[0083] Real-time analysis of the ranging error index, which measures the degree of error in the measurement results.

[0084] S403: The steps for real-time analysis of the ranging error index include:

[0085] By connecting sensors through a SCADA system, the stability, linearity, and temperature during distance measurement of the rangefinder can be obtained.

[0086] The stability, linearity, and temperature during distance measurement of the rangefinder are standardized.

[0087] The weighting factors of each parameter on the ranging error index are obtained by objective weighting method;

[0088] The ranging error index is calculated based on the standardized parameters and their weighting factors.

[0089] Furthermore, this embodiment also provides a transmission line visualization monitoring and ranging system based on three-dimensional laser point clouds, including,

[0090] The data acquisition module uses a drone to emit lasers at the power transmission line to acquire regional point cloud data, thus obtaining the power line point cloud data and reference object point cloud data of the power transmission line.

[0091] The data analysis module analyzes the point cloud data measurement accuracy index. If the point cloud data measurement accuracy index is not lower than the set threshold, subsequent processing is performed; otherwise, the area point cloud data is reacquired.

[0092] The point cloud extraction module acquires the first power line point cloud group and the second power line point cloud group, and extracts the standard power line point cloud from them.

[0093] The ranging processing module fits the standard power line point cloud with the reference object point cloud data to obtain a model of the power line and the reference object, and performs ranging based on this model.

[0094] In summary, by introducing a point cloud data measurement accuracy index, point cloud data can be accurately evaluated and screened, ensuring that only data meeting high accuracy requirements can be used for subsequent analysis. This achieves high-precision acquisition of power line monitoring data, avoids the error propagation problem caused by low-precision data, and effectively solves the problem of measurement error accumulation caused by insufficient data accuracy in existing technologies.

[0095] By combining statistical filtering algorithms, cloth simulation filtering algorithms, PCA principal component analysis, and fast Euclidean clustering algorithms, power line point cloud data is accurately extracted, thereby reducing the interference of noise point clouds and improving the extraction accuracy of power line point clouds. This achieves high precision and stability in power line modeling and ranging, effectively solving the problems of mis-extraction and noise interference in the power line point cloud extraction process in existing technologies.

[0096] By analyzing the ranging error index in real time and combining it with real-time monitoring of parameters such as the stability, linearity, and temperature of the rangefinder, the error control strategy is dynamically adjusted, thereby realizing real-time error correction and adjustment during the ranging process. This ensures the accuracy and stability of the monitoring data and effectively solves the problems of untimely correction of ranging errors and errors caused by environmental changes in existing technologies.

[0097] Example 2

[0098] Reference Figure 1 - Figure 2 This is the second embodiment of the present invention, which provides a method for visual monitoring and ranging of transmission lines based on three-dimensional laser point clouds. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0099] A drone emits lasers over power transmission lines to acquire regional point cloud data. This data is then categorized to obtain power line point cloud data and reference object point cloud data. The point cloud data measurement accuracy index is analyzed; this index reflects the precision of the acquired power line point cloud data. If the accuracy index is not lower than a set threshold, the acquired power line and reference object point cloud data are preprocessed. If the accuracy index is lower than the threshold, the regional point cloud data is re-acquired. A first and second power line point cloud group are acquired, each containing multiple first power line point clouds and multiple second power line point clouds. A standard power line point cloud is extracted from both groups. The standard power line point cloud is then fitted to the reference object point cloud data to obtain a model of the power lines and the reference object. Distance measurement is performed based on this model, and the distance measurement error index is analyzed in real-time to measure the degree of error in the measurement results.

[0100] In this embodiment, point cloud data refers to three-dimensional spatial data obtained through laser scanning or other 3D measurement methods. It contains a large number of points, each recording the coordinate information of an object in space, used to represent the three-dimensional structure of the object or scene. Power line point cloud data refers to point data specific to power lines extracted from point cloud data, used to represent the spatial distribution of transmission lines. Reference point cloud data is point cloud data of known objects used for positioning and marking during measurement, serving as a comparison or reference object for the power line model.

[0101] Furthermore, the specific process of classifying the acquired regional point cloud data to obtain the power line point cloud data and reference object point cloud data of the transmission line is as follows: read the regional point cloud data collected by the UAV, store it in a three-dimensional point cloud data format, filter out noise and outliers from the regional point cloud data, filter out ground point cloud data and low-altitude point cloud data, and obtain the power line point cloud data and reference object point cloud data of the transmission line.

[0102] In this embodiment, the 3D point cloud data format refers to a data format that stores the coordinates (usually x, y, z) and other attributes (such as color, reflection intensity, etc.) of each point in the storage space, facilitating the description and processing of the 3D shape of objects. The regional point cloud data collected by the UAV is first read and stored in the 3D point cloud data format, ensuring data integrity and facilitating subsequent processing. Noise and outliers refer to abnormal data in the point cloud data that needs to be filtered out due to measurement errors or other interference. Noise and outliers typically do not represent true object features. Filtering out these interfering data can purify the point cloud, maintain data representativeness, reduce errors, and thus improve data quality. Ground point cloud data refers to point data near the ground in the 3D point cloud, which usually does not contain power line information. Therefore, ground point cloud data is removed to reduce data volume and improve analysis accuracy. Point cloud connectivity analysis refers to an analysis method based on the spatial connection relationships between points, used to identify and distinguish different objects or structures in the point cloud. Geometric shape analysis methods refer to methods used to identify the shape features of objects in order to distinguish different types of objects in the point cloud, such as power lines and reference objects.

[0103] Further, the steps for obtaining the point cloud data measurement accuracy index are as follows: obtain the target monitoring area, the area covered by the UAV flight path, the sensor resolution, and the laser emission frequency, and perform standardization processing; obtain the standardized target monitoring area, the UAV flight path coverage area, the sensor resolution, and the laser emission frequency as weighting factors for the point cloud data measurement accuracy index through the objective weighting method; and calculate the point cloud data measurement accuracy index using the point cloud data measurement accuracy index formula.

[0104] In this embodiment, the area of ​​the target monitoring area is measured using Geographic Information System (GIS) software (such as ArcGIS, QGIS) or a high-precision topographic map. After the UAV flies, flight log data and aerial images are used to import the area covered by its path into the GIS software for area calculation. Simultaneously, the UAV's flight trajectory is recorded in real time using UAV control software (such as DJI GS Pro, Pix4D, etc.) and GIS software, and the area covered by the UAV's flight path is automatically generated based on the flight data. Sensor resolution is typically determined by equipment specifications, but can be calibrated through actual measurement. Under field conditions, a calibration board can be selected as the test target. The sensor is aligned with the calibration board to take pictures or scan, and the number of acquisition points per unit area is measured to confirm the actual resolution. Alternatively, it can be obtained through sensor specification sheets, calibration boards, and rangefinders. The calibration board has precise scales used to test whether the sensor resolution meets the design specifications. The laser emission frequency is generally specified in the equipment parameters, but can be verified using an oscilloscope or pulse counter. The laser beam emitted by the sensor is connected to a photodetector, and the output signal is transmitted to an oscilloscope or pulse counter to display or calculate the number of pulses per second in real time, thereby obtaining the actual transmission frequency.

[0105] Furthermore, the formula for the point cloud data measurement accuracy index is:

[0106]

[0107] In the formula, DYJD is the point cloud data measurement accuracy index, FG is the area covered by the UAV flight path, MB is the target monitoring area, and α1 is... The weighting factors for DYJD are: FB is the sensor resolution, α2 is the weighting factor of FB on DYJD, FS is the laser emission frequency, and α3 is the weighting factor of FS on DYJD.

[0108] In this embodiment, when FG=40, MB=100, α1=0.4, FB=10, α2=0.3, FS=20, and α3=0.3, we obtain DYJD=2.755.

[0109] By calculating the measurement accuracy index, the overall quality of point cloud data can be accurately assessed, ensuring that the data meets the accuracy requirements of the project. This information is used to provide feedback on the actual effectiveness of acquisition parameters (such as UAV flight coverage, sensor resolution, laser emission frequency, etc.), thereby helping to optimize data acquisition configurations, reduce unnecessary duplicate acquisitions, improve efficiency, and save time, manpower, and equipment resources. This is particularly effective in monitoring large areas or complex terrain.

[0110] Furthermore, the steps for obtaining the first power line point cloud group and the second power line point cloud group are as follows: Based on a statistical filtering algorithm, multiple coarse extractions are performed on the power line point cloud data of the preprocessed transmission line to obtain multiple first coarsely extracted power line point clouds; a cloth simulation filtering algorithm is then used to refine the multiple first coarsely extracted power line point clouds to obtain the first power line point cloud group. The first power line point cloud group includes power line point cloud 1, power line point cloud 2, ..., power line point cloud n, where n is the number of points extracted using the statistical filtering algorithm and the cloth simulation filtering algorithm. The first power line point cloud is obtained by taking the total number of points in the first power line point cloud. Based on the PCA principal component analysis method, the power line point clouds are coarsely extracted multiple times from the preprocessed power line point cloud data to obtain multiple second coarsely extracted power line point clouds. The multiple second coarsely extracted power line point clouds are then finely extracted using the fast Euclidean clustering algorithm to obtain a second power line point cloud group. The second power line point cloud group includes power line point cloud 1, power line point cloud 2, ..., power line point cloud m, where m is the total number of second power line point clouds extracted by the PCA principal component analysis method and the fast Euclidean clustering algorithm.

[0111] In this embodiment, the statistical filtering algorithm is a filtering method that removes noise and outliers by statistically analyzing the distribution characteristics of data, used to initially screen point cloud data that meets specific statistical characteristics. The cloth simulation filtering algorithm is a filtering algorithm based on physical simulation, treating point cloud data as cloth suspended under gravity, used to extract features resembling linear structures, such as power lines. Principal Component Analysis (PCA) is a dimensionality reduction method that analyzes the main directionality of point cloud data and maps the data onto principal components to highlight the main structural features, facilitating clustering and classification. The fast Euclidean clustering algorithm clusters point cloud data based on Euclidean distance, grouping data points with similar shapes together to help extract the target structure. From initial extraction to fine classification, two sets of power line point cloud data were obtained. The combined use of different extraction algorithms reduces data noise and errors while ensuring that the extracted point cloud data has a clear structure and high accuracy. This step-by-step processing method improves the quality and reliability of point cloud data, providing a high-quality data foundation for subsequent applications such as power line structure modeling and distance measurement.

[0112] Furthermore, extracting standard power line point clouds from the first power line point cloud group and the second power line point cloud group includes the following steps: Performing pairwise similarity comparisons among the first power line point clouds 1, 2, ..., n to obtain the power line point cloud group N with the highest similarity; performing pairwise similarity comparisons among the second power line point clouds 1, 2, ..., m to obtain the power line point cloud group M with the highest similarity; and assigning the corresponding power line point cloud group N to the power line point cloud group M. The similarity is compared with the similarity of the power line point cloud group M: if the similarity of the power line point cloud group N is greater than the similarity of the power line point cloud group M, then the power line point cloud group N is used as the standard power line point cloud; if the similarity of the power line point cloud group N is less than the similarity of the power line point cloud group M, then the power line point cloud group M is used as the standard power line point cloud; if the similarity of the power line point cloud group N is equal to the similarity of the power line point cloud group M, then either the power line point cloud group N or the power line point cloud group M is used as the standard power line point cloud.

[0113] In this embodiment, this comparison method can determine the structural and morphological consistency between point clouds, thereby selecting the point cloud groups N and M with the highest similarity. This process effectively extracts point clouds that are more consistent in spatial distribution and structure, providing the most accurate and highest-quality benchmark data for the final selection of standard power line point clouds. Selecting the standard power line point clouds with the highest similarity ensures the accuracy and consistency of the selected data, improving the reliability of subsequent analysis. As benchmark point clouds, standard power line point clouds provide high-quality reference data for distance measurement, structural analysis, etc. Furthermore, the comparison and selection of the two sets of point clouds greatly improves the stability and representativeness of the data, helping to reduce errors and optimize the accuracy of the overall analysis results.

[0114] Furthermore, fitting the standard power line point cloud with the reference point cloud data to obtain the power line and reference object model includes the following steps: Based on the obtained power line point cloud data and reference object point cloud data of the transmission line, using geometric shape analysis methods, point cloud data that conforms to the characteristics of the reference object are marked as reference object point clouds, thus obtaining point clouds that conform to the characteristics of the reference object; a reference point cloud is selected from the obtained standard power line point cloud and reference point cloud using geometric features; based on the selected reference point cloud as the alignment target, the standard power line point cloud and reference point cloud data are spatially aligned using the ICP algorithm, thus obtaining the fitted standard power line point cloud and reference point cloud data; the fitted standard power line point cloud and reference point cloud data are smoothed using Gaussian smoothing, and the smoothed standard power line point cloud and reference point cloud data are modeled using a 3D modeling tool to obtain the power line and reference object model.

[0115] In this embodiment, the ICP algorithm (Iterative Closest Point Algorithm) is used for point cloud data alignment. By iteratively adjusting the positions, two sets of point clouds are gradually matched together to achieve high-precision spatial alignment. Gaussian smoothing is a data smoothing method that reduces noise and irregularities in the point cloud using a Gaussian function, making the data surface smoother. A high-precision 3D model is generated, accurately reflecting the relative spatial structure of the power line and the reference object. The entire process improves the accuracy of point cloud alignment and the smoothness of the model, ensuring geometric accuracy and providing reliable support for subsequent monitoring, maintenance, and risk assessment of power lines.

[0116] Furthermore, distance measurement based on the model of electric field lines and reference objects includes the following steps: calculating the shortest distance from the electric field lines to the reference object based on the model of electric field lines and reference objects; using the formula for the shortest distance from a point to a plane for the distance from a point to a plane for the distance from a point to a surface for the distance from a point to a surface for the shortest distance calculation method for the surface.

[0117] In this embodiment, the shortest distance refers to the perpendicular distance between a point and a plane or curved surface, which is the distance between that point and the nearest point on the target surface. The formula for the shortest distance from a point to a plane is:

[0118]

[0119] In the formula, D is the shortest distance from the point to the plane, A is the component of the plane's normal vector in the x-axis direction, B is the component of the plane's normal vector in the y-axis direction, C is the component of the plane's normal vector in the z-axis direction, and A... x +B y +C z +D=0 represents the equation of the plane, where (x1, y1, z1) are the coordinates of the point. The shortest distance from a point to a surface can be calculated using differential geometry to determine the perpendicular projection between the point and the surface. Whether calculating the simple distance from a point to a plane or the complex distance from a point to a surface, it can be achieved through precise mathematical models and algorithms. This not only enhances the understanding of the relationship between power lines and their surrounding environment but also provides high-precision measurement results for power line maintenance and safety monitoring, helping to identify potential hazards, such as excessively close distances between power lines and reference objects, thus facilitating early risk assessment and decision-making.

[0120] Furthermore, the real-time analysis of the ranging error index includes the following steps: the SCADA system connects to the sensor, acquires the stability, linearity, and temperature during ranging based on the sensor, and performs standardization processing; the weighting factors of the standardized stability, linearity, and temperature during ranging for the ranging error index are obtained through an objective weighting method, and the ranging error index is calculated using the ranging error index formula.

[0121] In this embodiment, the SCADA (Supervisory and Data Acquisition) system is connected to stability sensors, linearity sensors, and temperature sensors to collect real-time stability, linearity, and temperature data of the rangefinder. The temperature during distance measurement can also be measured using a temperature sensor (e.g., thermocouple, RTD temperature sensor, or digital thermometer). By dynamically evaluating the accuracy and reliability of the measurement, the system ensures that the distance measurement data always meets the expected accuracy standards, avoiding the accumulation of measurement errors caused by equipment or environmental factors, thus improving the accuracy and reliability of the measurement results. Simultaneously, through real-time monitoring and analysis by the SCADA system, potential measurement deviations can be quickly addressed, ensuring efficient equipment operation and reducing risks and errors during the measurement process.

[0122] Furthermore, the formula for the ranging error index is:

[0123]

[0124] In the formula, CJWC is the ranging error index, WD is the stability of the rangefinder, β1 is the weighting factor of WD on CJWC, XD is the linearity of the rangefinder, β2 is the weighting factor of XD on CJWC, CW is the temperature during ranging, and β3 is the weighting factor of CW on CJWC.

[0125] In this embodiment, when WD=5, β1=0.5, XD=6, β2=0.4, CW=28, and β3=0.1, we obtain CJWC=1.99. The larger the ranging error index, the lower the ranging accuracy.

[0126] Example 3

[0127] This embodiment also provides a computer device applicable to a transmission line visualization monitoring and ranging method based on three-dimensional laser point clouds, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize a forced oscillation detection and location method for power distribution networks as proposed in the above embodiment.

[0128] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a forced oscillation detection and location method for power distribution networks as proposed in the above embodiments.

[0129] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0130] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0131] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0132] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0133] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0134] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for visual monitoring and ranging of transmission lines based on three-dimensional laser point clouds, characterized in that: This includes using drones to emit lasers at power transmission lines to acquire regional point cloud data, and obtaining power line point cloud data and reference object point cloud data of the power transmission lines; The point cloud data measurement accuracy index is analyzed. If the point cloud data measurement accuracy index is not lower than a set threshold, subsequent processing is performed; otherwise, the point cloud data of the region is reacquired. Acquire the first power line point cloud group and the second power line point cloud group, and extract the standard power line point cloud from them; The steps for acquiring the first power line point cloud group and the second power line point cloud group include: Based on the statistical filtering algorithm, the preprocessed power line point cloud data of the transmission line is coarsely extracted multiple times to obtain multiple first coarsely extracted power line point clouds. The first power line point cloud group is obtained by refining the multiple first coarse-extracted power line point clouds using a cloth simulation filtering algorithm. Based on the PCA principal component analysis method, multiple coarse extractions are performed on the preprocessed power line point cloud data of the transmission line to obtain multiple second coarse extraction power line point clouds. The multiple coarsely extracted electric field point clouds are refined using a fast Euclidean clustering algorithm to obtain the second electric field point cloud group. The standard power line point cloud is fitted with the reference object point cloud data to obtain a model of the power line and the reference object, and distance measurement is performed based on the model.

2. The method for visual monitoring and ranging of transmission lines based on three-dimensional laser point clouds as described in claim 1, characterized in that: The specific steps for obtaining power line point cloud data and reference point cloud data of the transmission line by emitting lasers at the transmission line using a drone to acquire regional point cloud data include: Read the regional point cloud data collected by the drone and store it in a 3D point cloud data format; The point cloud data of the region is processed to remove noise and outliers; By filtering out ground point cloud data and low-altitude point cloud data, we obtain power line point cloud data and reference point cloud data for the transmission lines.

3. The method for visual monitoring and ranging of transmission lines based on three-dimensional laser point clouds as described in claim 2, characterized in that: The steps for extracting standard power line point clouds from the first power line point cloud group and the second power line point cloud group include: The pairwise similarity of multiple first power line point clouds in the first power line point cloud group is compared to obtain the power line point cloud group N with the highest similarity. The pairwise similarity of multiple second power line point clouds in the second power line point cloud group is compared to obtain the power line point cloud group M with the highest similarity. The similarity of the power line point cloud group N and the power line point cloud group M are compared, and the group with higher similarity is selected as the standard power line point cloud.

4. The method for visual monitoring and ranging of transmission lines based on three-dimensional laser point clouds as described in claim 3, characterized in that: The step of fitting the standard power line point cloud with the reference object point cloud data includes: Based on geometric shape analysis methods, point cloud data that conform to the characteristics of the reference object are marked as reference object point clouds; A reference point cloud is selected from the obtained standard power line point cloud and reference object point cloud by using geometric features; Based on the selected reference point cloud as the alignment target, the standard power line point cloud and the reference object point cloud data are spatially aligned using the ICP algorithm; Gaussian smoothing was used to smooth the fitted standard electric field point cloud and the reference point cloud data. The smoothed data was modeled using 3D modeling tools to obtain models of the electric field lines and reference objects.

5. The method for visual monitoring and ranging of transmission lines based on three-dimensional laser point clouds as described in claim 4, characterized in that: The steps of obtaining a model of the power lines and a reference object, and performing distance measurement based on the model, include: Calculate the shortest distance from the power line to the reference object; The distance from a point to a plane is calculated using the formula for the shortest distance from a point to a plane. The distance from a point to a surface is calculated using the shortest distance calculation method for the surface. Real-time analysis of the ranging error index, which is used to measure the degree of error in the measurement results.

6. The method for visual monitoring and ranging of transmission lines based on three-dimensional laser point clouds as described in claim 5, characterized in that: The steps for real-time analysis of the ranging error index include: By connecting sensors through a SCADA system, the stability, linearity, and temperature during distance measurement of the rangefinder can be obtained. The stability, linearity, and temperature during distance measurement of the rangefinder are standardized. The weighting factors of each parameter on the ranging error index are obtained by objective weighting method; The ranging error index is calculated based on the standardized parameters and their weighting factors.

7. A transmission line visualization monitoring and ranging system based on three-dimensional laser point clouds, based on the transmission line visualization monitoring and ranging method based on three-dimensional laser point clouds as described in any one of claims 1 to 6, characterized in that: It also includes a data acquisition module, which uses a drone to emit lasers at the power transmission line to acquire regional point cloud data, and obtains the power line point cloud data and reference object point cloud data of the power transmission line; The data analysis module analyzes the point cloud data measurement accuracy index. When the point cloud data measurement accuracy index is not lower than a set threshold, subsequent processing is performed; otherwise, the point cloud data of the region is reacquired. The point cloud extraction module acquires the first power line point cloud group and the second power line point cloud group, and extracts the standard power line point cloud from them. The ranging processing module fits the standard power line point cloud with the reference object point cloud data to obtain a model of the power line and the reference object, and performs ranging based on the model.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the transmission line visualization monitoring and ranging method based on three-dimensional laser point cloud as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the transmission line visualization monitoring and ranging method based on three-dimensional laser point cloud as described in any one of claims 1 to 6.

Images